In this study, a commercial sodium butyrate protected by a new buffer salt solution (NSB) was tested to determine whether it can be used as an antibiotic alternative in broiler production. 192 1-day-old broilers were randomly allocated to three dietary treatments: soybean meal diet (CON), antibiotic diet (ANT, basal diet + 100 mg/kg aureomycin), and NSB (basal diet + 800 mg/kg NSB). The growth performance, serum anti-inflammatory cytokines, intestinal morphology, gut barrier function, antioxidative parameters, SCFAs content, and cecal microbiota were analyzed. The result showed that NSB significantly improved ADFI, ADG (p < 0.01), and decreased FCR (p < 0.01). Serum anti-inflammatory cytokine IL-10 was up-regulated (p < 0.01), and pro-inflammatory TNF-α was down-regulated (p < 0.05) by NSB supplementation. H&E results showed that VH and VH: CD ratio significantly increased (p < 0.05) in the jejunum and ileum in the NSB group. Furthermore, ZO-1 (p < 0.01), claudin-1 (p < 0.01), and occludin (p < 0.05) in the jejunum and claudin-1 (p < 0.01) and mucin-2 (p < 0.05) in the ileum significantly up-regulated in the NSB group, respectively. Additionally, SOD (p < 0.05) and T-AOC/MDA ratio (p < 0.01) in the jejunum and SOD in the ileum were significantly increased (p < 0.05) in the NSB group. The MDA level also significantly increased (p < 0.01) in the ANT group in the jejunum. Propionic acid (p < 0.05) and butyric acid (p < 0.01) content significantly increased in the NSB group in the jejunum and ileum segments. The 16S rRNA sequencing results showed no significant difference (p > 0.05) in alpha and beta diversity among the groups. LEFSe analysis also indicated that Peptostreptococcaceae, Colidextribacter, Firmicutes, Oscillospira, and Erysipelatoclostridiaceae, which promote SCFA production (p < 0.05), were identified as dominant taxon-enriched bacterial genera in the NSB group. The Spearman correlation analysis revealed that Colidextribacter with ADFI, ADG, VH, claudin-1 (p < 0.05), and unclassified_f__Peptostreptococcaceae with ADFI, IL-10, and ZO-1 were positively correlated (p < 0.05). Furthermore, ADFI and ADG with IL-10, claudin-1, SOD, T-AOC, and butyric acid (p < 0.05), and similarly, ADG with VH (p < 0.05) showed a positive correlation. In conclusion, NSB enhanced the growth performance by improving jejunum and ileum morphology, serum anti-inflammatory cytokines, regulating intestinal barrier function and antioxidant capacity, SCFAs content, and cecum microbiota, showing its potential use as an alternative to antibiotics in poultry nutrition.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Semisolid casting can provide excellent castings, but the nature of the pore-forming mechanisms has not been properly clarified. In the current communication, it was suggested that hydrogen precipitated during slurry making might have a decisive role in the formation of both gas and shrinkage porosity. Intensive stirring at the end of the slurry making process may act as a degassing step. Without the intense stirring, structures of primary slurry particles form around the hydrogen pores, strongly affecting pore formation and feeding during the intensification stage.

This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

Aiming at the problem of UAV cluster configuration reconstruction, in order to obtain the cluster reconstruction method, the theory and simulation of cluster reconstruction control strategy is studied by simplifying the model，deducing the theoretical formula and analyzing the example. Taking 10 UAVs cluster as an example, the distribution of UAVs in different configurations was analyzed. According to the change of cluster configuration, the principle of formation reconstruction and the execution ability of configuration, the cluster capability was analyzed. The group behavior is modeled by group behavior rules, reconfiguration topology analysis and reconfiguration cost measurement. Based on the cluster parameters and configuration characteristics, simulation experiments were carried out to obtain the change curve of the reconstruction cost and time and the trajectory movement diagram of the UAV in different configurations.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

Ideas about Artificial Intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided is getting bigger and bigger and, in this case, we need artificial intelligence that helps us with superhuman powers. The interaction with medicine is getting closer and closer, medicine being a field that continues to be perfected. Machine learning, deep learning, neural networks, computer vision are some of the mechanisms that are already applied in medicine, becoming a trend in healthcare worldwide. Artificial intelligence will be the thing that will change contemporary medicine. Developed countries such as Japan, France, Germany have already implemented artificial intelligence in their medical systems. The help is in medical diagnosis, patient monitoring, personalized therapy, and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques, to choose the best surgical technique. The goal is to predict complications, reduce diagnostic time, diagnose complex pathologies, guide the surgeon intraoperatively and reduce medical errors. We are at the beginning but the potential is enormous.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Continuum robots play the role of end-effector in various surgical robots and endoscopic devices. The robots may be categorized as rigid or soft continuum robots (SCRs). While the SCRs have proven advantages such as safety and compliance, more research and development are required to enhance their capability for specific medical scenarios. This research aims at designing a soft robot considering concepts of geometric and kinematic similarities. The chosen application is a semi-invasive medical application known as Transesophageal Echocardiography (TEE), but the methodology can be implemented in other similar applications. Since established rigid carrier construction make use of principles we find as well embodied in vertebral spines, the paragon for soft robot solution can be the phylogenetic precursor of the spine, the Chorda dorsalis. The feasibility of fabrication of a soft endoscopic device derived from that paragon within a robotics laboratory was shown empirically by producing a three-segment pneumatic SCR. The kinematic model was proposed based on the method of transformation matrices, and an algorithm based on a self-organizing map (SOM) was proposed and applied to realize kinematic similarity. The simulation results indicate that the algorithm acting as an open-loop controller forces the soft robot tip to follow the desired path which in this case is the path of the rigid probe. It is concluded that the solution provides a soft robot that can surrogate and succeed the traditional rigid counterpart owing to size, workspace, and kinematics.

In contemporary construction, the prevalence of vibration serviceability issues in lightweight and slender structures has become increasingly common, owing to advancements in building materials and construction methods. While these structures often meet the criteria for ultimate limit states, they can still elicit complaints due to excessive vibrations induced by human activity. To address this challenge, the Integral Resonant Control (IRC) technique has emerged as a favored approach for actively damping vibrations in various systems. This study introduces a fresh perspective on implementing a multi-input multi-output (MIMO) IRC scheme for active vibration control (AVC) specifically tailored for pedestrian structures utilizing inertial mass actuators. Building upon a common framework and design methodology outlined in previous research, this work presents a novel application of MIMO IRC for AVC. The designed controller is rigorously tested and implemented on a laboratory floor structure to validate its efficacy.

The efficacy of time-restricted feeding for weight loss has not been established as prior studies were limited by lack of controlled isocaloric designs. This study describes the design and implementation of a controlled feeding study evaluating time-restricted feeding. We designed a randomized, controlled, parallel-arm, feeding study comparing time restricted feeding (TRF) to a usual feeding pattern (UFP) for the primary outcome of weight change. Participants were aged 18-69 years with prediabetes and obesity. TRF consumed 80% of calories by 1300, and UFP consumed ≥50% of calories after 1700. Both arms consumed identical macro- and micro-nutrients, based on a healthy palatable diet. We calculated individual calorie requirements which were maintained throughout the intervention. We randomized 41 participants who all completed the study. The desired distribution of calories across feeding windows in both arms was achieved, as were weekly averages for macronutrients and micronutrients. All randomized participants completed the study. We actively monitored participants and adapted diets to facilitate adherence. We provide the first report, to our knowledge, on the design and implementation of a feeding study that isolated the effect of meal timing on weight, while maintaining constant caloric intake and identical diets during the study period.

Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

Pituitary adenylyl cyclase activating polypeptide (PACAP) belongs to the vasoactive intestinal polypeptide (VIP)/secretin/glucagon superfamily. PACAP is present in two forms, PACAP-38 and PACAP-27, and binds to three guanine-regulatory (G) protein-coupled receptors (PAC1, VPAC1, and VPAC2). PACAP is expressed in the central and peripheral nervous systems with high PACAP levels found in the hypothalamus, a brain region involved in feeding and energy homeostasis. PAC1 receptors are high-affinity and PACAP-selective receptors, while VPAC1 and VPAC2 receptors show a comparable affinity to PACAP and VIP. PACAP and its receptors are expressed in the central and peripheral nervous systems, with moderate to high expression in the hypothalamus, amygdala, and other limbic structures. Consistent with their expression, PACAP is involved in several physiological responses and pathological states. A growing body of literature suggests that PACAP regulates food intake in laboratory animals. However, there is no comprehensive review of the literature on this topic. Thus, the purpose of this article is to review the literature regarding the role of PACAP and its receptors in food intake regulation and to synthesize how PACAP exerts its anorexic effects in different brain regions. To achieve this goal, we searched PubMed and reviewed 68 articles regarding the regulatory action of PACAP on food intake. Here, we present the literature regarding the effect of exogenous PACAP on feeding and the role of endogenous PACAP in this process. We also provide evidence regarding the effect of PACAP on the homeostatic and hedonic aspects of food intake, the neuroanatomical sites where PACAP exerts its regulatory action, which PACAP receptors may be involved, and the role of various signaling pathways and neurotransmitters in hypophagic effects of PACAP.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

Loneliness is becoming prevalent across Japan in spite of its a dense population and communal culture. Surveys show 40% of adults report isolation across demographics. The contradiction emerges due to deteriorating social structure with modernization, economic parity, urbanization, and fading conventional setup. The continually social withdrawals, called “hikikomori." Relief seekers have embraced AI based emotive robots, digital avatars, and immersive VR as low-stakes social surrogates despite limited capacity to cultivate human intimacy. To stabilise government attempts awareness through reforms, scheme and support services. But a concrete transformation can be brough in with work-life balance, gender equality and rewire the metrics for self-esteem. Even a perfectly personalized AI cannot replace the human affection lacking in the society. Lasting solutions demand technological aids be balanced by confronting and reinventing community purpose and bring in structural change. Overall Japan’s endemic isolation reveals the societal fraying when modernization privileges independence over interdependence. The path forward rests on restoring cultural foundations to reverse loneliness and social isolation.

