Transition from basic to higher education is regarded as a ‘juggle of many balls’ because students ought to balance between theoretical and clinical requirements, including social life. As a result, a significant number of undergraduate first-year nursing students succumb to these challenges due to lack of coping mechanisms emanating from poor resilience. As a result, the aim of this study was to determine the factors that influence resilience among undergraduate first-year nursing students at a South African university. A quantitative cross-sectional descriptive research design was followed. Data was collected using an adapted self-administered online questionnaire. Principal component analysis extraction method and Varimax rotation were used to analyse data. A total of 123 participants completed the questionnaire. The eigenvalues showed that the first factor explained 23.9% of the variance, the second factor 11.2%, a third factor 7.5% while the fourth factor explained 5.9% of the variance. The fifth until the eight factor had eigenvalues greater than 1 but less than 2. This study showed that resilience can be dependent on various factors such as lecture support, parental support, academic achievement, peer and mentor support, optimism about the future and being self-determined.

Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. Experimental results show that the strategy we designed can effectively combine the advantages of the linear layer and self-attention mechanisms, compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.

The adapted Posner spatial cue-target paradigm was used to explore the relationship between exogenous temporal attention caused by rhythm and endogenous and exogenous spatial attention. The study was divided into two experiments. In Experiment 1, 33 participants used arrow cues to orient endogenous spatial attention and rhythm to orient exogenous temporal attention. In Experiment 2, with 29 participants, exogenous spatial attention was directed by highlighting the cue box. Exogenous temporal attention was directed by rhythm. Experiment 1 indicated that RTs were faster for attended targets than the unattended. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. RTs were faster for late compared to synchrony. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was not significant. Experiment 2 demonstrated that exogenous spatial attention only exhibited IOR under early four stimulus repetitions. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was significant. The results indicated that rhythmic temporal attention supported foreperiod effects. Exogenous temporal attention and endogenous spatial attention independently affect behavior, while exogenous temporal attention and exogenous spatial attention mutually influenced behavior.

Attention mechanisms have become a fundamental component of deep learning, including the field of computer vision. The key idea behind attention in computer vision is to help the model focus on the relevant spatial regions of the input image, rather than treating all regions equally. The traditional approaches to attention mechanisms in computer vision often suffer from distribution inconsistencies in the attention maps, resulting in sharp transitions that negatively affect model’s focus and lead to poor generalization on complex shapes. The problem of spatial incoherence is particularly pronounced in the task of semantic segmentation, where accurate pixel-level predictions require a detailed understanding of the spatial relationships within the image. In this paper, we propose an attention mechanism called Smooth Attention designed for convolutional neural networks to address the problem of spatial inconsistency in attention maps through multidimensional spatial smoothing. We conduct a series of experiments to evaluate the effectiveness of the proposed mechanism and demonstrate its superior performance compared to traditional methods.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for emotion recognition that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

To evaluate the used and End-of-Life tyre management chains, both direct observation and interviews with approximately 200 used-tyre dealers were conducted. The study revealed that while the used tyre business is well-organized, End-of-Life tyre treatment is nearly non-existent, often involving dumping, open burning for metal recovery, or use in meat processing, causing environmental and health risks. To address these challenges, an 8 t/hr capacity tyre recycling plant is planned, based on estimated tyre availability and development opportunities for sustainable recycling in Togo.

The prospect of drinking water serving as a conduit for gut bacteria, artificially selected by disinfection strategies and lack of monitoring at the point of use, is concerning. Certain opportunistic pathogens, notably some nontuberculous mycobacteria (NTM), often exceed coliform bacteria levels in drinking water, posing safety risks. NTM and other microbiota resist chlorination and thrive in plumbing systems. When inhaled, opportunistic NTM can infect the lungs of immunocompromised or chronically ill patients, and the elderly, primarily postmenopausal women. When ingested with drinking water, NTM often survive stomach acidity, reach the intestines, migrate to other organs using immune cells as vehicles, potentially colonizing tumor tissue, including in breast cancer. The link between the microbiome and cancer is not new, yet the recognition of intratumoral microbiomes is a recent development. Breast cancer risk rises with age, and NTM infections emerged as a concern among breast cancer patients. In addition to studies hinting at a potential association between chronic NTM infections and lung cancer, NTM have also been detected in breast tumors at levels higher than normal adjacent tissue. Evaluating the risks of continued ingestion of contaminated drinking water is paramount, especially given the ability of various bacteria to migrate from the gut to breast tissue via entero-mammary pathways. This underscores a pressing need to revise water safety monitoring guidelines and delve into hormonal factors, which includes addressing the disproportionate impact of NTM infections and breast cancer on women and examining the potential health risks posed by the cryptic and unchecked microbiota from drinking water.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

