Aims: Anxiety, major depressive disorder and accelerated cognitive decline frequently complicate traumatic brain injury. Obesity and type 2 diabetes mellitus drive peripheral inflammation which can accelerate traumatic brain injury-associated neurodegeneration. The Zucker rat harbors G-protein coupled receptor agonist IgG autoantibodies and in vitro neurotoxicity caused by these autoantibodies was prevented by a novel synthetic fragment of the serotonin 2A receptor. The aim of the present study was to test whether genetic obesity manifested in Zucker diabetic fatty rat is associated with anxiety, depression and spatial memory impairment induced by mild traumatic brain injury. Furthermore, we investigated whether these neurobehavioral or neurodegenerative complications can be lessened by administration of a novel putative neuroprotective peptide. Methods: Age-matched lean and fatty diabetic Zucker rats were tested in the sucrose preference test (anhedonia), open field test in bright light (anxiety measure) and the Morris water maze (spatial memory) prior to receiving a sham-injury or lateral fluid percussion (LFP) mild traumatic brain injury. Behavioral testing was repeated at 1-week, 1-month, and 3-month intervals following injury. A synthetic peptide consisting of a portion of the 5- hydroxytryptamine (serotonin) 2A receptor (2 mg/kg) (or vehicle) was administered via intraperitoneal route every other day for 7 days after sham or LFP injury to lean rats or 7 days before and after sham or LFP injury to fatty rats. Results: Sucrose preference decreased transiently (one week after mild traumatic brain injury) in Zucker lean rats indicative of anhedonia. Baseline anxiety-like behavior (bright light, open field test) was lower in fatty vs. lean Zucker rats, and anxiety behavior increased significantly in fatty rats but not lean rats following mild traumatic brain injury. Mild traumatic brain injury impaired recall of spatial memory in fatty and lean rats, and the effect was longer-lasting in fatty rats. A synthetic peptide fragment of the 5-hydroxytryptamine 2A receptor enhanced recall of spatial learning one-week after sham injury in Zucker rats and it may have prevented ‘anhedonia’ one-week after TBI (LFP) in a subset of Zucker fatty and lean rats expressing near-normal baseline sucrose preference. Mean plasma 5-hydroxytryptamine 2A receptor autoantibody level (determined 2 weeks after injury) was 2.5-fold higher than background, but did not differ significantly in (sham- vs TBI) Zucker lean rats. Conclusions: These are the first data to suggest reduced baseline anxiety-like behavior in Zucker fatty (vs. lean) rats. The pronounced increases in anxiety , and spatial memory impairment experienced by Zucker fatty (vs lean) rats following mild traumatic brain injury may have accounted in part for absence of a 5-hydroxytryptamine 2A receptor peptide neuroprotective effect in brain- injured Zucker fatty rats.

Extensive post reproductive lifespan (PRLS) is observed only in a few species, such as humans or resident killer whales, and its origin is under debate. Hypotheses like mother-care and grandmother-care invoke strategies of investment—provision to one’s descendants to enhance one’s overall reproductive success—to explain PRLS. The contribution of an investment strategy varies with the age of the caregiver, as the number of care-receiving descendant changes with age. Here we simulated an agent based model, which is sensitive to age-specific selection, to examine how the investment strategies in different hypotheses affect survival and reproduction across different stages of life. We found that extensive PRLS emerges if we combine multiple investment strategies, including grandmother-care but not mother-care, which allow an individual to have an increasing contribution as it ages. We also found that, if mother-care is further introduced to the PRLS-enabling strategies, it will let contribution at mid-life to substitute contribution at late life, which consequently terminates extensive PRLS.

Ctenophores are marine organisms attracting significant attention from evolutionary, molecular biology and ecological research. Here we describe an easy and affordable set-up to maintain a stable culture of the ctenophore Mnemiopsis leidyi. The challenging delicacy of the lobate ctenophores can be met by monitoring the water quality, providing the right nutrition, and adapting the handling and tank set-up to their fragile gelatinous body plan. Following this protocol allows stable laboratory lines, a continuous supply of embryos for molecular biological studies, and independence from population responses to environmental fluctuations.

We show that the accelerating expansion phase of the universe can emerge from the group field theory formalism, a candidate theory of quantum gravity. The cosmological evolution can be extracted from condensate states using mean field approximation, in a form of modified FLRW equations. By introducing an effective equation of state w, we can reveal the relevant features of the evolution, and show that with proper choice of parameters, w will approach to −1, corresponds to the behaviour of cosmological constant, results in a late time acceleration and leads to de Sitter spacetime asymptotically.

Background: Only few studies have studied the link between risk perception and sleep in the context of the COVID-19 pandemic. This study investigates the effect of two distinct risk appraisals—risk perception and perception of collective coordinated defense (PCCD) on Chinese adults’ sleep quality during the COVID-19 pandemic, and tested COVID-19-related fear and rumination as potential mediators of the relationships. Methods: Data were collected using a self-report online questionnaire from a sample of 224 Chinese adults during the fourth wave of the COVID-19 pandemic in Hong Kong. Results: COVID-19 risk perception and PCCD were related to poor sleep quality. Mediation analysis showed that both fear and rumination mediated the relationship between risk perception and sleep quality, whereas only fear mediated the relationship between PCCD and sleep quality. The model showed an excellent fit to the data and accounted for 44% of the variance in sleep quality in Chinese adults. Conclusions: This study demonstrates the distinct perceptual processes—risk appraisals in particular—contributed to poor sleep quality in Chinese adults during the COVID-19 public emergencies. These findings would be helpful for policy makers to address the sleep problems induced by psychological consequences of the pandemic.

Many lifestyle and occupational habits negatively impact on physical and mental health, increasing the risk of cardiovascular and other diseases. While larger institutions fulfill occupational health requirements to ensure their employees’ wellbeing at work, smaller business workers and entrepreneurs may neglect initial signs of stress or physical decline, putting themselves at increased risk. Early detection of compromised cardiopulmonary health or fitness has the potential to flag up indications for early interventions and reduce the risk of disease onset and progression. This case study highlights the usefulness of respiratory muscle training (RMT) in improving pulmonary and cardiovascular parameters as an early countermeasure to cardiopulmonary decline. We suggest RMT as an easily accessible, cost- and time effective opportunity to support a healthy lifestyle.

The aim of this study was to evaluate the Hapo ms, a passive upper limbs exoskeleton developed to assist workers for tasks with arms in front of the body. Twelve participants had to perform a static task, a manual handling task and a load carrying task two times: with and without the exoskeleton. In all cases subjective (perceived effort in arm and back areas, comfort) and objective (muscular activity, postural balance) criteria were evaluated. Results have shown a decrease in anterior deltoid (-12 to -18% depending of the task) and in biceps brachii (-19% to -33% depending of the task) muscular activity. No significant difference was pointed out in back muscle and postural balance was not significantly perturbed due to the wear of the exoskeleton. Finally, perceived effort reduction was observed during the three tasks (except in back area for task 1). To conclude, the Hapo ms seems well adapted to assist upper arms during tasks with arms in front of the body.

The growing interest in fair AI development is evident. The ''Leave No One Behind'' initiative urges us to address multiple and intersecting forms of inequality in accessing services, resources, and opportunities, emphasising the significance of fairness in AI. This is particularly relevant as an increasing number of AI tools are applied to decision-making processes, such as resource allocation and service scheme development, across various sectors such as health, energy, and housing. Therefore, exploring joint inequalities in these sectors is significant and valuable for thoroughly understanding overall inequality and unfairness. This research introduces an innovative approach to quantify cross-sectoral intersecting discrepancies among user-defined groups using latent class analysis. These discrepancies can be used to approximate inequality and provide valuable insights to fairness issues. We validate our approach using both proprietary and public datasets, including EVENS and Census 2021 (England \& Wales) datasets, to examine cross-sectoral intersecting discrepancies among different ethnic groups. We also verify the reliability of the quantified discrepancy by conducting a correlation analysis with a government public metric. Our findings reveal significant discrepancies between minority ethnic groups, highlighting the need for targeted interventions in real-world AI applications. Additionally, we demonstrate how the proposed approach can be used to provide insights into the fairness of machine learning.

In the context of the new crown epidemic, remote physical training will already be in order under the guidance of the Ministry of Education. There is a global focus on current lifestyle behaviors and the future health and well-being of youth. Youth physical activity, aerobic activity and muscle fitness levels are declining.[1] Research on the effects of youth physical training and group attitudes is still in its infancy. This study conducted in-depth interviews with online physical training students, parents, teachers involved in the construction of online physical training, and sports practitioners through interview methods, documentation, and mathematical statistics. The results of the interviews were qualitatively coded using NVivo to summarize the attitudes of different subjects toward physical fitness training and the influencing factors, (1) students were basically satisfied with the teaching effect of online physical fitness training (2) parents thought the teaching effect of online physical fitness training was average (3) physical education teachers thought the effect of online physical fitness training was average (4) physical education practitioners thought the effect of online teaching was poor.

Background: Micronutrients has roles in strengthening and maintaining immune function, but its supplementation and/or deficiency effects on respiratory tract infections are inconclusive. This review aims to systematically assess the associations between micronutrient supplementation or deficiency, with novel coronavirus incidence and disease severity. Methods: Systematic literature searches conducted in 5 electronic databases identified 751 unique studies, of which 33 studies (5 supplementation studies, 1 supplementation and deficiency study, and 27 deficiency studies) were eventually included in this review. Proportions of incidence and severity outcomes in each group, and adjusted summary statistics with their relevant 95% confidence intervaIs (CI) were extracted. Data from 19 studies were pooled in meta-analysis using the generic inverse variance method. Findings: A total of 360,346 patients across 16 countries, with a mean age between 32 and 87.7 years, were involved across 33 studies. All studies were on COVID-19 infections. In individuals without micronutrient deficiency, there was a significant reduction on odds of COVID-19 incidence (pooled OR: 0.37, 95% CI: 0.18, 0.78), and ICU admissions or severe/critical disease onset (pooled OR: 0.26, 95% CI: 0.08, 0.89). Insignificant protective effects were observed on other outcome measures – mortality, ICU admission, progression to respiratory-related complications, severe/critical disease onset or requiring respiratory support and hospitalization rate. Conclusion: The absence of micronutrient deficiency significantly reduced COVID-19 incidence and clinical deterioration in hospitalized patients. Usage of micronutrients as prophylaxis and complementary supplement in therapeutic management of COVID-19 patients may be a promising and cost-effective approach warranting in-depth investigation.

The sustainable development of mines has been the focus of attention in recent years, in China, there are a large number of untreated mined-out areas, and a series of disasters caused by instability of the goaf will bring heavy blows to people’s safety and financial resources. Mined-out areas management is an important part that cannot be ignored in mine sustainable development, and filling management is one of the most effective ways to solve the hidden danger of mined-out areas. Among them, cemented filling with tailings and the ultra-high-water backfilling are commonly used in mining enterprises. However, filling treatment will lead to increasing cost and the decreasing profits, which seriously depresses the motivation of mining enterprises and even leads to mor-al hazard. Therefore, the analysis of the economic benefits of goaf control plays a vital role in the sustainable construction and long-term development of mines. This paper proposed the mine-out area treatment economic model, the proposed method employs the guiding philosophy of news-boy model to create a mathematical economy model that provides the basis of goaf management mode for mine. The following research results are achieved: (1) The economic model of mined-out area backfilling treatment is constructed, and which is classified three different modes. (2) Com-bined with mathematical derivation and simulation, the influence of relevant variable parameters on each type of filling mode is discussed. (3) Various types of goaf filling treatment mode are com-pared with non-filling scheme (benchmark mode), so as to provide theoretical support for mining enterprises to choose the appropriate filling scheme. The results shows that the economic model of mined-out area management provides the optimal mode for mined-out area filling, the balance of tailings and ultra-high water filling materials procurement is realized, to making maximum profits. In this paper, the use of economic thinking has an important impact on the sustainable development of safety goaf management. To evaluate the sustainable development of goaf management by building economic models. It has important theoretical basis and practical guiding significance for the development and exploration of mined-out area management by combining economy with sustainable development.