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share several commonalities: historic, conceptual, methodological, and philosophical. It was possible to develop them only because of information technology, while their origins can be traced back to cybernetics. In this perspective, I'll revise the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. The paper poses more questions than answers, but hopefully it will be useful to align efforts in these fields towards overcoming --- or accepting --- their limits.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcel-lular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

Abstract: Cancer cells are originated by normal cells and with those share the need to be fed. Metabolic reprogramming in cancer cells is not yet fully understood although there is a vast literature suggesting that huge amounts of both energy and substrates are require for synthesis of components of new neoplastic cells so that any macronutrient may be efficiently utilized. Recently, in vitro data show that EAA supplementation promoted colon cancer cells apoptosis by enhanced autophagy. Therefore, in this study we tested in vivo the effects of matched macronutrients standard laboratory diet (control) versus EAA rich modified diet (EAArmd) in mice injected with colon cancer cells. Results of the first set of 12 animals, 6 controls and 6 EAArmd-fed, showed that controls mice develop cancer volume cancers 5 times larger and heavier than EAArmd-fed. If confirmed, these results would open new scenarios for the cancer integrate therapies.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

We report, for the first time in onychophorans, food hiding, parental feeding investment and an ontogenetic diet shift, from adhesive to prey, after their first two weeks of life.

Outbreaks of the crown-of-thorns seastar (CoTS) represent a major cause of coral loss on the Great Barrier Reef. Outbreaks might be explained by enhanced larval survival supported by higher phytoplankton availability after flood events, yet little is known about CoTS larvae feeding behaviour, in particular their potential for selective feeding. Here, single- and mixed-species feeding experiments were conducted on CoTS bipinnaria larvae using five algae (Phaeodactylum tricornutum, Pavlova lutheri, Tisochrysis lutea , Dunaliella sp. and Chaetoceros sp.) and two algal concentrations (1000 and 2500 algae mL^-1). Cell counts using flow-cytometry at the beginning and end of each incubation experiment allowed us to calculate the filtration and ingestion rates of each species by CoTS larvae. In line with previous studies, CoTS larvae ingested more algae when initial algal concentration was higher. We found evidence for the selective ingestion of some species (Chaetoceros sp., Dunaliella sp.) over others (P. lutheri, P. tricornutum). The preferred algal species had the highest energy content, suggesting that CoTS selectively ingested the most energetic algae. Ultimately, combining these results with spatio-temporal patterns in phytoplankton communities will help elucidate the role of larval feeding behaviour in determining the frequency and magnitude of CoTS outbreaks.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context.Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives.Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics.In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa.Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies.Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

Generative artificial intelligence tools are flourishing across various sciences, particularly in the educational domain. ChatGPT, with its versions 3.5 and 4.0, stands out for its multilingual capabilities, catering to both students and educators. This research aimed to evaluate the performance differences between these versions, and importantly, to compare the accuracy of ChatGPT's responses in English versus Arabic. In our study, we presented ChatGPT with 39 chemistry problems related to 6th-7th grade curriculum, including twenty-five open questions and fourteen multiple-choice questions. Each response was categorized as accurate, partially accurate, or inaccurate. Our analysis, focused on trends across versions and languages, revealed significant enhancements in version 4.0, particularly in its ability to process Arabic. However, despite these improvements, our findings indicate that responses in English consistently outperformed those in Arabic in terms of accuracy. In light of this discrepancy, we suggest that ChatGPT either incorporates additional Arabic databases into its training regimen or develops a code that adopts the following strategy to ensure more accurate responses in science education: questions should first be translated from Arabic to English, addressed, and then retranslated into Arabic before being presented to the user. This approach takes advantage of the greater accuracy of responses in English to benefit Arabic-speaking users, and it may also improve outcomes for speakers of other languages who experience less precise answers in the fields of chemistry or science education.

Salidroside, a valuable phenylethanoid glycoside, is obtained from plants belonging to the Rhodiola genus, known for its diverse biological properties. At present, salidroside is still far from large-scale industrial production due to the lower titer and higher process cost. In this study, we constructed a biosynthetic pathway from L-tyrosine to salidroside in Saccharomyces cerevisiae. To increase sali-droside production, we for the first time constructed an in vivo UDP-glucose regeneration system that sucrose synthase GmSUS worked in conjunction with UDP-glucose transferase RrUGT33 to improve the UDP-glucose availability with inexpensive sucrose as the consumed substrate. Finally, the molar conversion rate of L-tyrosine could reach 95%. To further reduce the cost, co-culture with E. coli that synthesized L-tyrosine de novo resulted in an 81% reduction in the final substrate cost per 1 kg of salidroside compared to the initial cost. The final titer of salidroside obtained 3.8 g/L and was the highest titer achieved at the Shake-Flask-Level in the existing reports. And this marked the first successful synthesis of salidroside in an in situ enhanced UDP-glucose system using sucrose. This accomplishment laid a robust groundwork for further investigations into the synthesis of other notable glycosides and natural compounds.

Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

Polyphenols and fermentable fibers have shown favorable effects on gut microbiota composition and metabolic function. However, few studies have investigated whether combining multiple fermentable fibers or polyphenols may have additive beneficial effects on gut microbial states. Here, an in vitro fer-mentation model, seeded with human stool combined from 30 healthy volunteers, was supplemented with blends of polyphenols (PP), dietary fibers (FB) or their combination (PPFB) to determine influence on gut bacteria growth dynamics and select metabolite changes. PP and FB blends independently lead to significant increases in the absolute abundance of select beneficial taxa, namely Ruminococcus bromii, Bifidobacterium spp., Lactobacillus spp. and Dorea spp. Total short chain fatty acid concentrations, relative to non-supplemented control (F), increased significantly with PPFB and FB supplementation, but not PP. Indole and ammonia concentrations decreased with FB and PPFB supplementation, but not PP alone while increased antioxidant capacity was only evident with both PP and PPFB. These findings demonstrated that, while the independent blends displayed selective positive impacts on gut states, combination of both blends provided an additive effect. The work outlines the potential of mixed substrate blends to elicit a broader positive influence on gut microbial composition and function to build resiliency toward dysbiosis.

Breastmilk is the only recommended source of nutrition for infants below six-months of age. However, a significant proportion of children are either on supplemental breastfeeding(SBF) or weaned due to the early introduction of solid/semi-solid/soft food and liquids(SSF) before 6 months of age. There is good evidence that Exclusive Breastfeeding(EBF) in infants below six-months of age protects them from preventable illnesses, including malnutrition. The relationship between infant feeding practices and coexisting forms of malnutrition(CFM) has not yet been explored. This study examined the association of different feeding indicators(continuation of breastfeeding, predominant feeding, and SSF) and feeding practices(EBF, SBF, and complete weaning) with CFM in infants aged below six-months of age in Pakistan. National and regional datasets of Pakistan from the last ten years were retrieved from the Demographic Health Surveys(DHS) and UNICEF data repositories. In Pakistan, 34.5%(n=6131) of infants have some form of malnutrition. Among malnourished infants, 44.7%(~15.4% of the total sample) had a CFM. Continuation of breastfeeding was observed in more than 85% of infants, but less than a quarter were on EBF, and the rest were either SBF(65.4%) or weaned infants(13.7%). Compared to EBF, complete weaning increased the odds of coexistence of underweight with wasting and underweight with both wasting and stunting by 1.96(1.12-3.47) and 2.25(1.16-4.36), respectively. Overall, breastfed children had lower odds of various forms of CFM (compared to non-breastfed), except for the coexistence of stunting with overweight/obesity. Continuation of any breastfeeding protects infants in Pakistan from various types of CFM during the first six months of life.

Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

The supply of feed has a significant effect on fish growth and operation costs, making it a critical factor in aquaculture. Owing to the repetitive nature of feed supply, feeding techniques have undergone a shift from manual feeding to systems allowing operators to set feed quantities and timing, reducing labor efforts. However, unlike manual feeding, automatic systems cannot ad-just the amount of feed supplied according to the feeding activities of fish, potentially resulting in overfeeding or underfeeding. Such overfeeding causes marine pollution and increases operational costs, whereas underfeeding hinders fish growth. In this paper, we present an intelligent feeding system that observes the depth-specific feeding behavior of red seabream during the feeding process and determines whether feed supply must be continued. The performance of the feeding algorithm is evaluated by comparing the feed loss rate measured during a feeding experiment at a red seabream sea cage farm with that of the traditional manual feeding method. The results reveal that the feed supply per unit time of the manual method and the developed intelligent feed supply system is at an equivalent level. Moreover, the difference in the average feed loss rate is a negligible 1.16%, confirming that the new system is slightly more advantageous.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

A number of studies have investigated different crustacean food stuffs, feeding methods, 18 and feeding behavior, but little attention has been given to the interaction between these aspects in 19 crustaceans. The aim of the present review is to update knowledge, and examine challenges and 20 opportunities in the development of formulated diets, as pelleted feed, which is vital for developing 21 better quality of seed or broodstock in hatcheries, and adaptation of hatchery product to the aqua- 22 culture environment, and production systems.

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

The artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopaedic-specific problems. One particular area of investigation focuses on the shoulder pathology, which is a range of disorders or abnormalities of shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate the current AI applications in the shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI are also discussed.

This study proposes an image recognition method to assist swine farm managers in collecting health data related to pig feeding and excretion. Analyzing the correlation between sow health data and indoor air quality in the pigsties revealed significant influences of air quality indicators on sow health. Increased levels of TVOC, CO2, and temperature were found to negatively affect feeding health, while increased temperature, humidity, and PM10 were found to negatively impact excretion health. These findings provide a basis for evaluating variables when constructing future sow disease prediction models. Analyzing the correlation between pig health status and air quality can help swine farm managers improve sow production environments, reduce disease risks, enhance production efficiency, and provide valuable insights for future research on disease prediction models.

(1) Background: Although diagnostic criteria for massive hemorrhage with organ dysfunction such as disseminated intravascular coagulation associated with delivery have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset of 107 deliveries with ≥2,000 g of blood loss, among 13,368 deliveries from nine national perinatal centers in Japan between 2020 and 2023 was obtained. Twenty-three patients had fibrinogen levels &lt;170 mg/dl, which is the initiation of coagulation system failure per our previous reports. Three of these patients had hematuria. We used six machine learning methods to identify the borderline criteria dividing the fibrinogen/fibrin/fibrinogen degradation product (FDP) planes, using 15 coagulation fibrinolytic factors. (3) Results: The boundaries of hematuria development on a two-dimensional plane of fibrinogen and FDP were obtained. If the FDP–fibrinogen/3–60 (mg/dl) value is positive, this indicates hematuria; otherwise, the case is non-hematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: The borderline criterion dividing the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery was obtained using artificial intelligence, and this method seemed to be useful.