Managing Water, Energy, and Food (WEF) resources within socio-environmental systems demands innovative strategies that provide decision-makers with actionable insights. This article explores the challenges and potential solutions of employing Focus Maps and choremes as a novel approach for assessing anthropogenic impacts and enhancing the visualization of the WEF Nexus in specific geographic areas. By integrating geospatial data, multi-criteria analysis, and involving stakeholders, our research aims to develop Choreomatic Focus Maps (CFMs) that offer a comprehensive spatial representation of the WEF Nexus, facilitating better-informed decision-making at the community level. Our current work outlines visualization of WEF Nexus, as an initial stages of creating CFMs through a detailed step-by-step process. This includes identifying the main drivers for WEF nexus, such as anthropogenic activities and their distribution, evaluating environmental factors, linking anthropogenic impact with environmental indicators over time, and conducting on-site validation. Our focus is on advancing through these crucial steps, particularly emphasizing field validation within the Local Decision-Makers Tools, visualized here by using Focus Maps. This phase is vital for ensuring our approach is grounded in reality and meets the needs of the communities within the Danube Delta region. By doing so, we aim to provide a visual summary of the WEF Nexus that not only captures the complexity of this fragile ecosystem but also incorporates local insights and priorities, identifying key areas where human activities have significant impacts.

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human keypoint detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of keypoints. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model's robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to re-weight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded data sets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.

Giant unilamellar vesicles (GUVs) are membrane model used to study membrane properties. Electroformation is one of the methods used to produce GUVs. During electroformation protocol dry lipid film is formed. Drying of the lipid film induce Cholesterol (Chol) demixing artifact, in which Chol forms anhydrous crystals, where these crystals does not participate in formation of vesicles. This leads to a lower Chol concentration in the vesicle bilayers compared to the Chol concentration in the initial lipid solution. To address this problem, we propose novel electroformation protocol that includes rapid solvent exchange, plasma cleaning and spin-coating methods to produce GUVs. We tested the protocol focusing on vesicles with high Chol content using different spin-coating durations and vesicle types deposition. Additionally, we compared the novel protocol using completely dry lipid film. The optimal spin-coating duration for vesicles made out of phosphatidylcholine/Chol mixture was 30 s. Mulitlamellar vesicles (MLVs), large unilamellar vesicles (LUVs) obtained by extrusion of MLVs through 100 nm membrane pores and LUVs obtained by extrusion of previously obtained LUVs through 50 nm membrane pores were deposited on electrode for 1.5/1 Chol/phosphatidylcholine lipid mixture and the results were compared. Electroformation using all three deposited vesicle types gave a high GUVs yield, but deposition of LUVs obtained by extrusion of MLVs through 100 nm membrane pores gave the most reproducible results. Using deposition of these LUVs, we produced GUVs with high yield for 6 different Chol concentrations (from 0 % to 71.4%). Using protocol that includes dry lipid film GUVs yield was lower and Chol demixing artifact was induced, proving that the lipid film should never undergo drying when Chol content is high.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% [email protected], which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

Attention deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder characterized by difficulty maintaining attention, impulsivity and hyperactivity. Although the cause of this disorder is still unclear, recent studies state that heredity is important in the development of ADHD. This is linked to a few comorbidities: depression, criminal behavior and anxiety. Although genetic factors play a major role in the pathogenesis of ADHD, there are also non-genetic factors, one of which would be oxidative stress (OS) that are associated with ADHD. OS is implicated in the pathogenesis of ADHD, through lower levels of antioxidant enzymes such as glutathione peroxidase (GPx), catalase (CAT), and superoxide dismutase (SOD), as well as a lower total antioxidant status (TOS) compared to control groups. Moreover, there is a close bidirectional connection between the nervous system and the intestinal microbiota, and the communication between the two systems is carried out through the gut-brain axis (GBA). A thorough review of diet, OS implications, mechanism of action, pharmacokinetics, and drug-drug interactions are reviewed here, with a special focus on treatment in adults with comorbid conditions.

The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a reflection is developed on the dual attention network that applies the learning equations and coordinates subnetworks toward purposeful behaviors. Speculation is made on possible developments of functionalities that could provide additional skills and at the same time shed light on competencies typical of ``natural intelligence".

In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leveraging HybridSN and multilayer perception (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983 with an RMSE of 0.73 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977 with an RMSE of 0.27. These results underscore AM-MHENet’s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. This findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field.