We find that the standard relative entropy and the Umegaki entropy are designed for the purpose of inferentially updating probability and density matrices respectively. From the same set of inferentially guided design criteria, both of the previously stated entropies are derived in parallel. This formulates a quantum maximum entropy method for the purpose of inferring density matrices in the absence of complete information.

A recent study found that a 3ppb increase in O₃ ambient concentration was associated with an increased progression of 0.18 percentage points in percent emphysema and that such increase was equal to smoking 20 cigarettes per day for 29 years. A simple estimation of population attributable fraction shows that COPD diagnoses due to smoking are actually 30 times more than those attributed to a 3 ppb increment in O₃ concentration. Labelling ozone pollution as the new smoking may distort perception of the risks and hinder proper response to real life threatening risk such as smoking.

Current therapeutic strategies targeting cancer cells within solid tumors have displayed limited success owing to the presence of non-cancer components referred to as the tumor stroma within the tumor microenvironment (TM). These stromal cells, extracellular matrix and blood vessels influence cancer cell response to therapy and play key roles in tumor relapse and resistance. Of the stromal cells present in the TM, a lot of attention has been given to cancer-associated fibroblasts (CAFs) as they are the most abundant and are important in cancer initiation, progression and therapy resistance. In this updated review I emphasize the role of CAFs in the regulation of tumor cell behaviour and reveal how CAF-derived factors and signaling influence tumor cell heterogeneity and development of novel strategies to combat cancer. To investigate the expression of CAF markers in tumor tissues versus normal tissues, transcriptomic data from The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) databases was used. Bioinformatic analysis reveals differential expression of CAF markers in several cancer types, underscoring the need for further multiomics and biochemical studies on CAFs, CAF subsets and markers. Differences in CAF markers’ expression could be due to different cellular origins as well as the effect of cancer-specific tumor microenvironmental effect on CAFs. Lastly, I present recent advances in therapeutic targeting of CAFs and the success of such endeavours or its lack thereof. It is recommended that for patients’ outcomes to improve, cancer treatment be combinatorial in nature, targeting both cancer cells and stromal cells and interactions.

: Extra- and intra-cellular activity occurs under the direction of numerous inter-molecular interactions, and in any tissue or cell, molecules are densely packed, thus promoting those molecular interactions. Galectins and chemokines, the focus of this review, are small, protein effector molecules that mediate various cellular functions, in particular cell adhesion and migration, as well as cell signaling/activation. In the past, researchers have reported that combinations of these (and other) effector molecules act separately, yet sometimes in concert, but nevertheless physically apart and via their individual cell receptors. This view that each effector molecule functions independently of the other limits our thinking about functional versatility and cooperation, and, in turn, ignores the prospect of physiologically important inter-molecular interactions, especially when both molecules are present or co-expressed in the same cellular environment. This review is focused on such protein-protein interactions with chemokines and galectins, their homo- and hetero-oligomeric structures that they can form, and the functional consequences of those paired interactions.

Many cellular functions important for tumor initiation and progression are mediated by members of the integrin family, a diverse family of cell adhesion receptors. With recent studies emphasising on the role of the tumor microenvironment (TME) in tumor initiation and progression, it is not surprising that a lot of attention is being given to integrins. Several integrins are under trial with many demonstrating appealing activity in patients with different cancers. A deeper knowledge of the functions of integrins within the tumor microenvironment is still required, and might lead to better inhibitors being discovered. Integrin expression is commonly dysregulated in many tumors with integrins playing key roles in signaling as well as promotion of tumor cell invasion and migration. This review report new data on the differential expression of integrins within solid tumors using two publicly available resources: The Cancer Genomic Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA). In this analysis, I investigated the expression of integrin alpha 2 (ITGα2), ITGα3, ITGβ4 and ITGβ6 in tumor tissues versus adjacent normal tissues. This analysis showed that integrins were differentially expressed in cervical squamous cell carcinoma (CESC), head and neck squamous cell carcinoma (HNSC), esophageal carcinoma (ESCA) and lung adenocarcinoma (LUAD). This analysis showed that ITGα2 and ITGβ6 expression are upregulated in CESC and ESCA, ITGα3 is upregulated in HNSC and ESCA whilst ITGβ4 is highly expressed in CESC, HNSC, ESCA and LUAD tumor tissues compared to adjacent normal tissues. Integrins also play a major role in adhesion of circulating tumor cells to new sites and the resulting formation of secondary tumors. Furthermore, integrins have demonstrated the ability to promoting stem cell-like properties in tumor cells as well as drug resistance. Anti-integrin therapies rely heavily on the doses or concentrations used as these determine whether the drugs act as antagonists or as integrin agonists. This review offers the latest synthesis in terms of current knowledge of integrins functions within the tumor microenvironment and potential targets for different cancers.

For screening excellent lactic acid bacteria (LAB) strains to inhibit Escherichia (E.) coli (ETEC) K88, inhibitory activities of more than 1100 LAB strains isolated from different materials and kept in the lab were evaluated in this study. Nine strains with inhibition zone at least 22.00 mm (including that of hole puncher 10.00 mm) and good physiological and biochemical characteristics identified by 16S DNA gene sequencing and recA gene multiple detection, were assigned to Lactobacillus (L.) plantarum subsp. plantarum (5), L. fermentum (1), L. reuteri (1), W. cibaria (1) and E. faecalis (1), respectively. As investigated for their tolerance abilities and safety, only strain ZA3 possessed high hydrophobicity and auto-aggregation abilities, had high survival rate in low pH, bile salt environment and GI fluids, sensitive to ampicillin, resistant to norfloxacin and amikacin, without hemolytic activity and didn’t carry antibiotic resistance genes, exhibited broad spectrum activity against a wide range of microorganisms, and antibacterial substance may attribute to organic acids, especially lactic acid and acetic acid. The results indicated that the selected strain L. plantarum subsp. plantarum ZA3 could be considered a potential probiotic to inhibit ETEC K88 for further research.

Early reports demonstrated the presence of cells with stem-like properties in bone marrow, with these cells having both hematopoietic and mesenchymal lineages. Over the years, various investigations have purified and characterized mesenchymal stromal/stem cells (MSCs) from different human tissues as cells with multi-lineage differentiation potential under the appropriate conditions. Due to their appealing characteristics and potential, MSCs are leveraged in many applications including medicine, oncology, bioprinting and as recent as treatment of COVID-19. To date, reports indicate mesenchymal stromal/stem cells have varied differentiation capabilities into different cell types and demonstrate immunomodulating and anti-inflammatory properties. Reports indicate that different MSCs microenvironments or niche and the resulting heterogeneity may influence their behavior and differentiation capacity. The potential clinical applications of mesenchymal stromal/stem cells have led to an avalanche of research reports on their properties and hundreds of clinical trials being undertaken. The future looks bright and promising for mesenchymal stem cell research with many clinical trials under way to prove their utility in many applications and in the clinic. This report provides an update on the potential broader use of mesenchymal stromal/stem cells, review early observations of the presence of these cells in the bone marrow and their magnificent differentiation capabilities and immunomodulation.

This paper defines the two main causes of global warming in the UK and seeks to cut climate change using green hydrogen made from renewable electricity sources. Combustion vehicles powered by fossil gasoline and diesel emit a third of UK climate-warming CO2 while buildings heated by natural gas provide a quarter. First, current UK grid problems are defined: Electricity grid, gas grid and petroleum grid. Then experiments on the private energy community of Keele University allow electrolysis demonstrations producing green hydrogen for buildings and vehicle refueling. Next, the model supply chain is planned and tested. Finally, experiments and calculations are outlined, analyzing the optimum system design criteria proposed. We conclude that economic green hydrogen can displace diesel and gasoline in vehicle transport, while also displacing natural gas for combined heat and power in buildings. In addition, the prospect in 2023 is that profits can now be made all along the green hydrogen supply chain, such that new businesses involved in clean communities can beat the National Grid monopoly and other large dominant fossil grid companies.

Iron oxide nanozymes are a form of nanomaterial with both superparamagnetic and enzyme-mimicking properties. Given the multifunctional nature of iron oxide nanozymes, it is attractive for creating iron oxide hybrid nanozymes through biomolecular modifications to imbue with auxillary properties. Such iron oxide hybrid nanozymes can be useful for rapid and cost-effective analysis of circulating tumor nucleic acids (ctNAs) in patient liquid biopsies during minimal residual disease (MRD) monitoring of cancer recurrence. Herein, the use of streptavidin-modified iron oxide hybrid nanozymes is reported for magnetic enrichment and bioelectrocatalytic sensing of three prostate cancer (PCa) ctRNA biomarkers with high detection specificity and sensitivity (10 copies) over an ultrabroad dynamic range (five orders of magnitude). Furthermore, the feasibility of ctRNA analysis for pre- and post-cancer treatment MRD monitoring is demonstrated using PCa urinary liquid biopsy samples.

Human-Computer Interaction, including technology-aided instruction, is beginning to focus on virtual reality (VR) technology due to its ability to support immersive learning, teaching through simulation, and gamification of learning. These systems can deliver high-level multisensory learning experiences that are important in the teaching of many subjects, especially those involving abstract concepts or requiring spatial skills, such as organic chemistry. Haptic experiences with VR, however, remain a challenge. In addition, development have focused on general entertainment/gaming; VR systems in chemistry implement simulations of the chemistry laboratory and other advanced systems whereas those that support safe, game-like, immersive and multisensory learning of organic chemistry with haptics at pre-university education levels are scarce. We developed the VR Multisensory Classroom (VRMC) as an immersive learning environment within a VR head-mounted display, where learners employ hand movements to build hydrocarbon molecules and experience haptic feedback through gloves with built-in sensors and hand-tracking with the Leap Motion system. We report here the evaluation of the first prototype by learners from diverse backgrounds who reported on the ability of the VRMC to support high engagement, motivation, interest and organic chemistry learning as well as diverse learning styles. The VRMC is a novel VR classroom that supports immersive learning in molecular organic chemistry with haptics for multisensory learning.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90.75%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Abstract:The ecological protection of cultivated land is crucial for advancing high-quality agricultural development. This study analyzes the impact of agricultural socialization services on the ecological conservation of farmland, focusing on the reduction of chemical fertilizers and pesticides among rice farmers in the Jianghan Plain area. Utilizing data from 743 farmer household surveys conducted in 2023, the findings reveal that agricultural socialization services significantly encourage farmers to reduce chemical input usage. Specifically, a 1% increase in the level of agricultural socialization services correlates with a 14% decrease in chemical fertilizers and a 16.4% decrease in pesticides per mu. The study identifies scale operation, factor substitution, and alleviation of capital constraints as mediating factors enhancing the efficacy of these services. Furthermore, heterogeneity analysis indicates that these services are more effective in reducing chemical inputs among large-scale and newer-generation farmers compared to their small-scale and older counterparts. Additionally, technology-intensive socialized services exhibit a stronger impact on reducing chemical inputs than labor-intensive services.

To improve the interfacial compatibility between cement matrix and expanded polystyrene (EPS) in the core-shell lightweight aggregates (CSLA), the effects of sodium silicate, Polyvinyl alcohol (PVA) emulsion, Vinyl acetate-ethylene (VAE) emulsion, acrylic acid, and acetic acid on the cement-EPS interface were investigated. The denseness of the interface was studied by scanning electron microscopy (SEM), and the effect of interfacial agents on the hydration process of cement was studied by the heat of hydration and induction resistivity. The macroscopic properties of the interface of the CSLA were characterized by the "leak-white" rate, drop resistance and numerical crushing strength. The results show that the sodium silicate densifies the interface by generating hydration products on the EPS surface. At the same time, organic acid enhances the interfacial properties of EPS and cement by increasing the surface roughness and allowing hydration products to grow in the surface micropores. In terms of the cement hydration process, both two interfacial agents delay the cement hydration. Above all, with comprehensive interface properties, "leak-white" rate, and mechanical properties, VAE emulsion and sodium silicate can achieve the best performance with a final crushing resistance of 5.7 MPa.