The individual and combined effects of extracellular products (ECP) from Arthrobotrys oligospora (Ao) and A. musiformis (Am) growth in liquid media against Haemonchus contortus L3 (HcL3) were assessed. The following methodological steps were established: isolation, morphological (MI) and molecular identification (Mol-I), assessment of nematocidal activity (NA) of fungal liq-uid culture filtrates (LCF) produced in two liquid media alone and in combination and finally, the myco-compound profile identification (MCP). The MI of both isolates suggested that the fungi corresponded to the species Ao and Am. This result was confirmed by PCR analysis followed by sequencing, alignment and obtaining coverage and similarity with respect to isolates reported in the NCBI database. Likewise, the highest Hc mortalities were 91.4% with individual LCF of Am and 86.2% with those of Ao at the highest concentration (100 mg/mL) in Czapek-Dox Broth. The combination of both LCF resulted in a similarly high larval mortality with no statistical differ-ences in relation to individual activity (P>0.05). The MCP showed the presence of alkaloids in both fungi. Coumarins, triterpenes and saponins were found only in Ao. Both fungi produced ECP with a high NA that could be identified and assessed in future studies as potential natural an-thelmintic compounds.

Infant malnutrition remains as an important cause of death and disability, Haiti has the highest prevalence in America, so preventive strategies are needed. Our objective was to assess infant and young children malnutrition in Haiti and to study an association to World Health Organization (WHO) recommended feeding practices adherence. Cross-sectional study of children younger than 2 years old, recruited at Saint Espri Health Center, Port Au Prince (August to September 2014). We registered feeding practices, social and demographic data, and performed anthropometry (WHO-2006 standard). We evaluated 278 infants and young children, 8.08 ± 6.5 months old, 53.2% female. 18.35% had underweight; 13.31% were stunted and 13.67% had moderate or severe wasting. Malnutrition was associated to male gender, older age, lower education of mothers and higher number of siblings. The adherence to WHO recommended practices for breastfeeding was from 11.8 to 97.9% and was related to a lower prevalence of malnutrition. For complementary food, adherence was 9.7 to 90.3%, also associated to lower malnutrition. Conclusion: Prevalence of infant and young children malnutrition in this Haitian Health Center population was high, related to some risk factors. The adherence to WHO recommended feeding practices was associated to a better nutritional status.

The aim of the study was to assess the adaptability of Polish Red cows to extensive housing and feeding conditions. The study was carried out using 22 cows together with their offspring, kept on a certified organic family farm, from 15 October 2017 – the start of the winter feeding season – to 30 October 2018 – the end of the pasture season. The animals were kept as beef suckler cows, which stayed in the pasture during the summer and were kept in buildings with permanent access to a large cattle run in winter. The following were determined in the study: changes in the cows’ weight and body condition, daily gains in calves, 210-day standardized body weight, and estimated milk yield of suckler cows. To evaluate the cows’ health, blood was collected from them twice and analysed for haematological and biochemical parameters. The results indicated that during the study period the Polish Red cows had adequate body condition, normal vital parameters, and a normal course and development of pregnancy despite the sparse winter diet. This definitively confirms the breed’s high capacity to adapt to an extensive system.

Mothers’ own milk (MOM) for premature babies is considered a life-saving drug for its proven protective action against the complications of prematurity and for the effects on the outcome in the short and long term, especially the neurological one. We studied the use of the MOM for WLBWs over a 5-year period. Statistical comparisons concerned data on feeding with the MOM during stay in NICU and at discharge with respect to maternal and neonatal variables (gestational age, birth weight, type of pregnancy if single or twin, maternal age) and to feeding data (timing of the start of the Minimal Enteral Feeding and the availability of the MOM, the achievement of the Full Enteral Feeding, the type of nutrition at discharge). We observed an increase, between 2017 and 2021, of MOM use (p=0.003). The availability of the MOM occurred on average on the fourth day of life and improved over the years. Start of MEF with human milk averaged 1.78 days and 54.3% of VLBWs received MEF with donor milk on the first day of life (50% within the first 6 hours). The average MOM at discharge was 47.6% with 36.1% exclusive MOM (EMOM) and an increase from 45.8% in 2017 (EMOM 33.3%) to 58.82% (EMOM 41.18%) in 2021. The mean average daily growth of the weight improved (p<0.001) during this period. There is no statistical difference between infants fed with MOM and those fed with bank milk. The maternal age, the start day of the MOM feeding and gestational age have a significant impact on the type of feeding at discharge.

In this study, we performed a bibliographical review examining the scientific literature on “feeding in Anthozoa” theme for the period from 1890 to 2019, using scientific databases (Google Scholar) supplemented with additional literature. This study categorized published scientific papers on this topic by decade of publication, target taxa, variability of species studied in each order and main themes studied. As a result, 153 studies were found, and based on their content, it was observed that within Anthozoa, there has been a concentration of feeding studies on species in the orders Actiniaria (Hexacorallia), Scleractinia (Hexacorallia), and Alcyonacea (Octocorallia). This indicates that the other remaining orders of the group have been comparatively neglected with regards to their feeding aspects. Therefore, as data on feeding in some groups of Anthozoa are scarce, studies need to be carried out to fill the gaps that permeate this important benthic group, in order to better understand their ecology.

Aims and objectives: The primary aim of our study was to evaluate morbidity and mortality following feeding jejunostomy in pancreaticoduodenectomy compared to the control group. We also evaluated individual complications like delayed gastric emptying; post operative pancreatic fistula, superficial and deep surgical site infection. Material and Methods: The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and MOOSE guidelines. Heterogeneity was measured using Q tests and I2.the random-effects model was used. Results: Four studies including Total of 1639 patients were included in the analysis. Total 843 patients were included in Feeding jejunostomy group and 796 patients included in control group without feeding jejunostomy. Over all morbidity was significantly higher in feeding jejunostomy group. (P = 0.001). There was no significant difference between both the groups. (P=0.07). Delayed gastric emptying was significantly higher in feeding jejunostomy group. [P=0.021]. There was no significant difference in development of pancreatic fistula between the two groups. Deep surgical site infection was significantly higher in feeding jejunostomy group. (P=0.013). Hospital stay was significantly more in feeding jejunostomy group (p<0.0001). There was no significant difference between readmission; TPN requirement and time to start oral feed. Conclusion: Feeding jejunostomy seems to be associated with increased morbidity and increased length of stay.

Originally developed to image the shadow region of the central black hole in Sagittarius A* and in the nearby galaxy M87, the Event Horizon Telescope (EHT) provides deep, very high angular resolution data on other AGN sources too. The challenges of working with EHT data have spurred the development of new image reconstruction algorithms. This work briefly reviews the status of the EHT and its utility for observing AGN sources, with emphasis on novel imaging techniques that offer the promise of better reconstructions at 1.3 mm and other wavelengths.

In this work, a general framework for the analysis of governance of artificial intelligence is presented. A dashboard is presented for this analysis from the perspective of political theory. The dashboard allows eventual comparisons between democratic and non democratic regimes, useful for countries in the global south and western countries. The dashboard allows us to assess the key features that determine the governance model for artificial intelligence at the national level, for local governments and for other participant actors. The dashboard allows us to appraise aims of AI strategy and aims left aside in alternative strategies. The work shows the theoretical framework could be of use to advance case studies globally, and comparative endeavors. Applying the theoretical dashboard, the case study on China has been undertaken. These are the main points the study tackles in the final discussion, conclusion and further recommendations: 1) the complex reality of AI command and control 2) uncertainties about future society and the polity against AI development and 3) particular cultural values enshrined in countries' AI development.

Preterm infants have increased body adiposity at term equivalent age and risk for adverse metabolic outcomes. The aim of the study was to define how nutrient intake may impact body composition (BC) of very low birth weight infants fed with early progressive enteral feeding and standard fortification. Eighty-six infants with

Llayta is a dietary supplement used by rural communities in Perú and northern Chile since pre-Columbian days. Llayta is the biomass of colonies of a Nostoc cyanobacterium grown in wetlands of the Andean highlands, harvested, sun-dried and sold as an ingredient for human consumption. The biomass has a substantial content of essential amino acids (58% of total amino acids) and polyunsaturated fatty acids (33% total fatty acids). This ancestral practice is being loss and the causes were investigated by an ethnographic approach to register the social representations of Llayta, to document how this Andean feeding practice is perceived and how much the community knows about Llayta. Only 37% of the participants (mostly adults) have had a direct experience with Llayta; other participants (mostly children) did not have any knowledge about it. These social responses reflect anthropological and cultural tensions associated to lack of knowledge on Andean algae, sites where to find Llayta, where it is commercialized, how it is cooked and on its nutritional benefits. The loss of this ancestral feeding practice, mostly on northern Chile, is probably associated to cultural changes, migration of the rural communities, and a very limited access to the available information. We propose that Llayta consumption can be revitalized by developing appropriate educational strategies and investigating potential new food derivatives based on the biomass from the isolated Llayta cyanobacterium.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

Agroindustrial by-products constitute an alternative source of feed livestock, and their use contributes to the sustainability of livestock systems and the circular bioeconomy. The effects of replacing cereal (0%, 40% and 80%) with dehydrated orange pulp (DOP) in the diet of goats on the antioxidant and fatty acid (FA) contents of cheeses were evaluated. For a more suitable understanding of the role of coagulant enzymes in establishing the properties of the cheese, the effect of milk-clotting with animal and vegetable rennet was also analysed. The rennet did not substantially affect the FA or the antioxidant compounds, and the use of DOP did not affect the FA contents. However, the α-tocopherol levels, total phenolic compounds (TPC), and total antioxidant capacity (TAC) in cheeses increased as the percentage of DOP replacing cereals increased. Moreover, the high correlation obtained between the TAC and the TPC (r = 0.73) and α-tocopherol (r = 0.62) contents indicated the important role played by these compounds in improving the antioxidant capacity of the cheese. In conclusion, DOP is a suitable alternative to cereals in the diet of goats and improves the antioxidant status of the cheese produced.