In today’s fast paced, attention-demanding society, executive functions and attentional resources are often taxed. Individuals need ways to sustain and restore these resources. We first review the concepts of attention and restoration, as instantiated in Attention Restoration Theory (ART). ART emphasizes the role of nature in restoring attention. We then discuss essentials of experiments on the causal influences of nature. Next, we expand the concept of ART to include modern, designed environments. We propose a wider perspective termed attentional ecology, in which attention behavior is viewed within a larger system involving the human and their interactions over time with environmental demands. When the ecology is optimal, mental functioning can be a positive “flow” that is productive, sustainable for the individual, and sometimes creative.

Among earth’s microbial populations are the genes, functional capacities, generational memory and sentient cognition that enable microorganisms to adapt and thrive in the full range of ecological conditions found on planet earth. In prior sequential publications, we considered fundamental properties (e.g., use of and responses to sound, light, vibrations) and features of microorganisms that cause them to be critical co-partners of human and other holobionts as well as the potential benefits for humanity that can be gained by fully applying quantum-related and novel capacities of microbial life. In this narrative review we: 1) discuss key concepts concerning a microbially-enhanced and sustainable future, and 2) focus on electric- and magnetic- based features of microorganisms that make them pivotal for myriad benefits ranging from beyond pharma-based health and wellness to free- and/or renewable energy and restorative agriculture to improved human networking. While the benefits are many, the risks posed by hazardous electric or magnetic field exposures to both holobionts and microbial communities are significant. This review concludes that microbes and their remarkable capacities offer both humanity and the planet a much brighter future if we reverse the demonization of microbes and wanton microbiome degradation that has predominated much of the past century.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

Hemicellulose is one of the most important natural polysaccharides in nature. Hemicellulose from different sources varies in chemical composition and structure, which in turn affects the modification effects and industrial applications. Grain and oil by-products (GOBP) are important raw materials for hemicellulose. This article reviews the modification methods of hemicellulose in GOBP. The effects of chemical and physical modification methods on the properties of hemicellulose biomaterials were evaluated. The potential applications of GOBP hemicellulose in films, hydrogel, Three-dimensional (3D) printing and adsorbent are discussed. Future research directions and recommendations are also proposed to provide theoretical foundations and technical support for the efficient utilization of these by-products.

Exosome-based therapy has emerged as a promising strategy for addressing diverse disorders, indicating the need for further exploration of the potential therapeutic effects of the exosome cargos. This study introduces "enhanced exosomes," a novel type of exosomes developed through a novel cell culture system. These specific exosomes may become potent therapeutic agents for treating ovarian disorders. In this study, we conducted a comparative analysis of the protein and miRNA cargo compositions of enhanced exosomes and naïve exosomes. Our findings revealed distinct cargo compositions in enhanced exosomes, featuring upregulated proteins such as EFEMP1, HTRA1, PAM, RSU1, and SDF4, suggesting their potential for treating ovarian disorders. MicroRNA profiling revealed that miR-1-3p, miR-103a-3p, miR-122-5p, miR-1271-5p, miR-133a-3p, miR-184, miR-203a-3p, and miR-206 are key players in regulating ovarian cancer and chemosensitivity by affecting cell cycle progression, cell proliferation, and cell development. We examined polycystic ovary syndrome and premature ovarian insufficiency and identified altered expression of various miRNAs, such as miR-106a-5p, miR-130b-5p, and miR-130b-3p, for diagnostic insights. This study highlights the potential of enhanced exosomes as new therapeutic agents for women's reproductive health, offering a detailed understanding of the impact of their cargo on ovarian disorders.

Antiphospholipid syndrome (APS) otherwise known as Hughes syndrome is a systemic autoimmune disease typically linked with characteristic features like thrombosis and pregnancy morbidity with the presence of antiphospholipid antibodies (aPLs). Although anticoagulation is the current flagship treatment for APS, there are reports indicating a considerable failure following this regimen in a significant number of obstetric APS and thrombotic APS cases. Antiphospholipid antibodies (aPLs) bind anionic phospholipids or protein-phospholipid complexes within the cell membrane. Here, we gave a general overview of the biology of APS and the human gut microbiome, our current understanding of the gut bacteria-immune axis in autoimmunity focus on the potential link to APS, highlighting the implicated microorganisms in this context, and proposing recommendations on future therapeutic development.