Novel high energy density materials N14 (1,6-dihydro-1,2,3,3a,4,5,5a,6,7,8,8a,9,10,10a-tetradecazapyrene) and N18 (1,2,2a,3,4,4a,5,6,6a,7,8,8a,9,10,10a,11,12a-octadecazacoronene) were designed, and their structures, detonation performance and stabilities were calculated employing density functional theory (DFT). Calculations reveals that they have a good balance between high energy and stability. Their energy gaps between LUMO and HOMO are all lower than that of TATB, while their impact sensitivity h_50% is estimated close to that of RDX. Concerning energy, detonation performance of the N14 (P = 43.6 GPa, D = 10040 m/s, Q = 2214 cal/g) and the N18 (P = 37.4 GPa, D = 9400 m/s, Q = 2114 cal/g) are comparable to CL-20.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

The rapid growth of network services, Internet of Things devices and online users on the Internet have led to an increase in the amount of data transmitted daily. As more and more information is stored and transmitted on the Internet, cybercriminals are trying to gain access to the information to achieve their goals, whether it is to sell it on the dark web or for other malicious intent. Through thorough literature study relating to the causes and issues that are brought from the security and privacy segment of wireless networks, it is observed that there are various factors that can cause the networks to be an insecure; especially factors that revolve around cybercriminals with their growing expertise and the lack of preparation and efforts to combat them by relevant bodies. The aim of this paper is to showcase major and frequent security as well as privacy issues in wireless networks along with specialized solutions that can assist the related organizations or the public to fathom how great of an impact these challenges can bring if every related stakeholder took a step in reducing them. Through this paper it is discovered that there are many ways these challenges can be mitigated, however, the lack of implementation of privacy and security solutions is still largely present due to the absence of practical application of these solutions by responsible parties in real world scenarios.

We examine the relation between environmental, social, and governance commitment levels (ESG) and firm dividend payer status. Given that larger and more profitable firms are positively associated with both payer status and ESG, it could be that ESG and dividends are complements. However, given that both dividends and ESG relate to firm spending decisions, it may be that the choice is “either/or” and that ESG and dividends are substitutes. We document a positive relation between ESG and dividend payer status in U.S. firms over the period 1991-2016. In particular we find that the proportion of dividend payers is roughly 13% higher for firms with positive ESG compared to those with negative ESG. Including ESG in the models used to predict payer status provides, on average, a nearly 26% improvement in relative forecast accuracy. Our results are robust to estimation techniques and the inclusion of variables known to be determinants of payer status.

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. The study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 keypoints using an open-source tool called DeepLabCut, which helped accurately mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the Long Short-Term Memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior were over 90%.

The theories of quantum mechanics and relativity dramatically altered our understanding of the universe ushering in the era of modern physics. Quantum theory deals with objects probabilistically at small scales, whereas relativity deals classically with motion in space and time. We show here that the mathematical structures of quantum theory and of relativity follow together from pure thought, defined and uniquely constrained by the same elementary ``combining and sequencing'' symmetries that underlie standard arithmetic and probability. The key is uncertainty, which inevitably accompanies observation of quantity and imposes the use of pairs of numbers. The symmetries then lead directly to the use of complex \sqrt{-1} arithmetic, the standard calculus of quantum mechanics, and the Lorentz transformations of relativistic spacetime. One dimension of time and three dimensions of space are thus derived as the profound and inevitable framework of physics.

The extracellular matrix (ECM) is a ubiquitous member of the body and is key to the maintenance of tissues and organs integrity. Initially thought to be a by-stander in many cellular processes, the extracellular matrix has been shown to have diverse components that regulate and activate many cellular processes and ultimately influence cell phenotype. Importantly, the ECM composition, architecture, and stiffness/elasticity influence cellular phenotypes. Under normal conditions and during development, the synthesized ECM is constantly undergoing degradation and remodeling processes via the action of matrix proteases that maintain tissue homeostasis. In many pathological conditions including fibrosis and cancer, the ECM synthesis, remodeling, and degradation is dysregulated causing its integrity to be altered. Both physical and chemical cues from the ECM are sensed via receptors including integrins and play key roles in driving cellular proliferation and differentiation and in various disease progression such as cancers. Advances in ‘omics’ technologies have seen an increase in studies focussing on bi-directional cell-matrix interactions and here we highlight emerging knowledge on the role played by the ECM during normal development and in pathological conditions. This review summarizes current ECM-targeted-therapies that can modify tumor ECM to overcome drug resistance and better cancer treatment.

More people around the world now have obesity than suffer from starvation thanks to our modern food system. Agriculture was transformed over the 20^th century by a variety of technological advancements that relied heavily on fossil fuels. In the United States of America, government policies and economic incentives led to surplus production of cheap inputs to processed food industries that produced a wide variety of heavily marketed, convenient, rewarding, timesaving, and relatively inexpensive ultra-processed foods. The energy available in the United States food supply increased much more than population needs, albeit with large inequities in food distribution and nutrition security. While most of the rise in per capita food availability during the late 20^th and early 21^st centuries in the United States resulted in increased food waste, a variety of mechanisms have been proposed by which changes in the increasingly ultra-processed food environment resulted in excess energy intake disproportionately in people genetically susceptible to obesity. As populations continue to grow, substantial investments in coordinated nutrition and agricultural research are needed to transform our current food system to one that relies less on fossil fuels, preserves biodiversity, ensures environmental health, and provides equitable access to affordable, safe, and nutritious food that reduces the prevalence of chronic diseases like obesity.

In recent years, with the increasing research and development of the LED industry which contains GaN, it is expected that there will be a large amount of related wastes in the future. Especially the gallium has extremely high value of economic, therefore, it is necessary to establish the recycling system of the GaN waste. However, GaN is a direct-gap semiconductor and with high energy gap, high hardness, and high melting point make it difficult to recycle. Therefore, this study will analyze the physical characteristics of LED wastes containing GaN and carry out various leaching method to leach the valuable metals from the waste optimally. Different acids are used to find out the best reagent for leaching the gallium. Different experimental parameters are discussed such as the effect of the different acid agents , concentration, pressure, solid-liquid mass ratio, temperature, and time which influence the leaching efficiency of the gallium. In this study, various leaching methods which effect the leaching efficiency of the gallium are compared and the advantages and disadvantages are discussed. Finally, pressurized acid leaching method is preferred to leach the GaN waste, and hydrochloric acid is used as the leaching solution because of its better leaching efficiency of gallium. Eventually, the leaching efficiency of the gallium can reach to 98%.

Based on its large surface area and covering the whole human body, the skin body’s largest organ and its main function is protection. Injuries and wound healing involving the skin offer valuable lessons shared with and of relevance to other organ systems and the diseases that impact them. Arguably the most complex human body process, wound healing is a multifaceted process that involves multiple cells and the extracellular matrix (ECM), with each component playing a specific role in the different stages of the healing process. Importantly, studies indicate that cells with stem cell-like properties are present within many of the human tissues and play key roles in case of tissue and cellular injury. Cell-to-cell and cell-to-ECM interactions are salient in wound healing subsequent to an injury. Microenvironment related factors and the variations therein including hypoxia or the abundance of oxygen, the presence/absence of growth factors and cytokines add to the complexity of the wound healing process and impact cell function and result in compromised or enhanced wound healing. This expert review critically examines the advances in biochemical and analytical tools that are enable the analysis of numerous cells and molecules within the wound microenvironment, revealing great cellular heterogeneity as well as novel molecular targets of importance to enhance wound healing. In a broader angle, we emphasize the ways in which wound healing is significant in the search for perfect skin after injury and in many common complex human diseases including cancer. In all, wound healing is a centrepiece of integrative biology research and applications in medicine as well as dermatology as discussed in this review.

Control charts have been widely used for monitoring process quality in manufacturing and play an important role in triggering a signal in time when detecting a change in process quality. Many control charts in literature assume that the in-control distribution of the univariate or multivariate process data is continuous and not categorical. This research develops two exponentially weighted moving average (EWMA) proportion control charts for monitoring a process with multinomial proportions when considering both large and small sample sizes. For a large sample size , the charting statistic depends on the well-known Pearson χ2 statistic, and the control limit of the EWMA proportion chart is determined by an asymptotical chi-square distribution. For a small sample size, we derive the exact mean and variance of the Pearson χ2 statistic. Hence, the exact EWMA proportion chart is determined. The proportion chart can also be applied to monitor the distribution-free continuous multivariate process as long as each categorical proportion associated with specification limits of each quality variable is known or estimated. Lastly, we investigate the detection performance of the proposed EWMA proportion chart by numerical analyses. Real data analysis demonstrates the beneficial application of the proposed EWMA proportion charts.

Celecoxib as adjuvant therapy has been shown in a small randomized trial for Covid-19 to prevent clinical deterioration and rapidly improve thoracic computerized axial tomography (CT-chest)¹. Multiple descriptive trials of high dose famotidine (both inpatient and outpatient) have demonstrated clinical response^2,3,4. We describe the rapid clinical responses after increasing the celecoxib dosage to 400mg bid with high dose famotidine 80mg qid in both a critical inpatient who on baseline required 40 liters per minute high flow nasal insufflation and an outpatient who declined admission but had critical Covid-19 biomarkers.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

Fractional differential equations have become a fundamental modelling approach for understanding and simulating the many aspects of nonlocality and spatial heterogeneity of complex materials and systems. Yet, while real-order fractional operators are nowadays widely adopted, little progress has been made in extending such operators to complex-order counterparts. In this work, we introduce new definitions for the complex-order fractional Laplacian, fully consistent with the theory of averaging of smooth functions over fractal sets, and present tailored spectral methods for their numerical treatment. The proposed complex-order operators exhibit a dual particle-wave behaviour, with solutions manifesting wave-like features depending on the magnitude of the imaginary part of the fractional order. Reaction-diffusion systems driven by the complex-order fractional Laplacian exhibit unique spatio-temporal dynamics, such as equilibrium of diffusion in random materials by interference of scattered waves, conduction block and highly fractionated propagation, or the generation of completely novel Turing patterns. Taken together, our results support that the proposed complex-order operators hold unparalleled capabilities to advance the description of multiscale transport phenomena in physical and biological processes highly influenced by the heterogeneity of complex media.

10% copper substituted (BiCUVOX/Bi₂V_0.9Cu_0.1O_5.5−δ) and 5% copper/titanium double-substituted bismuth vanadate (BiCUTIVOX/Bi₂V_0.9(Cu_0.05Ti_0.05)O_5.5−δ) platelets were formed by molten salt synthesis (MSS) using a eutectic KCl/NaCl salt mixture. The product was phase pure within the limits of X-ray diffraction. The size and form of the platelets could be controlled by changing the heating temperature and time. The crystallite growth rate at a synthesis temperature of 650 °C, and activation energy for grain growth were determined for BICUTIVOX, which experienced inhibited growth compared to BICUVOX. Quasi-equilibrium, multi-foil shapes consisting of lobes around the perimeter of the platelets were observed and explained in the context of relative two-dimensional nucleation and edge growth rates.

Climate warming is expected to have pronounced effects on Arctic and Subarctic ecosystems, especially regions underlain by discontinuous and relatively warm permafrost. The main goal of this research is to evaluate the vulnerability and dynamics of permafrost under climate warming across the various ecotypes in respect of ecosystem stability, socioeconomic impact, and for better understanding possible future environmental changes. We suggested the new version of the spatially distributed permafrost dynamics model (GIPL2-MPI), which is developed in the Geophysical Institute, University of Alaska Fairbanks. This model is based on the ecosystem approach to simulate the permafrost dynamics, which we are discussing in this paper. We combined ground-based observations and numerical freeze/thaw modeling using climate-ecosystem-permafrost interactions to understand the physical processes and mechanisms controlling permafrost physical state. We predict the changes in permafrost conditions using output from two GCMs (NCAR-CCSM4 and GFDL-CM3) and Five-Model Average Ensemble for the RCP-4.5 and RCP-8.5 scaled down to 1 by 1 km spatial resolution (https://uaf-snap.org/) across entire Alaska. Our result shows that by the end of the current century widespread near-surface permafrost degradation could begin everywhere in Alaska southward of the Brooks Range as well as across some spots at the North Slope Alaska.