Understanding tumor attachments and management of feeding arteries are very important factors to achieve safe resection of skull base meningioma. We examined the distributions and frequencies of feeding arteries in skull base meningioma by cerebral angiography, and also assessed relationships to tumor attachment for skull base meningioma. We enrolled patients with skull base meningioma who underwent MRI and cerebral digital subtraction angiography (DSA), from September 2015 to October 2022. Subjects comprised 115 patients (32 males, 83 females; mean age, 52.7 years) with 117 meningiomas showing tumor attachments around the “cavernous sinus to upper part of the clivus” (Area 1), “lower part of the clivus to foramen magnum” (Area 2), and “tentorium around the petrous bone” (Area 3). We examined feeding arteries based on cerebral angiography and counted the number of related feeding arteries. Particularly frequent arteries, such as the dorsal meningeal artery (DMA), ascending pharyngeal artery (APA), tentorial artery (TA), and petrosal branch (PB) of the middle meningeal artery (MMA) were analyzed for associations of tumor attachment to Areas 1–3. DMA, APA, TA, and PB are very frequent feeding arteries among skull base meningiomas. In some cases, the artery of the foramen rotundum, posterior ethmoidal artery, recurrent meningeal artery, posterior meningeal artery, etc., supply blood to the skull base meningioma. Meningiomas with DMA as a feeding artery correlated strongly with tumor attachment to Area 1 (p&lt;0.001). Meningiomas with APA correlated strongly with tumor attachment to Area 2 (p&lt;0.001). Meningiomas with TA correlated strongly with tumor attachment to Area 3 (p&lt;0.001). PB correlated with Area 3 (p&lt;0.05). Visualization of these arteries suggests tumor attachments to the correlated areas. An accurate anatomical understanding of tumor attachment and feeding arteries is essential to determine appropriate surgical strategies for skull base meningioma. Pre-operative DSA can provide further understanding of the feeding arteries and attachments of skull base meningioma.

Contrafreeloading (CFL) refers to animals' tendency to prefer obtaining food through effort rather than free access to food. Researchers have proposed various hypotheses to explain this intriguing phenomenon, but few studies have comprehensively analyzed its influencing factors. In this study, we observed the choice of alternative food containers in budgerigars to investigate their CFL tendencies and the effects of pre-training, food deprivation, and effort required on the CFL levels. The results show that, like most captive animals, budgerigars without food deprivation tend to choose challenging food containers. Additionally, the study finds that pre-trained budgerigars prefer to choose highly challenging food containers, while food-deprived budgerigars tend to choose Easy food containers. Moreover, budgerigars showed more selection time proportion on moderate challenging than high challenging food containers. In conclusion, budgerigars exhibit CFL tendencies, and pre-training enhances their CFL levels, while high challenge and deprivation of food decrease their contrafreeloading level. Studying animal contrafreeloading can help understand why animals choose to expend effort to obtain food rather than accessing it for free, and it also has implications for setting feeding environments to enhance the animal welfare of captive and domesticated animals.

Each individual has a unique gut microbiota; therefore the genes in our microbiome outnumber the genes in our genome by about 150 to 1. Perturbation in host nutritional status influences gut microbiome composition and vice versa. The gut microbiome can help in producing vitamins, hormones, and other active metabolites that support the immune system; harvest energy from food; aid in digestion; protect against pathogens; improve gut transit and function; send signals to the brain and other organs, oscillating the circadian rhythm and coordinate with host metabolism through multiple cellular pathways. Gut microbiota can be influenced by host genetics, medications, diet, and lifestyle factors from preterm to aging. So before prescribing a customized treatment, it is crucial to monitor and count the gut flora as a focused biomarker. Many nutritional approaches that have been developed help in maintaining and restoring an optimal microbiome such as specific diet therapy, nutrition interventions and customized eating patterns. One of these approaches is time-restricted feeding/eating (TRF/E), a type of intermittent fasting (IF) in which a subject abstains from food intake for a specific time window. Such a dietary modification might alter and restore the gut microbiome for proper alignment of cellular and molecular pathways throughout the lifespan. In this review, we have highlighted that gut microbiota would be a targeted biomarker and TRF/E would be a targeted approach for restoring the gut microbiome associated molecular pathways like hormonal signaling, the circadian system, metabolic regulators, neural responses, and immune-inflammatory pathways. Consequently, modulation of gut microbiota through TRF/E could contribute in proper utilization and availability of the nutrients and in this way confer protection against diseases for harnessing personalized nutrition approaches to improve human health.

Along with the rapid development and wide application of Artificial Intelligence (AI), discussions regarding AI Sustainability have emerged. Employing a systematic mapping study, this paper reviews research about AI Sustainability from diverse fields and viewpoints, including three dimensions economic, social, and environmental. Our analysis indicates a transformative shift in research focus, evolving from fragmented approaches to a more holistic understanding of AI Sustainability. This maturation is reflected in the interplay between multiple dimensions and the integration of both "AI as a tool for sustainability" and "Sustainability of AI" perspectives. As the field continues to mature, researchers should embrace a comprehensive and unbiased exploration of AI Sustainability across all dimensions and approaches, ensuring a well-rounded foundation for future advancements in this rapidly evolving domain.

Artificial Intelligence (AI) has emerged as a disruptive technology in various areas, and medicine is no exception. In the last decade, there has been rapid progress in the application of AI in healthcare, from diagnosing and prognosticating diseases to improving operational efficiency in hospitals. This literature review explores the advances of artificial intelligence in the field of medicine, discusses its benefits, and discusses the ethical and regulatory challenges that need to be addressed to maximize its potential.

Grain legumes are fair sources of protein, amino acids and energy, and can be used as a replacement for soybean meal in poultry feed formulations as the soybean meal becomes short in supply and costly. However, a concern associated with the use of grain legumes in poultry feeding is the presence of antinutritional factors. Effective processing and utilisation of these grain legumes in poultry feeding are well documented. The current review focuses on four selected grain legumes (lupins [Lupinus albus and Lupinus angustifolius], field peas [Phaseolus vulgaris], faba beans [Vicia faba] and chickpeas [Cicer arietinum]) and their nutrient content, the presence of antinutritional factors, processing methods and feeding value, including updated data based on recent research findings.

Energy management systems (EMS) play a central role in improving the performance of microgrids by ensuring their efficient operation while minimizing operational costs and environmental impacts. This paper presents a comprehensive study of a MILP-based EMS developed and implemented in MATLAB using Yalmip software for the energy management of a new positive energy district in the city of Savona, Italy, as part of the Interreg Alpine Space Project ALPGRIDS. The main objective of this research is to optimize the functioning of the microgrid, focusing on cost efficiency and environmental sustainability. In pursuit of this objective, the EMS undergoes comprehensive testing and analysis, replicating actual conditions and addressing the diverse demands of end-users across typical days throughout the year considering real electricity selling and purchase price. The EMS also accounts for the reactive power capabilities of the various technologies integrated into the microgrid. The levelized cost of electricity (LCOE) serves as a metric for assessing curtailment costs, while penalties related to reactive power absorption from the distribution network are appraised in alignment with prevailing regulatory guidelines. The case study provides valuable insights into the practical implementation of EMS technology in microgrids and demonstrates its potential for sustainable energy management in complex urban energy districts. The insights and strategies developed through this research contribute to the overall goals of improving energy efficiency and reducing the environmental footprint of future energy districts.

The western flower thrips, Frankliniella occidentalis, is invasive and polyphagous. Its visual signal plays a crucial role in host location, suggesting that thrips has diurnal behavior. However, any diel rhythmicity of the thrips behavior is not yet well understood. This study analyzed the diurnal rhythmicity of the feeding and mating behaviors of thrips, and the patterns were explained in terms of diel rhythmicity by assessing the expression of genes associated with the circadian clock. Most feeding behaviors were observed during photophase in both larvae and adults. In mating behavior, which is mainly exhibited in 2~3 days-old young adults after emergence, most adults copulated at photophase. This diurnal pattern of the mating behavior was further supported by the analysis of a temporal calling behavior for mating, which was biased toward photophase. Four genes associated with the circadian clock were predicted in the F. occidentalis genome: period (PER), Timeless (TIM), Doubletime (DBT), and clock (CLK). All these genes exhibited a characteristic diel pattern in their gene expressions in a 24 h-period. The high expression peaks of these genes except for DBT were observed at photophase. To confirm the functional association of these genes with the feeding and mating behaviors, individual RNA interference specific to each gene was executed by feeding the gene-specific double-stranded RNA. Aside from DBT, suppression of these gene expressions resulted in significant deviations of the diel rhythms of the behaviors. These results suggest that the genetic components of the diel rhythmicity mediate the diurnal feeding and mating behaviors of F. occidentalis.

Terpenoids are toxic compounds produced by plants as a defense strategy against insect herbivores. We tested the effect of Origanum vulgare terpenoids on the generalist herbivore Spodoptera littoralis and the response of the plant to herbivory. Terpenoids were analyzed by GC-FID and GC-MS and quantitative gene expression (qPCR) was evaluated on selected plant genes involved in both terpene biosynthesis. The insect detoxification response to terpenes was evaluated by monitoring antioxidant enzymes activity and expression of insect genes involved in terpene detoxification. O. vulgare terpenoid biosynthesis and gene expression was modulated by S. littoralis feeding. The herbivore-induced increased level of terpenoids (particularly carvacrol and p-cymene) interacted with the herbivore by decreasing larval survival and growth rate. The assimilation by S. littoralis of more than 50% of ingested terpenes correlated with the possible toxic effects of O. vulgare terpenoids. In choice test experiments, carvacrol and γ-terpinene mediated the larval feeding preferences, wherease the prolonged feeding on O. vulgare terpenoids (particularly on γ-terpinene) exerted relevant antinutritional effects on larvae. S. littoralis was found to react to O. vulgare terpenoids by increasing its antioxidant enzymes activities and gene expression, although this was not sufficient to sustain the toxicity of O. vulgare terpenoids.

Introduction The increase in survival of extremely preterm infants has led to increased rates of bronchopulmonary dysplasia (BPD). Therefore, a potential role of human milk feeding in protecting against this condition has been suggested. Material and methods Retrospective descriptive study based on data about morbidity in the population of infants born from 22+0 to 26+6 weeks of gestation, included in the Spanish network SEN1500 during the period 2004-2019 and who were alive at discharge. The primary outcome was moderate-severe BPD. In addition, associated conditions were studied, including breastfeeding at discharge. The temporal trends of BPD and breastfeeding rates at discharge were also studied. Results In the study population of 4341 infants who survived to discharge, the rate of moderate-severe BPD was 43,7% and increased over the period to a rate >50% in the last three years. The factors significantly associated with a higher risk of moderate-severe BPD were male sex, high-frequency oscillatory ventilation, inhaled nitric oxide, patent ductus arteriosus, and late-onset sepsis. Exclusive human milk feeding at discharge and any amount of human milk at discharge were associated with a lower incidence of moderate-severe BPD (OR 0.752, 95% CI 0.629-0.901, and OR 0.714, 95% CI 0.602-0.847, respectively). The rates of human milk at discharge in infants with moderate-severe BPD increased over the period. In the last years, more than one-third of extremely preterm infants were discharged on exclusive human milk feeds, and about two-thirds of them on any amount of human milk feeding. Conclusions
The results of the present study strongly support the role of any amount of human milk in preventing BDP in extremely preterm infants.

Understanding and controlling reproductive cycles and gonadal maturation in cultivated species are crucial in aquaculture. The sea urchin Paracentrotus lividus, known for its edible gonads, requires careful maturation control for both reproduction and commercialization. This study explores the impact of temperature, photoperiod, and diet on gonadal development to enhance year-round aquaculture practices. Using two independent cultivation systems, we manipulated environmental conditions to mimic different seasons. Sea urchins were exposed to natural or manipulated temperature and photoperiod conditions and fed either natural (Ulva spp.) or formulated diets. The gonadosomatic index (GI) and histological analysis assessed gonadal development. Results revealed a clear correlation between environmental conditions, diet, and gonadal maturation. Manipulated conditions accelerated maturation, with sea urchins showing advanced stages compared to natural conditions. Furthermore, sea urchins fed formulated diets exhibited higher GI values, indicating enhanced maturation. Histological analysis confirmed accelerated maturation, particularly in females. The study underscores the feasibility of controlling gonadal maturation through environmental manipulation and diet, enabling year-round marketable gonad production. Providing formulated diets rich in polyunsaturated fatty acids, notably docosahexaenoic acid (DHA), enhances commercial value. These findings optimize aquaculture practices for P. lividus, highlighting its adaptability to yield-maximizing production strategies. Understanding and controlling reproductive cycles and gonadal maturation in cultivated species are crucial in aquaculture. The sea urchin Paracentrotus lividus, known for its edible gonads, requires careful maturation control for both reproduction and commercialization. This study explores the impact of temperature, photoperiod, and diet on gonadal development to enhance year-round aquaculture practices. Using two independent cultivation systems, we manipulated environmental conditions to mimic different seasons. Sea urchins were exposed to natural or manipulated temperature and photoperiod conditions and fed either natural (Ulva spp.) or formulated diets. The gonadosomatic index (GI) and histological analysis assessed gonadal development. Results revealed a clear correlation between environmental conditions, diet, and gonadal maturation. Manipulated conditions accelerated maturation, with sea urchins showing advanced stages compared to natural conditions. Furthermore, sea urchins fed formulated diets exhibited higher GI values, indicating enhanced maturation. Histological analysis confirmed accelerated maturation, particularly in females. The study underscores the feasibility of controlling gonadal maturation through environmental manipulation and diet, enabling year-round marketable gonad production. Providing formulated diets rich in polyunsaturated fatty acids, notably docosahexaenoic acid (DHA), enhances commercial value. These findings optimize aquaculture practices for P. lividus, highlighting its adaptability to yield-maximizing production strategies.

Background: Patients in the postoperative period following bariatric surgery are at risk of developing eating disorders. This study aims to analyse the relation between bariatric surgery and the development and recurrence of eating disorders. Material and methods: Literature review was done on 15th November 2020. Fourteen studies that met the eligibility criteria were included for qualitative synthesis, and 7 studies for meta-analysis. Results: The prevalence of eating disorders in the postoperative period was 7.83%, based on the 7 studies in the meta-analysis. Binge eating disorder alone was 3.81%, which was the most significant factor, and addressed in 6 of these studies. Conclusion: The investigated studies have significant methodological limitations in assessing the relation between bariatric surgery and eating disorders, since they mostly present data on prevalence. PROSPERO CRD42019135614.

Human action recognition (HAR) based on skeleton data is a challenging yet important technique because of its wide range of applications in many fields, including patient monitoring, security surveillance, and observing human-machine interactions. Many algorithms that attempt to distinguish between many types of activities have been proposed. However, most practical applications require highly accurate detection of specific types of activities. In this study, a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data is proposed. Furthermore, an extensive study was conducted using different modalities. For this purpose, a spatiotemporal graph autoencoder that automatically learns spatial as well as temporal patterns from human skeleton datasets was built. The powerful graph convolutional network named GA-GCN, developed in this study, notably outperforms most of the existing state-of-the-art methods based on two common datasets, namely NTU RGB+D and NTU RGB+D 120 . On the first dataset, we achieved an accuracy of 92.3% and 96.7% based on the cross-subject and cross-view evaluations, respectively. On the other more challenging dataset (i.e. NTU RGB+D 120), GA-GCN achieved 88.8% and 90.4% based on the cross-subject and cross-set evaluation, respectively.

The diet and feeding behavior of South Polar Skuas (Stercorarius maccormicki) are well studied within the species' breeding range, but are poorly understood on the Haswell Islands. Goal: to establish how South Polar Skuas used the abundance of living resources of the Haswell Archipelago (66°31' S, 93°01' E, Davis Sea, Southern Ocean) under conditions of prolonged human activity. Methods: direct observations, study of pellets, spontaneous regurgitation and stomach contents of feathered birds. South Polar Skuas fed primarily on terrestrial resources. The dominant prey of skuas were vertebrates. Emperor Penguins (Aptenodytes forsteri) and Adélie Penguins (Pygoscelis adeliae) make up the bulk of the skuas’ diet in the pre-breeding and breeding periods. Surface feeding at sea was observed in the post-breeding period, when the availability of land-based food resources was reduced. Throughout history, kitchen waste supported the skuas population. Scavenging is the dominant strategy for obtaining food. Predation (primarily on Adélie Penguin eggs and chicks) is of minor importance. South Polar Skuas do not affect the breeding success of Emperor Penguins if they feed on frozen chicks and eggs in their colony. Skua predation influences breeding success of Adélie Penguins and fulmarine petrels, but the extent of the impact is unknown.

The prevalence of nonalcoholic fatty liver disease (NAFLD) has been rapidly increasing worldwide. A choline-deficient L-amino acid-defined high fat diet (CDHFD) has been used to create a mouse model of nonalcoholic steatohepatitis (NASH). There are some reports about the effects on mice of being fed CDAHFD for a long time, 1 to 3 months. However, the effect of this diet over a short period has been unknown. Therefore, we examined the effect of one week of feeding CDAHFD on the mouse liver. Feeding this diet for only one week induced lipid droplet deposition in the liver with increasing activity of liver-derived enzymes in the plasma. On the other hand, it did not induce fibrosis and cirrhosis. Additionally, it was demonstrated that mitochondrial respiration is significantly impaired with severe oxidative stress in the liver by CDAHFD, associated with a decreasing mitochondrial DNA copy number and complexes-proteins. In the gene expression analysis of the liver, inflammatory and oxidative stress markers were significantly increased by CDAHFD. These results demonstrated that one week of feeding CDAHFD to mice induces steatohepatitis with mitochondrial dysfunction and severe oxidative stress, without fibrosis, which can partially mimic the early stage of the NASH in humans.

Artificial Intelligence (AI) tools and techniques are comparatively recent advances of present-day globalized world. With their easy availability and friendly user interface AI has affected every sphere of human being. These tools are increasingly becoming popular because of the numerous benefits occurred to the society in many ways. However, there are not only the positive side of these technologies, but also many negative sides. Along with the development and adoption of AI, cyber-attacks are increased and disrupting the information system. Threat actors may misuse artificial intelligence. Hence, these tool designers are working and applying the ability of generative AI can tip the scales in cybersecurity. So, in the age of AI, there is always tension between the cyber attackers and the defenders to make a balanced information system. It is a general rule that defenders must be well in advance in their favor and keep ahead of adversaries. The United National and several governments for example the US, UK, and the big AI giants like Microsoft along with OpenAI, are working on emerging cyberattacks. This research paper explores the concept of information resilience in ML and AI systems, focusing on strategies to enhance their ability to withstand uncertainties, adversarial attacks, and data perturbations.

The recent remarkable progress in Artificial Intelligence (AI) has renewed the discussion on the possibility of human-level AI. Despite the difficulty of the problem and the presence of many large gaps and challenges, most people working in the field think that human-level AI is achievable, but they disagree on the date. In this paper, I briefly discuss the possibility of human-level AI from the perspective of two traditions in the Islamic world, namely Kalam and Sufism, and despite their different mode of investigation, they converge to the same conclusion on this topic.

Historically the use of artificial intelligence (AI) in paediatrics dates back to 1984 with the intro-duction of a computer-assisted medical decision-making system called SHELP. Since then, research on the use of AI in pediatrics has become much more popular, and the number of reviewed publications largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into, deepen their knowledge about or inform them about the use of AI in pediatrics arised. To fill this gap a novel methodology named synthetic knowledge synthesis was applied. Using SKS we identified most prolific countries, in-stitutions, source titles, funding agencies, research themes, research gaps and hot spots.

The artificial intelligence (AI) is a machine or computing platform that is capable of making intelligent decision similarly to the human mind. The AI could improve diagnosis, treatment prognosis, and clinical workflow, particularly in the field of radiology. Concerning acute respiratory distress syndrome (ARDS) is a rather heterogeneous syndrome characterized by an inflammatory lung edema leading to an increased lung weight, decreased lung aeration with the presence of alveolar collapse and interstitial opacities mainly in the dependent area. Thus, lung imaging is an essential tool to assess not only the morphology but also the mechanical characteristics of ARDS patients. Chest computed tomography (CT) and ultrasound have a higher sensitivity and specificity than conventional chest radiography. This narrative review summarizes the state of art of AI in the field of lung imaging, focusing on CT and ultrasound technique in ARDS patients. A total number of 18 articles were retrieved. The application of AI in lung imaging was mainly devoted to assess the prediction of ARDS, the quantification of alveolar recruitment, the possible alternative diagnosis and the outcome. Although the presence of a physician is still essential to ensure a high quality of examinations the AI could assist the clinical team to provide the best possible care.

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning. We provide our ideas about how to combine this optimization method with evolutionary algorithms in order to boost the development of specialized Artificial Neural Networks, which define the proprioceptive configuration of particular robotic units that are part of a swarm. We consider how optimization of the free-energy can promote the homeostasis of the swarm system, i.e. ensures that the system remains within its sensory boundaries throughout its active lifetime. We will show how complex distributed cognitive systems can be build in the form of hierarchical modular system, which consists of specialized micro-intelligent agents connected through information channels. We will also consider the co-evolution of various robotic swarm units, which can result in development of proprioception and a comprehensive awareness of the properties of the environment. And finally, we will give a brief outline of how this system can be implemented in practice and of our progress in this area.