‘‘Sustainable Supply Chain Management’’ (SSCM) has become a hot topic on which eminent researchers are working on every day. However, it is clear that we still observe in this field of research, an artistic vagueness on the fundamental differences between Sustainable Supply Chain Management and Green Supply Chain Management (GSCM) Moreover, it is regrettable to note that previous researchers haven't yet thought about having a specific theory which can be applied to SSCM, the use of macro theories still being observed. In addition, we observe a lack of research in the oil and gas industry especially in Developing Countries when we know that this industry is the most lucrative and polluting. Therefore, this paper’s purpose consists to make a Systematic Literature Review (SLR) of 38 papers published from 2005 to 2023 especially in oil and gas industry and in Developing Countries, in order to make a clear distinction between SSCM and GSCM while highlighting the practices related to them and at the same time, generating a united theory which will bring together the triple bottom line of the SSCM. As findings, the authors built a Conceptual Framework that applies the triple bottom line of the SSCM in oil and gas industry by demonstrating that GSCM which is more related to environmental issues and is a part of the SSCM. Therefore, in order to be more efficient and to have a great impact on their performances, Oil and gas companies should apply SSCM practices rather than GSCM practices which are very different from what is applied in Developed Countries because of certain barriers and pressures.

One of the most used markers of cancer stem cells in several cancers, including colorectal cancer and breast cancer, is CD44. CD44 is a glycoprotein that traverses the cell membrane and binds to many ligands including hyaluronan resulting in activation of signaling cascades. Several reports have shown conflicting data on the expression of CD44 and that the expression depends on modes of investigations and subtypes of cancers. In addition, the correlation between CD44 expression and drug resistance, immune infiltration, EMT, metastasis and patients prognosis in several cancer types remains unclear. This study investigated CD44 expression in several cancers and explored its relationship with tumorigenesis using various publicly available databases, including The Cancer Genome Atlas, GEPIA, Oncomine, Genomics of Drug Sensitivity in Cancer and Tumor Immune Estimation Resource. Our analysis reveals that CD44 is differentially expressed in different cancers. CD44 expression is significantly associated with cancer patients’ survival in gastric, pancreatic and colorectal cancers. In addition, CD44 expression is closely linked with immune infiltration and immune suppressive features in pancreatic, colon adenocarcinoma and stomach cancer. High CD44 expression was significantly correlated with the expression of drug resistance-, EMT- and metastasis- linked genes. Tumors expressing high CD44 have higher mutation burden and afflict older patients than tumors expressing low CD44. Cell lines expressing high CD44 are more resistant to anti-cancer drugs compared to those expressing low CD44. Protein-protein interaction investigations and functional enrichment analysis showed that CD44 interacts with gene products related to cell-substrate adhesion, migration, platelet activation, and cellular response to stress. KEGG pathway analysis revealed that these genes play key roles in biological adhesion, cell component organization, locomotion, G-α-signaling and the response to stimulus. Overall, this investigation reveals that CD44 play significant roles in tumorigenesis, can be used as a prognostic biomarker in several cancers and can be therapeutically targeted in cancer therapy.

Candida albicans is a major fungal pathogen of humans, accounting for 15% of nosocomial infections with an estimated attributable mortality of 47%. C. albicans is usually a benign member of the human microbiome in healthy people. Under constant exposure to highly dynamic environmental cues in diverse host niches, C. albicans has successfully evolved to adapt to both commensal and pathogenic lifestyles. The ability of C. albicans to undergo a reversible morphological transition from yeast to filamentous forms is a well-established virulent trait. Over the past few decades, a significant amount of research has been carried out to understand the underlying regulatory mechanisms, signaling pathways, and transcription factors that govern the C. albicans yeast-to-hyphal transition. This review will summarize our current understanding of well-elucidated signal transduction pathways that activate C. albicans hyphal morphogenesis in response to various environmental cues and the cell cycle machinery involved in the subsequent regulation and maintenance of hyphal morphogenesis.

To guide the design and optimization of King Grass (KG) harvesting machinery, this paper studied the cutting characteristics of KG stalks under different physical properties and mechanical parameters. A cutting device that can achieve different parameter adjustments was designed, and indoor cutting experiments were conducted. The results of the single factor experiment show that with the increase of moisture content and stalk height, the ultimate shear stress (USS) and unit-area cutting energy consumption (UCEC) gradually decrease. The optimal range of blade clearance for cutting KG stalks is 2.0-4.0 mm. The optimal value range for oblique cutting angle is 10 ° -40 °. The reasonable range of skew cutting angle is 10 °-20 °. The optimal range for sliding cutting angle is 20 ° - 40 °. The response surface methodology was used to conduct parameter optimization experiments. The regression models were established for various factors and the USS, as well as the UCEC. The optimal combination of cutting parameters has been determined. Under this parameter setting, the USS is 0.4 MPa and UCEC is 3.5 mJ/mm2. The average relative errors between the predicted and experimental values of USS and UCEC were 6.34% and 4.71%, respectively. This indicates that the model can correct reflect the mathematical relationship between various factors and USS and UCEC. This study provides theoretical support for the design of KG harvesting machinery.

The motivation of this work is to investigate experimentally the influence of non-thermal plasma (NTP) application on the reaction kinetics of atmospheric pressure steam gasification of charcoal using a thermostatically controlled drop tube reactor. A gliding-arc generator provides about 1 kW electrical power NTP. For comparison thermal gasification is investigated under comparable flow and specific energy input conditions providing additional heat to the steam. Optical temperature measurement 20 cm flow down of the NTP zone is utilized to characterize the specific enthalpy of the reactive flow. The composition of produced syngas is measured by a gas analyzer and used for the calculation of gas flow rates. The results show a NTP-enhancement on the production of individual syngas components (H2, CO, CH4), especially on hydrogen production by around 39%. The syngas-based carbon conversion and hydrogen release are calculated from the carbon and hydrogen balance between the correspondent content in syngas and in the feedstock. The NTP promoted the carbon conversion and hydrogen release by 25% and 31%, respectively. The first-order reaction kinetics are determined by data-fitting in an Arrhenius diagram. The plasma enhanced the reaction rate coefficients by 27%. Based on experimental results and other literature, possible plasma-induced reactions are proposed.

Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interventions developed using small animal models fail to translate to human use due to physical or biological differences. Recently, large-animal minipigs have emerged in neuroscience due to both brain similarity and economic advantages. Medical image processing is a crucial part of research as it allows researchers to monitor their experiments and understand disease development. However, although many algorithms are created and optimized for MR analysis of human data, those tools are not directly applicable or sufficiently sensitive to measure minipig data. In this work, we propose PigSNIPE - a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. The pipeline allows for image registration, AC-PC alignment, landmark detection, skull stripping, brainmasks and intracranial volume segmentation (DICE 0.98), tissue segmentation (DICE 0.82), and caudate-putamen brain segmentation (DICE 0.8) in under two minutes. To the best of our knowledge, this is the first automated pipeline tool aimed at large animal images.

Nickel base superalloy GH4169 is a material with strong mechanical properties and is difficult to process. In order to reduce tool wear during material processing and improve workpiece surface processing quality, based on the finite element simulation software DEFORM, the influence of n, ap, and fz parameters on tool wear during carbide tool milling GH4169 was studied, and a simulation orthogonal experimental model was established, The prediction model of tool wear is obtained; The ultrasonic vibration milling is compared with ordinary milling, and the improvement degree of different coating materials on carbide tool wear is explored. The results show that the ultrasonic vibration signal is helpful to reduce tool wear, improve the surface quality of the workpiece, and improve the stability of the milling process. TIALN/TIN (WC) composite coated tools have good cutting performance, help to reduce tool temperature, reduce tool wear, and improve tool life.

In the present manuscript, we report new insights into the concept of (a)synchronicity in Diels-Alder (DA) reactions in the framework of the reaction force analysis, in conjunction with natural population calculations and atomic resolution of energy derivatives along the intrinsic reaction coordinate (IRC) path. Our findings suggest that the DA reaction transitions between a preferentially concerted mechanism to a stepwise one in a 0.10 Å window of synchronicity indices, ranging from 0.90 to 1.00 Å. We have also shown that the relative position of the global minimum of the reaction force constant with respect to the TS is an alternative and quantifiable indicator of the (a)synchronicity in DA reactions. Moreover, the atomic resolution of energy derivatives reveals that the mechanism of the DA reaction involves two inner elementary processes associated with the formation of each of the two C-C bonds. This resolution goes on to indicate that, in asynchronous reactions, the driving and retarding components of the reaction force are mostly due the fast and slow-forming C-C bonds (elementary process) respectively, while in synchronous ones both elementary processes retard and drive the process concomitantly and equivalently.

We present a modified version of the Szilard Engine, demonstrating that an explicit measurement procedure is entirely unnecessary for its operation. By considering our modified engine, we are able to provide a new interpretation of Landauer’s original argument for the cost of erasure. From this view, we demonstrate that a reset operation is strictly impossible in a dynamical system with only conservative forces. Then, we prove that to approach a reset yields an unavoidable instability at the reset point. Finally, we present an original proof of Landauer’s principle that is completely independent from the Second Law of Thermodynamics.

In the U.S. 44% of low-income households struggle to pay their utility bills, affecting their ability to afford necessities such as food and health expenses. Several government and utility funded energy efficiency programs exist to assist those experiencing energy insecurity. In Salt Lake City, Utah, there is a high demand for, but low availability of, energy efficiency services in underserved neighborhoods creating an opportunity for creative community-based programs to fill this inherent gap. This pilot project, involving the exchanging of LED bulbs in Salt Lake City, highlights the development of a community-based energy efficiency program that aims to bring energy savings to a uniquely targeted portion of the city and determines its feasibility in addressing energy insecurity at a larger scale. Through the 8-month project duration, 1,432 bulbs were exchanged at 23 events reaching 181 households in low-income areas. Through a year of use, these bulbs are estimated to save residents approximately 18,219 USD in electricity bills and reduce CO2 emissions from power plants by 122.23 metric tons, in addition to a savings of 4,400 USD in social cost of carbon as defined by the U.S. Environmental Protection Agency. Since this pilot reached less than 1% of households, we extrapolated a reach of 2%, 5%, and 7.5% and found substantial potential decreases in power plant emissions and financial savings. As this project is ongoing and being expanded, we discuss relevant findings that will help shape future community-based models so that they are appropriately deployed and more effective in alleviating local energy insecurity.

Tumorigenesis is a complex and dynamic process involving cell-cell and cell-extracellular matrix (ECM) interactions that allow tumor cell growth, drug resistance and metastasis. This review provides an updated summary of the role played by the tumor microenvironment (TME) components and hypoxia in tumorigenesis and highlight various ways through which tumor cells reprogram normal cells including into phenotypes that are pro-tumorigenic including cancer associated- fibroblasts, -macrophages and -endothelial cells. Tumor cells secrete numerous factors leading to transformation of a previously anti-tumorigenic environment into a pro-tumorigenic environment. Once formed, solid tumors continue to interact with various stromal cells including local and infiltrating fibroblasts, macrophages, mesenchymal stem cells, endothelial cells, pericytes, and secreted factors and the ECM within the tumor microenvironment (TME). The TME is key to tumorigenesis, drug response and treatment outcome. Importantly, stromal cells and secreted factors can initially be anti-tumorigenic but over time promote tumorigenesis and induce therapy resistance. To counter hypoxia, increased angiogenesis leads to formation of new vascular networks in order to actively promote and sustain tumor growth via supply of oxygen and nutrients whilst removing metabolic waste. Angiogenic vascular network formation aid in tumor cell metastatic dissemination. Successful tumor treatment and novel drug development require the identification and therapeutic targeting of pro-tumorigenic components of the TME including cancer-associated- fibroblasts (CAFs) and -macrophages (CAMs), hypoxia, blocking ECM-receptor interactions, in addition to targeting of tumor cells. Re-programming of stromal cells and the immune response to be anti-tumorigenic is key to therapeutic success. Lastly, this review highlights potential TME- and hypoxia-centred therapies under investigations.

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6,497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

Consumer preference not only dictates what food is consumed but how that food is produced. Ingredients in livestock feed that are not antibiotics, not genetically modified, not of animal origin, and that are nutrient-like, tend to be more acceptable to consumers, retailers, and producers. Mono- and diglycerides (MDG) fit these criteria, are commonly used in food, and are generally recognized as safe. But beyond being emulsifiers and a source of energy, MDG are also known to have antimicrobial, immune and microbiome modulation, and angiogenic activity. MDG in broiler diets have been shown to impact a variety of immune-related functions such as regulation of circulating antibodies, as well as decreased gene expression or protein concentration of pro-inflammatory cytokines like TNF-α, IL-1β, IL-6, and IFN-γ. In addition, MDG can affect metabolic function and intestinal integrity. Results of this review show that MDG can serve as antimicrobial and growth-supporting alternatives for reducing poultry production losses, improving resource utilization, and meeting consumer demand for sustainably produced and safe animal protein.

Fractional calculus gained a lot of attention in the last couple of years. Researchers discovered that processes in various fields follow rather fractional dynamics than ordinary integer-ordered dynamics, meaning the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of them being that fractional derivatives’ inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It further provides many insights and discoveries in various scientific disciplines. As these two modern-day topics provide a lot of potential for combined approaches to describe complex dynamics, this article reviews combined approaches of fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there already is profound literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning & fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools and enhance their existing approaches.

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. Machine learning algorithms have achieved state-of-the-art material classification performance on benchmark hyperspectral data sets; however, these techniques often do not consider varying atmospheric conditions experienced in a real-world detection scenario. To reduce the impact of atmospheric effects in the at-sensor signal, atmospheric compensation must be performed. Radiative Transfer (RT) modeling can generate high-fidelity atmospheric estimates at detailed spectral resolutions, but is often too time-consuming for real-time detection scenarios. This research utilizes machine learning methods to perform dimension reduction on the transmittance, upwelling radiance, and downwelling radiance (TUD) data to create high accuracy atmospheric estimates with lower computational cost than RT modeling. The utility of this approach is investigated using the instrument line shape for the Mako long-wave infrared hyperspectral sensor. This study employs physics-based metrics and loss functions to identify promising dimension reduction techniques. As a result, TUD vectors can be produced in real-time allowing for atmospheric compensation across diverse remote sensing scenarios.

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying machine learning models. Application-wise, we show how machine learning surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate machine learning models may hold the key to making the simulation of complex biological models possible using a typical desktop computer.

Crowdsourced Data (CSD) generated by citizens is becoming more popular as its potential utilisation in many applications is increasing due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data collection procedures. The quality of CSD can be assessed according to a range of attributes including its relevance. Information relevance has been explored through using in Geographic Information Retrieval (GIR) techniques to identify relevant information. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. The thematic and geographic relevance were assessed using the Term Frequency-Inverse Document Frequency (TF-IDF), Vector Space Model (VSM) and Natural Language Processing (NLP) techniques. The thematic and geographic specificities of the queries were calculated as 0.44 and 0.67 respectively, which indicates the queries used were more geographically specific than thematically specific. The Spearman's rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment for other possible crowdsourced data analysis.

We provide quantitative results from GIS-based modelling of urban emission functions for a range of representative low- and mid-rise locations, ranging from individual streets to residential communities within cities as well as entire towns and city regions with the aim of whether lantern photometry or built environment has the dominant effect on light pollution. We demonstrate the scalability of our work by providing results for the largest urban area modelled to date, comprising the central 117 km2 area of Dublin City and containing nearly 42,000 public lights. Our results show a general similarity in the shape of the azimuthally-averaged emission function for all areas examined, with differences in total light output distribution depending primarily on the nature of the lighting and, to a smaller extent, on the obscuring environment including seasonal foliage effects. A comparison with global satellite observations shows that they are consistent with the deduced angular emission function for other low-rise areas worldwide. We further validate our approach by comparing results for a range of urban locations by the close agreement observed in a detailed comparison of with calibrated imagery from the International Space Station. To our knowledge, this is the first such detailed quantitative verification of light loss calculations.

We analyze a time-delayed SIQR model that considers the transportation-related infections and entry-exit screening. This model aims to determine the measures for preventing and controlling major emergent infectious diseases and the associated costs. We calculate the basic reproduction number (R0), apply the linearization method, and construct an appropriate Lyapunov function to determine the stability of the disease-free equilibrium in the local and global. We collect COVID-19 infection data from two regions in the United States in 2020 for data fitting, obtain a set of optimal parameter values, and show that the model is suitable for simulating the outbreak and spread of emerging infectious diseases. We find that transportation-related infections increase the basic reproduction number, enhancing the impact on disease spread. Entry-exit screening effectively suppress the spread of disease by reducing the basic reproduction number. Furthermore, we investigate the influence of the incubation period on disease and find that a shorter incubation period results in a shorter duration but a larger scale of infection, and the peaks are reduced. We conduct a sensitivity analysis of the R0 and propose three measures to prevent the spread of new infectious diseases based on the most sensitive parameters: wearing masks, implementing urban closures, and administering medication to sick but not yet hospitalized patients promptly. In the case of COVID-19, we use Pontryagin’s minimum principle to get time-varying control measures to control the spread of the outbreak. Optimal control effectively controls the development and deterioration of the disease. Finally, several control measures are compared through cost-effectiveness analysis, and the results show that wearing masks is the most cost-effective measure. Our study provides some practical guidance for controlling the spread of major emerging infectious diseases.

Dementia education and training for workforce development is becoming increasingly important in bridging knowledge gaps among health and social care practitioners in the UK and internationally. Dementia Education for Workforce Excellence (DEWE) was developed during the COVID-19 pandemic blending both synchronous and asynchronous instruction and delivered across three different contexts: care homes, home care, and nurse education within the UK and India. This study aimed to evaluate DEWE using mixed methods with online survey data analyzed descriptively and interview data analyzed thematically. Integration of survey and interview data aimed toward a comprehensive evaluation of this novel approach for dementia workforce development. Thirty-four social care practitioners and nurse educators completed the online survey demonstrating high-level learner satisfaction; learning gains; behavioral change, and motivation to share new knowledge. Four key themes developed from the analysis of interviews (n=9) around participants’ pursuit of new knowledge; delivery modes in DEWE; learning gains and impact of DEWE; and adaptations for future program implementation. Findings suggest DEWE is an innovative resource that promotes person- and relationship-centered dementia care across all stages of one’s dementia journey. Cultural adaptations are recommended for international delivery to ensure contextual alignment and maximum impact.

The terms 'open' and 'openness' are widely used across the current higher education environment particularly in the areas of repository services and scholarly communications. Open-access licensing and open-source licensing are two prevalent manifestations of open culture within higher education research environments. As theoretical ideals, open-licensing models aim at openness and academic freedom. But operating as they do within the context of global neoliberalism, to what extent are these models constructed by, sustained by, and co-opted by neoliberalism? In this paper, we interrogate the use of open-licensing within scholarly communications and within the larger societal context of neoliberalism. Through synthesis of various sources, we will examine how open access licensing models have been constrained by neoliberal or otherwise corporate agendas, how open access and open scholarship have been reframed within discourses of compliance, how open-source software models and software are co-opted by politico-economic forces, and how the language of 'openness' is widely misused in higher education and repository services circles to drive agendas that run counter to actually increasing openness. We will finish by suggesting ways to resist this trend and use open-licensing models to resist neoliberal agendas in open scholarship.

In the power plant industry, the turbine inlet temperature (TIT) plays a key role in the efficiency of the gas turbine and, therefore, the overall—in most cases combined—thermal power cycle efficiency. Gas turbine efficiency increases by increasing TIT. However, an increase of TIT would increase the turbine component temperature which can be critical (e.g., hot gas attack). Thermal barrier coatings (TBCs)—porous media coatings—can avoid this case and protect the surface of the turbine blade. This combination of TBC and film cooling produces a better cooling performance than conventional cooling processes. The effective thermal conductivity of this composite is highly important in design and other thermal/structural assessments. In this article, the effective thermal conductivity of a simplified model of TBC is evaluated. This work details a numerical study on the steady-state thermal response of two-phase porous media in two dimensions using personal finite element analysis (FEA) code. Specifically, the system response quantity (SRQ) under investigation is the dimensionless effective thermal conductivity of the domain. A thermally conductive matrix domain is modeled with a thermally conductive circular pore arranged in a uniform packing configuration. Both the pore size and the pore thermal conductivity are varied over a range of values to investigate the relative effects on the SRQ. In this investigation, an emphasis is placed on using code and solution verification techniques to evaluate the obtained results. The method of manufactured solutions (MMS) was used to perform code verification for the study, showing the FEA code to be second-order accurate. Solution verification was performed using the grid convergence index (GCI) approach with the global deviation uncertainty estimator on a series of five systematically refined meshes for each porosity and thermal conductivity model configuration. A comparison of the SRQs across all domain configurations is made, including uncertainty derived through the GCI analysis.

Objectives Asylum seekers in Germany represent a highly vulnerable group from a health perspective due to a variety of risk factors. At the same time their access to healthcare is restricted. While the introduction of the Electronic Health Insurance Card (EHIC) for asylum seekers instead of healthcare-vouchers is discussed controversially using politico-economic reasons, there is hardly any empirical evidence on its actual impact on the use of medical services Study design Thus, the aim of this study is to examine the influence of the possession of the EHIC on the use of medical services by asylum seekers as measured by their consultation rate of ambulant physicians (CR). For this purpose, a standardized survey was carried out to 260 asylum seekers in different municipalities of which some have introduced the EHIC for asylum seekers, while others have not. Methods Various CR were differentiated considering possible third variables as well as confounding factors. The period prevalence was compared between the groups "with EHIC" and "without EHIC" using a two-sided t-test. Multivariate analysis was done using a linear OLS regression model. Results Asylum seekers who are in possession of the EHIC are significantly more likely to seek ambulant medical care than those receiving healthcare-vouchers. Their CR, however, does not differ significantly from the age-corrected CR of the autochtonous population. Taking into account relevant covariables, the possession of the EHIC can be viewed as an independent influencing factor on the asylum seekers' use of medical care. Conclusions The results of this study suggest that having to ask for healthcare-vouchers at the social security office could be a relevant barrier for asylum seekers. Nevertheless, the ownership of the EHIC does not seem to lead to an overuse of medical services.

In the last two decades, the discovery of various pathways involved in renal cell carcinoma (RCC) have led to the development of biologically-driven targeted therapies. Hypoxia inducible factors (HIFs), angiogenic growth factors, von Hippel-Lindau (VHL) gene mutations and oncogenic miRNAs play essential roles in the pathogenesis and drug resistance of clear cell renal cell carcinoma. These insights have led to the development of VEGF inhibitors, mTOR inhibitors and immunotherapeutic agents which have significantly improved outcomes of patients with advanced RCC. HIF inhibitors will be a valuable asset in the growing therapeutic armamentarium of RCC. Various histone deacetylase (HDAC)inhibitors, including selenium and agents such as PT2385 and PT2977, are being explored in various clinical trials as potential HIF inhibitors to ameliorate the outcomes of RCC patients. In this article, we will review the current treatment options and highlight the potential role of selenium in the modulation of drug resistance biomarkers expressed in ccRCC tumors.