Environmental monitoring systems play a crucial role in assessing environmental quality, detecting limits exceedances, and predicting potential ecological episodes. These systems rely on the measurement of various variables at specific locations and time intervals over an extended period. The concept of environmental monitoring encompasses the assessment of health and safety issues for public and environmental health purposes. Pollution of the atmosphere and water, climate change, and natural disasters are among the consequences of continuous industrial and municipal development and human interference in natural ecosystems. To address these challenges and to protect human lives and the environment, with a special concern on mitigating the ecological effects of industrial development, advanced technical solutions, including the technologies associated with artificial intelligence (artificial neural networks ANNs, machine learning ML) have been developed. These technologies offer powerful tools for analysing the vast amount of data collected by monitoring systems and extracting valuable insights. By applying ANNs and machine learning algorithms, environmental monitoring systems can effectively process and interpret the measured variables to assess environmental quality. Despite challenges and limitations, such as data quality and interpretability of AI models, ongoing research and interdisciplinary collaboration are paving the way for the successful implementation of AI in environmental monitoring, ultimately supporting informed decision-making and sustainable resource management.While several review papers have explored the theory of artificial intelligence (AI), here I aim to review the application of ANNs and ML, in environmental aspects, specifically in automotive and industrial emissions toxicity measurements, as well as atmospheric pollution prevention. By examining the potential of AI in these domains, the paper contributes to understanding the role of advanced technologies in environmental monitoring and protection.

Mastitis-causing bacteria can establish persistent infections in the mammary gland of commercially important dairy animals despite the presence of strong specific humoral and cellular immune mechanisms. The aim of this study was to encapsulate vitamin D3 by complex coacervation with sulfur-saturated bovine lactoferrin-alginate using microbial transglutaminase-catalyzed crosslinking and investigate the oral administration of encapsulated vitamin D3 in blood serum biomarkers of the immune response in late-lactating dairy goats. Dairy goats (n = 18) in late lactation were randomly assigned to three experimental groups (n = 6), after balancing weight. Dairy goats were orally administered with 0.35 mg vitamin D3/day in the unencapsulated form and 0.35 mg vitamin D3/day in the encapsulated powder form. Another group received the basal diet. The experimental period lasted 6 weeks. The blood serum concentrations of 25-hydroxyvitamin D3 [25-(OH)-D3], lactoferrin, immunoglobulin A (IgA), and interferon-gamma (INF-γ) were measured. The delivery of vitamin D3 to dairy goats resulted in a marked increase of 25-(OH)-D3 concentration in serum while the serum level of lactoferrin also increased. Alternatively, the serum levels of IgA and the immunomodulatory cytokine INF-γ were elevated following supplementation with the encapsulated vitamin D3. Overall, the capsules in the powder form used in this study kept vitamin D3 highly bioavailable.

Litchi (Litchi chinensis Sonn.) is considered as one of the most important sub-tropical fruits of the World. Under western part of Odisha, India, the litchi growers are facing problem of unstable and lower marketable yield and inferior quality due to higher incidence of fruit cracking, fruit drop, low sugar content and higher fruit acidity. Keeping in mind the positive effects of nutrients and bioregulators, the current study was conducted to elucidate their impact on fruit yield, and quality in the farmers field of Jamankira block in Sambalpur district of Odisha under Odisha University of Agriculture and Technology, India. For the studies, eight years old litchi trees were selected. With 12 treatments, the experiment was set up in a randomized block design replicated thrice viz, T₁: Spray treatment of Borax @ 0.5%; T₂: Spray treatment of Borax @ 0.3%; T₃: Spray treatment of ZnSO₄ @ 0.75% ; T₄: Spray treatment of ZnSO₄ @ 0.5%; T₅: Spray treatment of CaCl₂ @ 0.5%; T₆: Spray treatment of CaCl₂ @ 0.1%; T₇: Spray treatment of Humic acid @ 1.5%; T₈: Spray treatment of Humic acid @ 1%; T₉: Spray treatment of Seaweed extract @ 0.5%; T₁₀: Spray treatment of Seaweed extract @ 0.1 %; T₁₁: Foliar spray of NAA @ 20 ppm and T₁₂ : Control (water spray).The current study compared foliar feeding of different nutrient and bioregulators treatments, which were applied during the first week of December, just after completion of new leaf formation and untreated control. The total number of fruits per plant was recorded highest in plants sprayed with ZnSO4 @ 0.5% (T₄) followed by those treated with humic acid @ 1% (T₈). The highest total fruit yield was recorded in plants with foliar feeding of 0.3% Borax (T₂) which was found to be statistically similar to plants treated with seaweed extract @ 0.1% (T₁₀)and seaweed extract @ 0.5% (T₉).Among the treatments better response for higher number of marketable fruits and marketable yield was recorded in plants treated with both 0.3% Borax (T₂) followed by 0.5% Zinc sulphate (T₄), 1% Humic acid (T₈) and 0.1% CaCl₂ (T₆) in litchi. Application of 1% Humic acid (T₈) followed by 1.5% Humic acid (T₉) enhanced the fruit set (%) and fruit retention (%) along with reduction in fruit drop (%). The enhanced fruit size (fruit length and fruit breadth) and higher fruit weight was obtained in plants treated with 0.3% Borax in litchi. The foliar application of 0.3% Borax (T₂) had also resulted in higher TSS, total sugars, reducing sugar, lower acidity, highest aril weight and lower seed weight in litchi cv. Bombai.

Fifty-eight Karacabey Merino lambs were used to evaluate the effects of feeding system (triticale pasture, oat pasture, or stall fed), birth type (single or multiple), gender (male or female) and birth year (2016 or 2017) on various carcass and meat quality characteristics. Stall lambs had higher conformation (CS) and fatness scores (FS), meat L* and h* values than both pasture groups, possibly as a result of higher fat content, while oat pasture lambs had the most tender meat. Single lambs had higher CS, FS and Longissimus thoracis muscle section area, while females had higher subcutaneous and non-carcass fat depositions, over their counterparts. Both single born lambs and 2016 born lambs had a higher meat a* and C* values than their counterparts. In conclusion, a pasture-based feeding system is thought to be more suitable for Karacabey Merino than stall feeding system, for those who prefer leaner meat and/or meat products. However, it should not be ignored that stall fed lambs showed a better fattening performance in terms of conformation and fatness, in the same period.

In Low and Lower-Middle-Income countries, the prevalence of anemia in infancy remains high. In early childhood anemia cause irreversible cognitive deficits and represents a higher risk of child mortality. The consequences of anemia in infancy are a major barrier to overcome poverty traps. The aim of this study was to analyze based on a multi-level approach, different factors associated with anemia in children 6–23 m old based on recent available Standard Demographic Health Surveys (S-DHS). We identified 52 S-DHS that had complete information in all covariates of interest in our analysis between 2005 and 2015. We performed traditional logistic regressions and multilevel logistic regression analyses to study the association between hemoglobin concentrations and household, child, maternal, socio-demographic variables. In our sample, 70 % of the 6–23 m old children were anemic. Child anemia was strongly associated with maternal anemia, household wealth, maternal education and low birth weight. Children fed with fortified foods, potatoes and other tubers had significantly lower rates of anemia. Improving overall household living conditions, increasing maternal education, delaying childbearing and introducing iron rich foods at six months of age may reduce the likelihood of anemia at in toddlerhood.

The impact of cyclic heat stress (CHS) and lighting events on pig feeding behavior (FB) was investigated. The FB of 90 gilts was recorded in real time under two ambient temperatures (AT): thermoneutrality (TN, 22 °C) or CHS (22°/35 °C). The day was divided into four periods: PI (06-08 h); PII (08-18 h); PIII (18-20 h); and PIV (20-06 h). Automatic and Intelligent Precision Feeders recorded each feed event for each pig. An estimated meal criterion (49 minutes) was used to calculate the FB variables. Feed behavior in both ATs followed a circadian pattern. The CHS reduced the feed intake by 6.9%. Pigs prioritized feed intake during the coolest hours of the day; however, nocturnal cooling did not allow the pigs to compensate for the reduced meal size due to CHS. The highest meal size and most of the meals were observed during the lighting-on period. Pigs reduced their interval between meals during periods II and III. The lighting program increased the meal size when the lights were switched on and reduced the meal size when the lights were switched off. Thus, the dynamics of the FB were largely influenced by AT, while the meal size was affected by the lighting program.

Humpback whales perform long migrations from their breeding and nursing areas at low latitudes to feeding grounds at high latitudes. Nonetheless this strictly dichotomous paradigm of migration is challenged by accumulating examples of occasional or regular feeding in tropical or subtropical areas for several stocks worldwide. Here we report multiple lines of evidence of IWC ‘Stock G’ humpback whales feeding in coastal waters at low latitudes of the Southeast Pacific Ocean. Lunge-feeding behavior was observed in Ecuador, while both lunge- and trap-feeding in pursuit of Peruvian anchovy was documented in northern Chile. Five records of substantial defecation in a key breeding ground in Ecuador also demonstrates local foraging. In addition mark-recapture analysis of one individual feeding at high and mid latitudes of Chile suggested potential site fidelity to two foraging areas. Whether these behaviors are novel due to changes in prey distribution, intensifying competition for food from a growing humpback whale population, or rather reflect vastly increased research effort in a poorly studied region, remains unknown. Further research into the feeding ecology of 'Stock G' should help reveal historic and potentially novel feeding grounds, prey composition and precise migration paths.

The term artificial intelligence refers to the field of computer science that deals with the design and implementation of computer systems that mimic elements of human behavior that imply basic intelligence: learning, adaptability, inference, contextual understanding, and problem solving among others. Artificial intelligence is a point of intersection between multiple sciences such as computer science, psychology, philosophy, neurology, linguistics and engineering science, with the aim of synthesizing intelligent behavior, with elements of reasoning, learning and adaptation to the environment, while it is usually applied to specially made machines or computers.

This work has its origin in intuitive physical and statistical considerations. The problem of optimizing an artificial neural network is treated as a physical system, composed of a conservative vector force field. The derived scalar potential is a measure of the potential energy of the network, a function of the distance between predictions and targets. Starting from some analogies with wave mechanics, the description of the sys-tem is justified with an eigenvalue equation that is a variant of the Schr˜odinger equation, in which the potential is defined by the mutual information between inputs and targets. The weights and parameters of the network, as well as those of the state function, are varied so as to minimize energy, using an equivalent of the variational theorem of wave mechanics. The minimum energy thus obtained implies the principle of minimum mutual information (MinMI). We also propose a definition of the potential work produced by the force field to bring a network from an arbitrary probability distribution to the potential-constrained system, which allows to establish a measure of the complexity of the system. At the end of the discussion we expose a recursive procedure that allows to refine the state function and bypass some initial assumptions, as well as a discussion of some topics in quantum mechanics applied to the formalism, such as the uncertainty principle and the temporal evolution of the system. Results demonstrate how the minimization of energy effectively leads to a decrease in the average error between network predictions and targets.

Cytokine and AMH profiling were done in prepubertal Murrah buffalo heifers post-progesterone supplementation and co-synchronization protocol. Cytokines: IFNγ, IL6, IL1, IL13, TNFα, and TGFβ, and, AMH, Progesterone and Estrogen hormone estimations were done. Ovarian follicular pattern and fertility outcome were recorded. Eighteen prepubertal heifers, 15-17 months of age, 250-300 kg BWt were randomly divided into 3 groups, Group1: n=6, supplemented with altrenogest 0.044mg/kg/day/heifer orally for 14 days along with Co-synch programme; Group2: n=6, administered CIDR for 14 days and co-synch programme, Group3: n=6, received co-synch programme. Ultrasonography was done to ovarian follicle status on respective days of sampling. Pregnancy diagnosis was done around 45 and 60 post-AI. Estradiol 17-β level remained constant in altrenogest group, CIDR implanted and Control groups during the Progesterone treatment and Co-synch treatment. Estradiol levels during Co-synch were significantly less on day 9 of Co-synch (14.41 ± 1.97 pg/mL) than day 0 and on day7 in pre pubertal buffalo heifers in altrenogest group. However, no significance was observed in other groups. Progesterone levels in buffalo heifers subjected to synchronisation protocols varied significantly (P< 0.05) on day 7 of P4 treatment between altrenogest supplemented and CIDR implanted groups, as well as, Co-synch groups. Supplementation of altrenogest showed a significant (P= 0.043) increase in Progesterone levels by day 14 of altrenogest treatment. Progesterone varied significantly in all Groups on day 9 of co synch protocol [ Group 1 (P= 0.02), Group 2 (P= 0.041) and Group 3 (P= 0.007)]. Cytokine IFN-γ showed high correlation with progesterone, indicating the role of IFN-γ in puberty in buffalo heifers (r= 0.626, P< 0.01). AMH had a significant positive correlation supplemented with altrenogest with IFN-γ (r= 0.673, P< 0.01) and TGF-β (r= 0.463, P< 0.01), whereas, with TNF-α it was a negatively correlated (r= -0.34, P< 0.05). Based on Karl Pearson correlation coefficients, IL-13 and TGF-β could be considered as markers for puberty in buffalo heifers.

Phenylketonuria (PKU) is an inherited disorder of protein metabolism. It is generally treated using dietary management with limited intake of phenylalanine (Phe). Partial breastfeeding (BF) is encouraged among mothers of infants with PKU, together with a Phe-free mixture of synthetic amino acids. We aimed to describe our current BF rates and complementary feeding practices, as well as examining parental experiences of infant feeding. The aim was to better understand the challenges faced by families so improvements can be made to clinical care. A chart review was carried out on 39 PKU patients, examining the BF rate and duration, use of second stage synthetic protein (SP) and average complementary feeding age. A parental questionnaire on complementary feeding and BF experience was designed. 26% of babies were partially breastfed at three months. 70% of mums would like to have breastfed for longer and cited PKU as a reason for stop-ping. 52% parents reported challenges during the complementary feeding process including food refusal, protein calculation and anxiety around maintaining good Phe levels. Suggestions to improve BF continuation and duration include active promotion of the benefits and suitability, access to lactation consultant and peer support. The delay in introducing a second stage SP may contribute to long-term bottle use for SP. Improved patient education, written resources and sup-port is necessary to improve food choices and long term acceptance of SP.

Abstract: The morphological characteristics of the tongue of the Rahmani sheep (Ovis aries) were described using gross inspection, morphometry analysis, and scanning electron microscopy. Nine mature male sheep heads were slaughtered, and samples were taken right away. The tongue was dissected, and various samples were obtained for SEM. The tongue had two different types of lingual papillae: mechanical (filiform, conical, and lenticular papillae) and gustatory (fungiform and vallate papillae). The main papillae and several secondary and tertiary papillae combine to produce each filiform papilla. On the dorsal surface of the tongue, there were five different types of conical papillae dispersed throughout, as well as three huge conical papillae and various-sized flattening papillae in front of the frenulum linguae. Furthermore, on the torus linguae, two different types of lenticular papillae were found. The majority of fungiform papillae possessed prominent taste buds' pores and were button-shaped with a central depression region, particularly in the rostral section of the tongue. On either side of the torus linguae, there are two rows of round, elliptical, pyramidal, and oval circumvallate papillae. Each papilla is surrounded by flat, broad, undulating annular rods and papillary grooves. The lingual surface also included numerous lin-gual gland openings in a variety of forms, which increased in number at the root. Finally, variations in the form, size, and number of lingual papillae, as well as the distribution of lingual gland openings along the lingual surface, may provide efficiency to the Rahmani sheep's tongue when feeding hard forages and high fiber foods.

Introduction: Body size is correlated with many aspects of an animal species' natural history, such as life span, abundance, dispersal capacity and diet breadth. However, contrasting trends have been reported for the relationship between body size and these ecological traits. Methods: Butterfly species from fruit-feeding guilds were used to investigate whether body size correlates with species abundances, dispersal, permanence, and diet breadth in a Neotropical savanna in Brazil (Cerrado). We used Blomberg’s K and Phylogenetic Generalized Least Squares models (PGLS) to measure phylogenetic signal strength in species traits, and to estimate size-dispersal-diet breadth associations while taking shared ancestry into account. Results: 539 individuals from 27 species were captured, and 190 individuals were recaptured, representing a 35% recapture rate. We found that body size negatively influenced butterfly abundance. In contrast, body size was positively associated with dispersal levels, distance traveled, number of traps visited, individual permanence, and diet breadth. These results indicate that larger butterflies have a greater proportion of dispersing individuals over longer distances, as they permanence were detected over longer periods than their smaller relatives. Moreover, larger butterflies are more generalized, based on the number of host plant families and genera they consume. Smaller butterflies demand fewer resources, which is reflected in their higher survival in small patches, and may explain their lower dispersal ability, and higher diet specialization. Nevertheless, lower dispersal ability, if not compensated by large population sizes, may threaten small-bodied species inhabiting environments with intense deforestation rates, such as the Cerrado. Conclusions: Body size positively influences dispersal and diet breadth in the fruit-feeding butterflies collected in this study.

The recent advancement of Artificial Intelligence (AI) and Machine Learning (ML) tools has had a pervasive effect on every sphere of human existence. Libraries and information centres are not immune to these technological advances. These disruptive technologies have transformed the traditional face of libraries and their operations. There are many available AI tools, for example, chatbots, literature review tools, plagiarism detection tools, and several other developments in the field that have perhaps made the functioning of libraries easier. However, there is growing concern and uncertainty among both traditional library and modern professionals regarding the possible job loss and security issues related to these new technologies, which the professionals must deal with. Librarians and information professionals must be ready to handle the unforeseen situation. However, it is to be observed how libraries cope with these uncertainties, and threats to the integrity, availability of data, and normal library housekeeping functions.

The body's billions of bacteria—the microbiota—influence health and illness. The microbiome—the genetic material of these microbes—affects digestion, immunological function, and mental health. Due to bacterial variety, complicated relationships, and research technique constraints, investigating the microbiota is difficult. AI and machine learning have expanded microbiome research. Artificial intelligence (AI) systems can efficiently scan large microbiome datasets to help researchers understand microbial populations and their functions. AI-based predictive algorithms can analyze food impacts on microbial communities and forecast illness risks based on gut microbiota composition. AI helps find microbial biomarkers linked to specific health disorders, enabling early illness identification and targeted therapy. AI-driven medication development platforms also modulate microbiome to treat microbiota-related diseases. Understanding microbial populations' involvement in health and illness requires understanding complicated microbial interactions within the microbiome and between it and the host. AI systems improve human health by comprehending these complex relationships. AI in microbiota study must overcome various obstacles. Data quality and AI model interpretability are essential for accurate findings. Diverse and representative datasets avoid biases and strengthen AI-driven conclusions. AI in microbiota research requires data protection, informed permission, and appropriate use of sensitive data. AI is improving our understanding of the human microbiome and its health effects, changing microbiota research. Overcoming difficulties and adhering to ethical norms will enable the proper use of AI-driven findings, leading to microbiome insights and precision medical breakthroughs.

Globally, an approximate of 380,000 patients succumbed to death due to the pandemic COVID-19 which also infected more than six million people since December 2019. Not sparing anyone, COVID-19 infections are widely reported among healthcare professionals, sanitation workers and researchers too while global leaders and various governments are providing their best in defending their citizens against this airborne and contact spread virus. In order to contain the virus and protect millions of lives from this deadly coronavirus, there is a need to have a combination of advanced engineering technology and medical facilities. Application of applied science, engineering and technology diffuse almost every aspect of contemporary living. Grasping the fundamentals to determine humanity's most imperative and forthcoming challenges is essential. Artificial Intelligence, the technology that learns, adapts and reciprocates the actions according to the situations, finds optimum position in the fight against corona virus and acts as a powerful tool against this pandemic. In this research article, the authors discusses how Artificial Intelligence (AI) can be leveraged to fight the deadly virus. The research paper further discusses the efficient utilization of AI across the globe to help in testing, treating and serving the population in these hard times. This manuscript focuses on the potential impact of the process in which AI can be implemented to prevent, test and treat.

Intimate partner violence (IPV), particularly sexual and emotional violence, against Black mothers who acquire human immunodeficiency virus (HIV) during childbearing age is a significant health and social concern worldwide requiring targeted interventions and precautions. IPV against women increases the chances of early mixed feeding, putting infants at high risk of mother-to-child transmission of HIV and increased infant morbidities. Although violence complicates many Black mothers’ lives, there is limited research evidence about the critical intersections of violence, HIV, and Black motherhood. Women's fears associated with IPV make them less likely to disclose their positive HIV status to their partners which subsequently prevents them from using the recommended guidelines for safe infant feeding practices. This review aims to explore the critical intersections between IPV and HIV and the impact of both on the infant feeding practices of Black mothers living with HIV. Furthermore, the theme of IPV and how it overlaps with other factors such as HIV-positive status and gender dynamics to compromise the Black motherhood experience are the focus of this narrative review of existing literature. Understanding the intersection of IPV and other factors influencing infant feeding practices among women living with HIV will help inform programming and policy interventions for HIV-positive Black mothers who may be experiencing IPV during the perinatal period.