This paper provides an overview of the current state-of-the-art digital twin and digital thread technology in industrial operations. Both are transformational technologies that have the advantage of improving the efficiency of current design and manufacturing. Digital twin is an important element of the Industry 4.0 digitalization process; however, the huge amount of data that are generated and collected by digital twin offer challenges in handling, processing and storage. The paper aims to report on the development of a new framework that combines the digital twin and digital thread for better data management in order to drive innovation, improve the production process and performance, and to ensure continuity and traceability of information. The digital twin/thread framework incorporated behavior simulation and physical control components, in which these two components rely on the connectivity between the twin and thread for information flow and exchange to drive innovation. The twin/thread framework encompasses specifications that include organizational architecture layout, security, user access, cloud computing set-up, and hardware and software requirements. It is envisaged that the framework will be applicable to enhancing optimization of operational processes and traceability of information in the physical world, especially in Industry Shipyard 4.0.

In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the Variational Quantum Classifier (VQC) and the Quantum Kernel Estimator (QKE). We evaluate the performance of these classifiers on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, we developed a data set based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results.\\ Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers like XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes.\\ Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field.\\ To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.

We developed a temporal population receptive field model to differentiate the functional and hemodynamic responses in the human LGN. The hemodynamic response of the human LGN is dominated by the richly vascularized hilum, a structure that serves as a point of entry for blood vessels entering the LGN and supplying the substrates of central vision. The location of the hilum along the ventral surface of the LGN and the resulting gradient in the amplitude of the hemodynamic response across the extent of the LGN has made it difficult to segment the human LGN into its more interesting magnocellular and parvocellular regions that represent two distinct visual processing streams. Here, we show that an intrinsic clustering of the LGN responses to a variety of visual input reveals the hilum, and further that this clustering is dominated by the amplitude of the hemodynamic response. We introduce a temporal population receptive field model that includes both a sustained and transient temporal impulse response. When we account for the hemodynamic amplitude, we demonstrate that this temporal response model is able to functionally segregate the residual responses according to their temporal properties.

This paper proposes an Integrated Fire Management (IFM) framework that can be used to support communities and resource managers in finding effective and efficient approaches to prevent damaging fires, as well as maintain desirable fire regimes in Kenya. Designing and implementing an IFM approach in Kenya calls for a systematic understanding of the various uses of fire and the underlying perceptions and traditional ecological knowledge of the local people. The here proposed IFM framework allows an evaluation of the risks posed by fires, while balancing them with their beneficial ecological and economic effects, and thus developing effective fire management approaches. A case study of the proposed IFM framework was conducted in Gathiuru Forest that is part of the larger Mt. Kenya Forest Ecosystem. Focus group discussions were held with key resource persons, primary and secondary data on socio-economic activities were studied, fire and weather records were analyzed and the current fire management plans were consulted. Questionnaires were used to assess how the IFM is implemented in the Gathiuru Forest Station. The results show that the proposed IFM framework is scalable and can be applied in places with fire-dependent ecosystems as well as in places with fire-sensitive ecosystems in Kenya. The effectiveness is dependent on the active participation, formulation and implementation of the IFM activities by the main stakeholder groups (Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), and the Community Forest Associations (CFA)). The proposed IFM framework helps in implementing cost-effective approaches to prevent damaging fires and maintain desirable fire regimes in Kenya.

A series of novel N-n-butyl-1,8-naphthalimide derivatives were synthesized via a three-step reaction involving nucleophilic substitution and acylation. All of the compounds were characterized by IR, ¹H NMR, ¹³C NMR, MS, and elemental analysis, and the crystal structure of N-n-butyl-4-[N’,N’-bis(2`,4`-dichlorobenzoyl)ethylamino]-1,8-naphthalimide was determined. The π-π stacking interactions and hydrogen bonds between the two molecular core planes (naphthalimide ring) and the van der Waals forces between the flexible n-butyl groups resulted in a 3D long-chain structure. The UV-vis and fluorescence properties of the title compounds were investigated. The results indicated that the monosubstituted 1,8-naphthalimide derivatives bearing an electron-donating group on the benzene ring or a structure with a larger conjugative effect exhibited enhanced fluorescence properties.

Chlorophyll a concentration (Chl) is a key variable to estimate primary production (PP) through ocean color remote sensing (OCRS). Accurate Chl estimate is crucial for better understanding of spatio-temporal trends of PP over recent decades as a consequence of climate change. However, a number of studies have reported that currently operational chlorophyll a algorithms perform poorly in the Arctic Ocean (AO), which is mainly caused by the interference of colored and detrital material (CDM) with the phytoplankton signal in the visible part of the spectrum. To determine how and to what extent that CDM would bias the estimation of Chl, we evaluated the performances of 8 currently available ocean color algorithms: OC4v6, OC3Mv6, OC3V, OC4L, OC4P, AO.emp, GSM01 and AO.GSM. Our results suggest that the empirical AO.emp algorithm performs the best overall, but for waters with high CDM (acdm(443) > 0.067 m-1), which is of much interest in the Arctic, it is the two semi-analytical GSM models that show better performance. Besides, sensitivity analyses using an Arctic spectrally- and vertically-resolved primary production model further show that errors in Chl mostly propagate proportionally to PP estimates with 7% amplification at maximum. We aslo demonstrate that the higher level of CDM relative to Chl in the water column, the larger the bias would occur in both Chl and PP estimates. Although the AO.GSM overall best performs among algorithms tested in the present study, it tends to fail for a significant number of pixels (16.2% observed in the present study) particularly for waters with high CDM. Our results suggest that an algorithm that provides reasonable Chl estimates for a wide range of optically-complex Arctic waters is still required.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. First, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. Simulation and physical experiments are carried out to evaluate the performance of the proposed method. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in swarm foraging.

The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and finally identified six main ridership patterns. We then developed a new built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis result revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type station, and centrality was only associated with employment-oriented hybrid type station. These results could inform the coordinated development of rail transit and land use, and the renewal and enhancement of the built environment in the pedestrian catchment area surrounding metro stations.

Wastewater-based surveillance is emerging as an important tool for COVID-19 pandemic trending. Current methods of wastewater collection, such as grab and auto-composite sampling, have drawbacks that impede effective surveillance, especially from small catchments with limited accessibility. Passive samplers, which are more cost-effective and require fewer resources to process, are promising candidates for monitoring wastewater for SARS-CoV-2. Here, we compared traditional auto sampling with passive sampling for SARS-CoV-2 detection in wastewater. Torpedo-style 3D printed passive sampler device containing both cotton swabs and electronegative filter membranes was used. Between April and June 2021, fifteen passive samplers were placed at a local hospital wastewater outflow alongside an auto sampler. Reverse transcription and quantitative polymerase chain reaction (RT-qPCR) was used to detect SARS-CoV-2 in the samples after processing and RNA extraction. The swab and membrane of the passive sampler showed similar detection rates and Ct values for SARS-CoV-2 RNA for the N1 and N2 gene targets. The passive method performed as well as the grab/auto sampling, with no significant differences between N1 and N2 Ct values. There were discrepant results on two days with negative grab/auto samples and positive passive samples, which might be related to the longer duration of passive sampling in the study. Overall, the passive sampler was rapid, reliable and cost-effective, and could be used as an alternative sampling method for the detection of SARS-CoV-2 in wastewater.

There are frequently interactions between active folds and major rivers (mean annual water discharges > 70 m³s^-1). The major river may incise across the fold, to produce a water gap across the fold, or a bevelling (or lateral planation) of the top of the fold. Alternatively, the major river may be defeated to produce a diversion of the river around the fold, with wind gaps forming across the fold in some cases, or ponding of the river behind the fold. Why a river incises or diverts is often unclear, though influential characteristics and processes have been identified. A new scheme for investigating fold-river interactions has been devised, involving a short description of the major river, climate, and structural geology, and 13 characteristics of river and fold geomorphology: 1) Channel width at location of fold axis, w, 2) Channel-belt width at location of fold axis, cbw, 3) Floodplain width at location of fold axis, fpw, 4) Channel sinuosity, Sc, 5) Braiding index, BI, 6) General river course direction, RCD, 7) Distance from fold core to location of river crossing, C-RC, 8) Distance from fold core to river basin margin, C-BM, 9) Width of geological structure at location of river crossing, Wgs, 10) Estimate of erosion resistance of surface sediments/rocks and deeper sediments/rocks in fold, ERs, ERd, 11) Channel water surface slope at location of fold axis, s, 12) Average channel migration rate, Rm, 13) Estimate of fold total uplift rate, TUR. The first 10 geomorphological characteristics should be readily determinable for nearly all major rivers using widely available satellite imagery and fine scale geological maps. The last 3 characteristics should be determinable for most major rivers where other data sources are available. This study demonstrates the methodology of this scheme, using the example of the major rivers Karun and Dez interacting with active folds in the foreland basin tectonic setting of lowland south-west Iran. For the rivers Karun and Dez (mean annual water discharges 575 m³s^-1 and 230 m³s^-1, respectively), it was found that geomorphological characteristics Nos. 2, 3 and 7 had statistically significant differences (p-value ≤ 0.05) between the categories of river incision across a fold and river diversion around a fold. For river incision, at the fold axis, channel-belt width was always < 2.7 km, and floodplain width was generally (80 % of cases) < 5.7 km; whereas for river diversion, at the projection of the fold axis, these two characteristics had a wide range of values. For river incision, the distance from the fold core to the location where the river channel crossed the fold axis, was generally (80 % of cases) ≤ 8.5 km; whereas for river diversion, this distance was always > 22 km. Since it is highly likely that different characteristics will be important for other major rivers interacting with other folds, it is recommended that this scheme is now used to investigate a variety of major rivers from across the globe. By comparing the same parameters for different major rivers, a better understanding of fold-river interactions should be achieved.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

Endoscopic medical images can suffer from uneven illumination, low contrast, and lack of texture information due to the use of point directional light sources and the presence of narrow tissue structures, posing diagnostic difficulties for physicians. In this paper, a deep learning-based su-pervised illumination enhancement network is designed for low-light endoscopic images, aiming to improve both global illumination and local details. Initially, a global illumination enhancement module is formulated utilizing a higher-order curve function to improve global illumination. Sec-ondly, a local feature extraction module incorporating dual attention is designed to capture local detailed features. Considering the significance of color fidelity in biomedical scenarios, the designed loss function prioritizes introducing color loss to alleviate image color distortion. Compared with seven state-of-the-art enhancement algorithms on Endo4IE endoscopic datasets, experimental re-sults show that the proposed method can better enhance low-light endoscopic images and avoid image color distortion. It provides an efficient method to enhance images captured by endoscopes which can effectively assist clinical diagnosis and treatment.

With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018.

Cancer patients and their family caregivers experience various losses when patients become terminally ill, yet little is known about the grief experienced by patients and caregivers and factors that influence grief as patients approach death. Additionally, few, if any, studies have explored associations between advance care planning (ACP) and grief resolution among cancer patients and caregivers. To fill this knowledge gap, the current study examined changes in grief over time in patients and their family caregivers and whether changes in patient grief are associated with changes in caregiver grief. We also sought to determine how grief changed following completion of advance directives. The sample included advanced cancer patients and caregivers (n=98 dyads) from Coping with Cancer III, a federally funded, multi-site prospective longitudinal study of end-stage cancer care. Participants were interviewed at baseline and at follow-up roughly 2 months later. Results suggest synchrony, whereby changes in patient grief were associated with changes in caregiver grief. We also found that patients who completed a living will (LW) experienced increases in grief, while caregivers of patients who completed a do-not-resuscitate (DNR) order experienced reductions in grief, suggesting that ACP may prompt “grief work” in patients while promoting grief resolution in caregivers.