There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.

Smallholder dairying engages around 70 to 80 million rural households who are mostly landless, small and marginal farmers. These farmers face challenges in terms of adoption of balanced feeding practices including inclusion of compound feed in animal feeding. This necessitates to understand the socio-economic factors that are influencing the adoption of compound feed. The study found that 51% of dairying households adopted compound feed in animal feeding. The adopters and non-adopters of compound feed differed (P< 0.01) in terms of land holding, family educational status, breeds owned, membership status in farmer’s collectives, fodder adoption, area under fodder, access to grazing resources, availability of crop residues, milk production and milk marketing opportunities. Furthermore, users and non-users of compound feed had significant difference (P< 0.05) in extension agency contact, mass media contact, attitude towards dairying and gender participation in farming. The logit regression model explained the variance in adoption to 30%. Further it indicated the role of selective socio-economic characters, farming situations, feeding and marketing practices in adoption of compound feed. In specific membership in farmer’s collectives, marketing opportunity, milk production, farm men participation, family education status, grazing resources and attitude towards dairying has greatly influenced the adoption behaviour of compound feed in positive direction. Added, households with high experience, cultivating fodder and having access to crop residues tend to influence the adoption decision negatively. The research observed that farmers tend to use compound feed for increasing productivity in areas of better markets and there exists trade-offs between milk market opportunities, access to cheaper roughages and usage of compound feed.

To study the mechanism of light color on a sea urchin (Tripneustes gratilla), blue light (B, λ450nm), yellow light (Y, λ585-590nm), red light (R, λ640nm), green light (G, λ510nm), white light (W, λ400-780nm) and darkness groups (H) were set up in a seawater recirculating aquaculture system. Six different LED light color treatment groups with a photoperiod of 12 L:12 D were tested for 30 d to investigate the effects of different light colors on the feeding, growth and enzyme activities of T. gratilla (142.45±4.36 g). We found that varying LED light colors significantly impacts the feeding, growth, and enzyme activity of T. gratilla. Notably, the sea urchins in group B exhibited better growth with a weight gain rate of 39.26%, while those in group R demonstrated poorer growth with a weight gain rate of only 26%. The feeding differed significantly (P&lt;0.01) between groups B and R, with group B consuming the highest daily intake (6.03±1.69 g) and group R consuming the lowest (4.54±1.26 g). Throughout the three phases, there was no significant change in the viability of the AMS (P&gt;0.05). Conversely, pepsin viability significantly increased (P&lt;0.01) in group B. LPS viability remained consistently at the lowest level, with no notable differences in group W from those observed in group B. In group R, both AMS and pepsin viability remained lower, whereas lipase viability was noticeably higher in each phase compared to group B (P&lt;0.01). Regarding antioxidant enzymes, group R exhibited a trend of initially increasing and then decreasing CAT, SOD, and GSH-PX activities, particularly during the third stage (15-30 d), with a significant decrease observed in antioxidant enzyme activities (P&lt;0.01). The outcomes suggest that blue light positively affects the growth, feeding, digestion, and antioxidant capacity of T. gratilla in comparison to other light environments, whereas the red light has an inhibitory impact.

The success of any tsetse control programs depends on the knowledge of their behaviour. This study assessed the host choice and feeding behaviours of Glossina morsitans’ siblings whose parents were bloodfed on Rabbits, Guinea pigs, Rodents and Squirrels. Individual host was placed in a screen cage which allowed flies to enter through openings on each side. The groups of flies (20 per replicate) colour-marked differently basing on their parents’ bloodmeal hosts, were released from the centre of large semi-field cage. Released flies were aspirated after 24 hours, then, sorted basing on the location, feeding status and parents’ bloodmeal. A total of 213 flies (72.95% of the recovered) were attracted to the hosts. The number of flies attracted to different hosts varied significantly (χ²4= 33.685, p= 0.0001); Rodent (n=80, p=0.006), Rabbit (n=59, p=0.331), Guinea pig (n=49, p=0.057) and squirrel (n=25, p=0.005). The number of flies attracted to their parent’s blood meal source varied significantly (χ²12 = 56.476, p&lt;0.001); rabbits (n= 35, 59.32%, p&lt;0.001), rodent (n=25, 31.25%, p=0.043) and guinea pig (n= 19, 38.78%, p=0.45). But, only 39 flies (18.31% of total attracted) bloodfed on the hosts; Guinea pigs (n=10, 25.64%), Rodents (n=23, 58.97%), Rabbits (n=6, 15.38%) and Squirrels (n=0,0.0%). There was significant variation in number of flies that fed successively across hosts (χ²4=49.478, p&lt;0.001). The findings from this study confirms the presence of the hosts’ differential attractiveness to flies but failed to explain observed behaviours with reference to genetic inheritance. Therefore, the study attracts the need for detailed investigation on the influence of bloodmeal sources on tsetsefly siblings’ behaviours across filial generations using small mammals.

Changes in food quality can dramatically impair zooplankton fitness, especially in eutrophic water bodies where cyanobacteria are usually predominant. Cyanobacteria are considered a food with low nutritional value, besides some species can produce bioactive secondary metabolites reported as toxic to zooplankton. Considering that cyanobacteria can limit survival, growth and reproduction of zooplankton, we hypothesized that the adverse effects of the dietary exposure of Daphnia species to saxitoxin-producing cyanobacteria depend on the relative abundance of nutitious food source in the diet. Life table experiments were conducted with different diets: 1) without nutritional restriction, where neonates were fed with diets at a constant green algae (Monoraphidium capricornutum) biomass as a nutritious food source, and increasing cyanobacterial concentration (STX-producing Raphidiopsis raciborskii), and 2) with diets consisting of different proportions of M. capricornutum (nutritious) and R. raciborskii (poor food) at a total biomass 1.0 mg C L 1. In general, the presence of high proportions of cyanobacteria promoted a decrease in Daphnia somatic growth, reproduction and intrinsic rate of population increase (r) in both diets with more pronounced effects in the nutritionally restricted diet (90% R. raciborskii). Two-way ANOVA revealed significant effects of species/clone and treatments in both assays, with significant interaction between those factors only in the second assay. Regarding the grazing assay, only D. laevis was negatively affected by increased cyanobacterial proportions in the diet. In the life table assay with constant edible food, a reduction in the reproduction parameters and the intrinsic rate of population increase (r) of all species was observed, In conclusion, we found adverse effects of the toxic cyanobacterial strain R. raciborskii on Daphnia fitness, regardless of the constant amount of nutritious food available, proving the toxic effect of R. raciborskii and the nutritional quality of the food has a greater influence on the fitness of these animals.

The chitin synthase B gene is a key enzyme in the chitin synthesis of insect peritrophic matrix (PM), affecting insects' feeding behavior. The chitin synthase B gene has been cloned from the transcription library of Heortia vitessoides Moore. RT-qPCR showed that HvChsb was highly expressed in the larval stage of H. vitessoides, especially on the first day of the prepupal stage, and in the midgut of larvae and the abdomen of adults. After starvation treatment, HvChsb was found to be significantly inhibited with time. After 48 h of starvation, the feeding experiment showed that HvChsb increased with the prolongation of the refeeding time. The experimental data showed that feeding affected the expression of HvChsb. HvChsb was effectively silenced by RNA interference, so its function was lost, significantly decreasing the survival rate of H. vitessoides. The survival rate from larval-to-pupal stages was only 43.33%, accompanied by abnormal phenotypes. It can be seen that HvChsb plays a key role in the average growth and development of H. vitessoides.

We hypothesized that rumen fluid with yeast producing cellulase enzyme can occur and also produces a high biomass compared to S. cerevisiae. Therefore, the aim of this study was to screen and isolate yeast from rumen fluids with an experimental design method. We optimized a fermentation medium containing sugarcane molasses as a carbon source and urea as a nitrogen source to measure the efficiency of biomass production and cellulase activity. Two fistulated-crossbred Holstein Friesian steers, averaging 350 ± 20 kg body weight, were used to screen and isolate ruminal yeast. The two experiments were designed. A 12 × 3 × 3 factorial was used in a completely randomized design to determine biomass and carboxymethyl cellulase activity. Factor A was isolated yeasts and S. cerevisiae. Factor B was sugarcane molasses (M) concentration. Factor C was urea (U) concentration. Potential yeast was selected for identified and analyzed as a 4 × 3 factorial use in a completely randomized design including. Factor A was incubation times. Factor B was isolated yeast strains including code H-KKU20 (as P. kudriavzevii-KKU20), I-KKU20 (C. tropicalis-KKU20), and C-KKU20 (as Galactomyces sp.-KKU20). Isolation was under aerobic conditions, resulting in a total of 11 different colonies. We noted two appearances of colonies including, asymmetric colonies of isolated yeast (indicated as A, B, C, E, and J) and ovoid colonies (coded as D, F, G, H, I, and K). The highest biomass was observed in three yeasts including codes H, I, and C-KKU20 when inoculated in 25% molasses with 1% urea (M25+U1) (p <0.01). The highest CMCase activity was observed in yeast code H-KKU20 when inoculated in all media solutions (p <0.01). Ruminal yeasts strains H-KKU20, I-KKU20, and C-KKU20 were selected for their ability to produce biomass and their CMCase enzyme synthesis. Identification of isolates H-KKU20 and I-KKU20 revealed that those isolates belonged to Pichia kudriavzevii-KKU20 and Candida tropicalis-KKU20, while C-KKU20 was identified as Galactomyces sp.-KKU20. Two strains provided maximum cell growth: P. kudriavzevii-KKU20 (9.78 and 10.02 Log cell/ml) and C. tropicalis-KKU20 (9.53 and 9.6 Log cells/ml) at 60 and 72 h of incubation time, respectively. The highest ethanol production was observed in S. cerevisiae: 76.4, 77.8, 78.5, and 78.6 g/L at 36, 48, 60, and 72 h of incubation time, respectively (p <0.01). The P. kudriavzevii-KKU20 yielded the least reducing sugar about 30.6 and 29.8 g/L at 60 and 72 h of incubation time, respectively. It could be concluded that screening and isolating yeast from rumen fluids resulted in 11 different characteristics of yeasts. The first novel yeasts discovered in the rumen fluid of cattle were Pichia kudriavzevii-KKU20, Candida tropicalis-KKU20, and Galactomyces sp.- KKU20. P. kudriavzevii-KKU20 had higher results than the other yeasts in terms of biomass production, cellulase enzyme activity, and cell number.