Alphaviruses are positive-sense RNA arboviruses that are capable of causing severe disease in otherwise healthy individuals. There are many aspects of viral infection that determine pathogenesis and major efforts regarding the identification and characterization of virulence determinants have largely focused on the roles of the nonstructural and structural proteins. Nonetheless, the viral RNAs of the alphaviruses themselves play important roles in regard to virulence and pathogenesis. In particular, many sequences and secondary structures within the viral RNAs play an important part in the development of disease and may be considered important determinants of virulence. In this review article, we summarize the known RNA-based virulence traits and host:RNA interactions that influence alphaviral pathogenesis for each of the viral RNA species produced during infection. Overall, the viral RNAs produced during infection are important contributors to alphaviral pathogenesis and more research is needed to fully understand how each RNA species impacts the host response to infection as well as the development of disease.

NIRS uses the relative absorption of light at 850nm and 760nm, to determine skeletal muscle oxygen saturation. Previous studies have used the ratio of both signals to report muscle oxygen saturation. Purpose: To evaluate the different approaches used to represent muscle oxygen saturation, and to evaluate the pulsations of the O₂heme and Heme signal. Method: Twelve participants, ages 20-29years were tested on the forearm flexor muscles using continuous wave NIRS at rest. Measurements were taken during 2-3mins rest, during physiological calibration (5-minuts Ischemia) and during reperfusion. Results: There was a significant difference in pulse size between O₂heme and Heme signal at the three locations (p < 0.05). Resting oxygen saturation was 58.8+9.2%, 69.6+3.9%, and 89.2+6.9% when calibrated using O₂heme, TSI, and Heme, respectively. Conclusion: The difference in magnitude of O₂heme and Heme pulse with each heartbeat might suggest different anatomical locations of these signals, which propose calibrating with just one of the signals instead of the ratio of both. Calculations of physiological calibration must account for increased blood volume in the tissue, because of the changes in blood volume which appear to be primarily from the O₂heme signal. Resting oxygen levels calibrated with Heme agrees with theoretical oxygen saturation.

Zika virus (ZIKV), without a vaccine or no effective treatment approved as yet, have globally spread since the past century. The infection caused by ZIKV in humans has changed progressively from mild to subclinical in the last years, causing epidemics with greater infectivity, tropism towards new tissues, and other related symptoms as a product of various emergent ZIKV-host cell interactions. However, it is still unknown why or how the RNA genome structure impacts those interactions in differential evolutionary origin strains. Moreover, genomic comparison of ZIKV strains from the sequence-based phylogenetic analysis is well known, but differences from RNA structure comparisons are less known. Thus, in order to understand the RNA genome variability of lineages of various geographic distributions better, 412 complete genomes in a phylogenomic scanning were used for studying the conservation of structured RNAs. We found specific genomic regions, which highlight their patterns of conserved RNA structures at the level of inter-geographical comparisons. We have proposed these structures as candidates for further experimental validation to establish their potential role in vital functions of the viral cycle of ZIKV and their possible associations with the singularities of different outbreaks that occurred in specific geographic regions.

Glucocorticoid administration is a common clinical practice in attempt to decrease inflammation associated with, and improve resectability of, canine mast cell tumors (MCTs). However, the impact of neoadjuvant glucocorticoids on histological features and proliferation indices of canine MCTs is unknown. The objective of this study was to evaluate changes in tumor grade, mitotic count, Ki67, AgNOR, and AgNORxKi67 scores following short-course, anti-inflammatory neoadjuvant prednisone in canine patients with MCTs. This was a prospective, single-arm pilot study. Client-owned dogs with treatment-naïve, cytologically-confirmed MCTs were enrolled. Patients underwent an initial incisional biopsy followed by a 10-14 day course of anti-inflammatory prednisone, and surgical resection. All histological samples were randomized, masked, and evaluated by a single pathologist. Unstained paired pre- and post-treatment samples were submitted to a commercial laboratory for Ki67 and AgNOR immunohistochemical analysis. There were 11 dogs enrolled with 11 tumors. There were no statistical differences between the pre- and post-treatment histological parameters of mitotic index, Ki67, AgNOR, or Ki67xAgNOR. There were no clinically-significant alterations between pre-treatment and post-treatment in the assignment of tumor grades. A short-course of anti-inflammatory prednisone does not appear to alter the histological parameters that affect grade-determination, nor significantly alter the proliferation indices in canine MCTs.

This study aims to examine the cytotoxicity mechanisms of synthetic cathinone (bath salts) on rat primary cultured neurons and primary astroglial cells, and to assess their neurobehavioral effects on mice. We administered methylenedioxypyrovalerone (MDPV) to both rat primary cultured neurons and primary astroglial cells to assess cell injury. We also analyzed the effects of MDPV on these cell cultures using immunocytochemistry. We utilized western blotting to assess the breakdown of αII-spectrin and glial fibrillary acidic protein (GFAP) induced by MDPV. The western blotting experiment also included calpain and caspase inhibitors (SNJ1945 and Z-D-DCB, respectively) and pro-apoptotic and pro-necrotic agents (Staurosporine and calcium ionophore A23187, respectively). Lastly, we assessed MDPV’s effects on behavioral effects using rotarod, locomotor activity, elevated plus maze, Morris water maze, forced swimming, and open field tests. MDPV caused a dose-dependent release of LDH in both cerebrocortical neuron-astroglia mixed cultures and primary astroglial cultures. MDPV also caused neurite breakages and astroglial process retraction on immunocytochemistry. Lastly, MDPV induced αII-spectrin breakdown in western blotting experiments. Co-administration of calpain and caspase inhibitors reduced the degradation of αII-spectrin and GFAP. MDPV administration also increased anxiety-like behavior and locomotor activity in the mice. Synthetic cathinones, which share structural similarities with methamphetamine, also induce significant neurotoxic effects and neurobehavioral effects on rodent models. These neurotoxic effects are likely mediated by calpain and caspase-induced apoptosis and necrosis, while astroglial death is likely only due to calpain activation. Therefore, further research may focus on pharmacological interventions targeting these pathways to mitigate the cytotoxic impact of cathinones in humans.

It’s well known that existing horizontal well drilling and hydraulic fracturing technology that is used to achieve large-scale cost-effective production from immature to low-moderate maturity continental shale in China, where the organic matter mainly exists in the solid form is fairly ineffective. To overcome the obstacles, in-situ conversion technology seems feasible, while implementing it in the target layer along with estimating the amount of expected recoverable hydrocarbon in such shale formations seems difficult. This is because there is no guideline to choose the most appropriate method and select relevant key parameters for this purpose. Hence, based on thermal simulation experiments during the in-situ conversion of crude oil from the Triassic Chang 73 Formation in the Ordos Basin and the Cretaceous Nenjiang Formation in the Songliao Basin, this deficiency in knowledge was addressed. First, relationships between the in-situ converted total organic carbon (TOC) content and the vitrinite reflectance (Ro) of the shales and between the residual oil volume and the hydrocarbon yield was established. Second, the yields of residual oil and in-situ converted hydrocarbon were measured, revealing their sensitivity to fluid pressure and crude oil density. In addition, a model was proposed to estimate the amount of in-situ converted hydrocarbon based on the TOC, hydrocarbon generation potential, Ro, residual oil volume, fluid pressure, and crude oil density. Finally, a method was established to determine key parameters on the final hydrocarbon yield from immature to low-moderate maturity organic material during in-situ conversion in shales. Following the procedure outlined in this paper, estimated recoverable in-situ converted oil in the shales of the Nenjiang Formation in the Songliao Basin was estimated to be approximately 292×108 tons, along with 18.5×1012 cubic meters of natural gas, in an area of approximately 8×104 square kilometers. Collectively, the method developed in this study is independent from the organic matter type and other geological and/or petrophysical properties of the formation and can be applied to other areas globally where there isn’t any available in-situ conversion thermal simulation experimental data.

MicroRNAs (miRNAs) are a class of small non-coding RNAs, which function as regulators of gene expression and contribute in numerous physiological processes. However, little is known referring to miRNAs function in insect chemosensation. In the current study, nine small RNA libraries were constructed and sequenced from the antennae of nymphs, adult males and females of Apolygus lucorum. In total, 399 miRNAs were identified including 275 known and 124 novel miRNAs. Known miRNAs were classified into 71 families, amongst which, 23 families were insect-specific. Expression profile analysis showed that miR-7-5p_1 was the most abundant miRNAs in the antennae of A. lucorum. Altogether, 69708 target genes related to biogenesis, membrane and binding activities were predicted for 399 miRNAs. Particularly, 15 miRNAs were found to target 16 olfactory genes. These miRNAs could be involved in regulation of olfactory-associated genes ex-pression. Comparing the antennae of nymphs, adult males and females, 94 miRNAs were found to be differentially expressed. The expression levels of some differentially expressed miRNAs measured by qPCR were consistent with sequencing results. This study provides a global miRNAs transcriptome in the antennae of A. lucorum and valuable information for further investigation on miRNA-mRNA interactions, especially the functions of miRNAs in regulating chemosensation.

The human serotonin transporter (hSERT) terminates neurotransmission by removing serotonin from the synaptic cleft, which is an essential process plays an important role in depression. In addition to substrate serotonin, hSERT is also the target of drugs of abuse like cocaine and clinically used antidepressants such as escitalopram and paroxetine. To date, few studies attempt to investigate the unbinding mechanism underlying the orthosteric and allosteric modulation of hSERT. The high-resolution X-ray structure of hSERT resolved recently enables us to theoretically study the unbinding of the above four ligands against the S1 or S2 site of hSERT, by means of molecular docking, molecular dynamics (MD) and potential of mean force (PMF) simulations. We proved that for either the S1 or S2 site, the other three ligands (cocaine, escitalopram and paroxetine) are much more favorable than the original substrate serotonin, whether in kinetics along the unbinding pathways or in thermodynamics at the equilibrium states. Furthermore, the S1 site is much more favorable than the S2 site, for each ligand. Interestingly, inspection revealed that there are ~ 3Å lengths between the allosteric site of serotonin and cocaine, and an unseen un-binding pathway for escitalopram at the S1 site except for verification of the broadest trail.

South Africa possesses the largest anti-retroviral therapy (ART) program in the world but the path to this record was dramatic. There is scarce literature employing a comprehensive framework to explain this ART policy change and inform current policy making processes. This paper applies the Advocacy Coalition Framework (ACF) to analyse the interactions among diverse actors, institutions and networks that were associated with the ART policy change in South Africa. Post-apartheid, HIV/AIDS and AIDS-related mortality were serious public health problems. At the time, the discernible coalitions in the AIDS policy subsystem were the prescience coalition and AIDS dissidents. In view of the availability of compelling scientific evidence on the pathogenesis of HIV/AIDS, the clinical usefulness of ART, the availability of funding for national ART roll-out, strong global advocacy to reduce the cost of ART, all of these in an era when access to adequate HIV care was increasingly considered a human right, the environment to establish an appropriate ART policy for the country was conducive. However, AIDS dissidents dominated the policy agenda via their control over key institutions, the use of various dimensions of power, biasing evidence to inform policy, and promoting the activities of strong interest groups that were not in support of ART. National ART roll-out ultimately emerged as a political priority as a result of external shocks (on the ART policy subsystem) which disfavoured the dominant coalition. Failure to supplement this application of the ACF with key pubic policy concepts such as power dimensions, evidence use in policy, governance and emergence of global health networks would have led to suboptimal appraisal of the ART policy change and misinformation of current policy making processes.

Inflammation is an underlying problem for many disease states and has been implicated in iron deficiency (ID). This study aimed to determine whether iron status is improved by epigallocate-chin-3-gallate (EGCG) through reducing inflammation. Thirty-two male Sprague-Dawley rats were randomly divided into four groups (n = 8 each): positive controls, negative controls, lipo-polysaccharide (LPS, 0.5 mg/kg body weight), and LPS + EGCG (LPS plus 600 mg EGCG/kg diet). Iron status, hepcidin, C - reactive protein (CRP), serum amyloid A (SAA), and interleukin-6 (IL-6) were measured. There were no differences in treatment groups compared with control in CRP, hepcidin, and liver iron concentrations. Serum iron concentrations were significantly lower in the LPS (p=0.02) and the LPS+EGCG (p=0.01) than in the positive control group. Compared to the positive control group, spleen iron concentrations were significantly lower in the negative con-trol (p<0.001) but not with both LPS groups. SAA concentrations were significantly lower in LPS + EGCG group compared to LPS alone group. IL-6 concentrations were significantly higher in LPS+EGCG (p= 0.004) than in any of the three groups. EGCG reduced SAA concentrations but did not affect hepcidin or improve serum iron concentration or other iron markers.

(1) Background: Patella and achilles tendon injuries have become increasingly prevalent due to an increasingly active population. These injuries can cause significant dysfunction which is especially true for elite athletes. Standard B-Mode ultrasound has been useful in the diagnosis of these injuries, however, only to a limited extent. Shear wave elastography (SWE) can aid in evaluation by objectively measuring tissue stiffness. Prior studies have proven shear wave values can be used to evaluate tendon tissue noting differences in healthy versus pathologic tendons. One main limitation remains a lack of normative data for specific tendon tissues which we aim to address in this study; (2) Methods: A cross-sectional study of healthy volunteers aged 18-65 without lower extremity injury was evaluated using A Samsung RS85 Prestige ultrasound machine with a 14L-2 MHz transducer. Shear wave values were collected in a standardized manner for the patellar tendon at one location, as well as the achilles tendon at the midportion and insertional areas; (3) Results: Data for 54 healthy adult patellar and achilles tendons was collected bilaterally. The mean SWE average of the patellar tendon (P1-P3) for all subjects was 96.3 +/- 10.9 kPA. A higher BMI was correlated with a decrease in patella tendon kPA and the mean PT1 was higher for males compared to females (adjusted mean 96.4 kPA for males and 88.5 kPA for females, mean difference = 7.9 kPA; P = 0.009). The mean SWE average of the insertional Achilles (AT1-AT3) for all subjects was 101.7 +/-16.2 kPA and for the midportion achilles (MTP1-MTP5) was 145.6 +/- 18.8 kPA. There was a decrease in kPA for insertional Achilles measurements in those with a higher BMI, but this did not reach statistical significance (p=.12); (4) Conclusion: This study provides normative SWE values for the patella and achilles tendon using the Samsung RS85. These values can be used for comparison to pathological tissue by means of the same examination protocol to better understand tendon degeneration. Further studies are warranted as to how SWE can be applied to the diagnosis of tendinopathy, partial and full-thickness tears, and response to treatment.

Solid tumors display complex biology and most therapies including chemotherapy cannot prevent therapy resistance and relapse. Most therapeutics target cancer cells, but recent data suggest the presence of cancer stem cells as cells with self-renewal and tumorigenic abilities. Cancer stem cell markers have been suggested to have prognostic value and can be targeted during cancer treatment and in resistant disease. CSCs have been postulated to play significant contextual roles in tumor initiation, progression, therapy resistance and metastasis. CSCs have thus been targeted by new generation cancer drugs. The transcriptional expression of several CSC markers in different cancers was evaluated by searching publicly available The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We report here new findings on expression and prognostic significance of CSC markers in several cancers by examining the expression of CSCs markers in tumor tissues versus the adjacent normal tissues. We found that CSC markers were mostly highly expressed various tumors such as colon, lung, pancreatic and esophageal cancers. No CSC marker is expressed in the same pattern in all cancers and individual CSC marker expression was not linked to patient survival. This analysis calls for continued research on CSCs and clinical evaluation of the CSC markers in relation to prognosis of cancers in large population samples. Novel cancer drugs ought to target CSCs, cancer cells and tumor microenvironment variations.

Aqueous Zinc ion batteries (ZIBs) possess significant potential in large energy storage systems due to their cost-effectiveness and environmental protection. However, the limited voltage window and poor reaction kinetics of the cathode material are still bottlenecks, restricting the further development of ZIBs. In this work, we rationally design Ni-doped V2O5@3D Ni core/shell composite on carbon cloth electrode (Ni-V2O5@3D Ni@CC) by growing Ni-V2O5 on the free-standing 3D Ni metal nanonets for high-voltage and high-capacity ZIBs. Impressively, the embedded Ni doping increases the interlayer spacing of V2O5, extending the working voltage and improving the Zn2+ kinetics reaction of the cathode materials, at the same time the 3D structure with high specific surface area and superior electronic conductivity benefits to fast Zn2+ transport. Consequently, the as-designed Ni-V2O5@3D Ni@CC cathodes can operate within a wide voltage window from 0.3 to 1.8 V vs. Zn/Zn2+ and deliver a high capacity of 270 mAh g-1 at a high current density of 0.8 A g-1. In addition, the reversible Zn2+ (de)-incorporation reaction mechanisms in the Ni-V2O5@3D Ni@CC cathodes are investigated by multiple characterization methods. As a result, we achieve a big step toward the practical applications of commercial ZIBs.

We have determined Hf isotopic compositions of 12 samples associated with the giant El Teniente Cu-Mo deposit, Chile. The samples range in age from ≥8.9 to 2.3 Ma and provide information about the temporal evolution of their magmatic sources from the Late Miocene to Pliocene. Together with previously published data, the new analysis indicate a temporal decrease of 10 εHf(t) units, from +11.6 down to +1.6, in the 12.7 m.y. from 15 to 2.3 Ma. These variations imply increasing incorporation of continental crust through time in the magmas that formed these rocks. The fact that the samples include mantle-derived olivine basalts and olivine lamprophyres suggests that these continental components were incorporated into their mantle source, and not by intra-crustal contamination (MASH). We attribute the increase, between the Middle Miocene and Pliocene, of crustal components in the subarc mantle source below El Teniente to be due to increased subduction erosion and transport of crust into the mantle. The deposit formed above a large, long-lived, vertically zoned magma chamber that developed due to compressive deformation and persisted between the period ~7 to 4.6 Ma. Progressively more hydrous mantle-derived mafic magmas feed this chamber from below, providing heat, H₂O, S and metals, but no unique “fertile” Cu-rich magma was involved in the formation of the deposit. As the volume of these mantle-derived magmas decreased from the Late Miocene into the Pliocene, the chamber crystallized and solidified, producing felsic plutons and large metal-rich magmatic-hydrothermal breccias that emplaced Cu and S into the older (≥8.9 Ma) mafic host rocks of this megabreccia deposit.

Glaucoma poses a serious public health concern as it is the second leading cause of blindness after cataracts, while also being the leading cause of irreversible blindness worldwide. Glaucoma is more prevalent among Black, Latino, and Asian populations and it is essential to examine both racial and ethnic disparities that exist in treatment and prevention. As the number of people with glaucoma continues to grow, it is important to review aspects of this condition in order to better care for those affected. This article will review significant forms of glaucoma and what differentiates them in a diagnosis, treatment options, and recommendations on prevention and improving awareness

(E)-β-ocimene was the only found volatile chemical emitted by whole, live worker larvae of Apis mellifera L. by sampling in the vapor phase. While in addition to (E)-β-ocimene, there is evidence for the existence of other volatiles; but the changes of their composition and contents remain unknown during larval development, as are their differences from larvae to larval food. This is the main purpose of the study. We investigated volatile components of worker larvae and larval food using solid phase dynamic extraction (SPDE) coupled with gas chromatography-mass spectrometry (GC-MS). Nine compounds were identified with certainty and six tentatively, consisting of terpenoids, aldehydes, hydrocarbons, ester and ketone. The contents of volatiles of the second-instar worker larvae differ greatly from larvae of other stages mainly attributable to terpenoids, which made the second-instar worker larvae had significantly higher amounts of overall volatiles. Larval food contained significantly higher amounts of aldehydes and hydrocarbons than the corresponding larvae from the fourth to fifth-instar. We discovered volatiles in worker larvae and their food which were never reported before; we also mastered the change of these volatiles’ contents during larval development.

Early detection of neurological conditions is critical for timely diagnosis and treatment. Identifying cellular-level changes is essential for implementing therapeutic interventions prior to symptomatic disease onset. However, monitoring brain tissue directly through biopsies is invasive and high-risk. Bodily fluids such as blood or cerebrospinal fluid contain information in many forms, including proteins and nucleic acids. Particularly, cell-free DNA (cfDNA) has potential as a versatile neurological biomarker. Yet, our knowledge of cfDNA released by brain tissue and how cfDNA changes in response to deleterious events within the brain is incomplete. Mapping changes in cfDNA to specific cellular events is difficult in vivo, where many tissues contribute to circulating cfDNA. Organoids are tractable systems for examining specific changes consistently in a human background. However, few studies have investigated cfDNA released from organoids. Here, we examined cfDNA isolated from cerebral organoids. We found that cerebral organoids release quantities of cfDNA sufficient for downstream analysis with droplet-digital PCR and whole-genome sequencing. Further, gene ontology analysis of genes aligning with sequenced cfDNA fragments revealed association with terms related to neurodevelopment and autism spectrum disorder. We conclude that cerebral organoids hold promise as tools for the discovery of cfDNA biomarkers related to neurodevelopmental and neurological disorders.

A cuprous oxide (Cu₂O) thin layer served as the base for a non-enzymatic glucose sensor in an alkaline medium, 0.1 NaOH solution, with a linear range of 50-200 mg/dL using differential pulse voltammetry (DPV) measurement. An X-ray photoelectron spectroscopy (XPS) study confirmed the formation of the cuprous oxide layer on the thin gold film sensor prototype. Quantitative detection of glucose in both phosphate-buffered saline (PBS) and undiluted human serum were carried out. Neither ascorbic acid nor uric acid even at a relatively high concentration level of 100mg/dL in serum interfered with the glucose detection, demonstrating the excellent selectivity of this non-enzymatic cuprous oxide thin layer based glucose sensor. Chronoamperometry (CA) and single potential amperometric voltammetry were used to verify the measurements obtained by differential pulse voltammetry (DPV), and the positive results validated that the detection of glucose in a 0.1 M NaOH alkaline medium by DPV measurement was effective. Nickel, platinum and copper are commonly used metals for non-enzymatic glucose detection. The performance of these metal-based sensors for glucose detection using DPV were also evaluated. Cuprous oxide (Cu₂O) thin layer based sensor showed the best sensitivity for glucose detection among the sensors evaluated.

Biomedical devices made from high-modulus and hardness materials play a critical role in enhancing the quality of life for individuals with bone-related ailments. While these materials have been successfully used in orthopedic applications, concerns including stress-shielding have necessitated the exploration of alternative solutions. An ideal biomedical implant requires a delicate balance of mechanical performance, corrosion resistance, tissue biocompatibility, and other properties such as tribological performance and osseointegration. This review explores the suitability of biodegradable magnesium (Mg) alloys as a promising material for biomedical implants. It delves into the essential properties of biomedical implants, emphasizing the importance of matching mechanical characteristics with human bone properties to mitigate stress shielding. The corrosion properties of implant materials are discussed, highlighting the need for controlled degradation to ensure the safety and longevity of implants. The focus then shifts to the potential of magnesium alloys as biomedical implants, examining their benefits, limitations, and the challenges associated with their high degradation rates and less-than-satisfactory mechanical properties. Alloying with elements such as aluminum, zinc, and others is explored to improve magnesium alloys' mechanical performance and corrosion resistance. Furthermore, this review discusses surface modification techniques, including chemical conversion coatings and biomimetic deposition, as effective strategies to enhance the corrosion resistance and biocompatibility of magnesium and its alloys. These modifications offer opportunities to improve the long-term performance of magnesium-based biomedical implants. This paper provides a comprehensive overview of the properties, challenges, and potential solutions associated with biodegradable magnesium alloys as a promising material for biomedical implants. It underscores the importance of addressing issues related to mechanical performance, corrosion resistance, and biocompatibility to advance the development of safe and effective biomedical implant materials.