The global increase in population has placed significant pressure on food security, leading to the emergence of aquaculture as a vital source of aquatic foods. However, rising costs and the limited availability of fishmeal in aquafeeds have spurred the search for alternative protein sources. While plant-based ingredients have been integrated into commercial aquafeeds, they come with challenges, including low protein content, palatability issues, and the presence of antinutritional factors. In this context, fish silage, derived from fish waste and discarded fish, stands out as a promising alternative technology due to its cost-effectiveness and sustainability attributes. The production of fish silage involves the addition of organic/inorganic acids or lactic acid bacteria to homogenized fish waste. This process yields a valuable mixture rich in peptides, free amino acids, and partially hydrolyzed proteins, offering significant nutritional benefits for animal diets. Beyond its nutritional advantages, fish silage contributes to environmental sustainability by reducing the impact of waste disposal and enhancing the value of fish waste. The objective of this review is to promote sustainable practices in the aquaculture industry in response to the increasing demand for high-quality feed nutrients. To achieve this, it includes an analysis of research results related to silage technology, an appraisal of the advantages and disadvantages of using fish silage as a feed additive, and a focus on emerging trends in this field.

Axial resolution is one of the most important characteristics of a microscope. In all microscopes, a high axial resolution is desired in order to discriminate information efficiently along the longitudinal direction. However, when studying thick samples that do not contain laterally overlapping information, a low axial resolution is desirable as information from multiple planes can be recorded simultaneously from a single camera shot instead of plane-by-plane mechanical refocusing. In this study, we increased the focal depth of an infrared microscope non-invasively by introducing a binary axicon fabricated on barium fluoride substrate close to the sample. Preliminary results of imaging thick and sparse silk fibers showed an improved focal depth with a slight decrease in lateral resolution and increase in background noise.

This article seeks to provide an improved and more comprehensive understanding of the concept and theories on wasteland. It achieves this by focusing on the Indian context allowing us to unpack the importance of including multiple perspectives of wasteland narratives; this means including more positive narratives of the potential of wasteland to inform and improve prospects for land policies in the global south. Wasteland is commonly recognized as an underutilized category of land that may transform into a valuable resource base with proper management measures. The term waste has multiple angles that carry different notions ranging from fallow to agroforestry land in the global south and Brownfield to green space in the global north. We conduct a narra-tive review approach to qualitatively analyze the concept wastelands studied in pre-existing lit-erature from 1970 to present. The unsystematic literature review approach incorporates multiple elements of wasteland discourse, like understanding the meaning of the term on a global scale, setting out the meaning of the term waste into multiple perspectives explicitly in the Indian con-text, along with different classes and management approaches of wasteland from a national per-spective. The multiple perspectives of wasteland not only generate misconception on land resource but spawn difficulties in land use policy particularly for Indian scenario. For sustainable land use policy, reclaiming wasteland would be the best possible way for India and other countries in Global South, which requires a comprehensive methodological overview on wasteland narrative.

Recently, attention mechanisms have developed into an important tool for performance improvement of deep neural networks. In computer vision, attention mechanisms are generally divided into two main branches: spatial and channel attention. Both attention categories have their own advantages. The fusion of both attentions achieves higher performance, on the cost of the computational load. This paper introduces an innovative and lighter n-shifted sigmoid channel and spatial attention (CSA) module to reduce the computational cost and to improve the 3D scene relevant features selection. To validate the proposed attention module, 3D scene object detection in the deep Hough voting point sets is considered as the testing application. The proposed attention module with its piecewise n-shifted sigmoid activation function improves the network’s learning and generalization capacity which effectively predict bounding box parameters directly from 3D scenes and detect objects more accurately. This advantage is achieved by selectively attending to more relevant features of the input data. When used in the deep Hough voting point sets, the proposed attention module outperforms state-of-the-art 3D detection methods on the sizable SUNRGBD dataset. Experiments conducted showed an increase of 12.02 mean accuracy precision (mAP) when compared to the celebrated VoteNet (without attention). It also got 9.92 mAP higher compared to the MLVCNet, and 10.32 mAP higher than the Point Transformer. The proposed model not only decreases the sigmoid vanishing gradient problem but also brings out valuable features by fusing channel-wise and spatial information while improving accuracy results in 3D object detection.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

This publication aims to create 4K UHD video sequences with a specific focus on smart cities and transport. The created database will be accessible to all experts in the field and can be further used for research purposes. All reference video sequences will be transcoded into different quality settings. The resolution, compression standard, and bitrate will be changed. The work will aim to find out the best combination of video parameters from the measured values. Thanks to the overall evaluation, we will ensure for each single video sequence the quality with the best experience for the providers that will offer the service. The database will also be available with its objective and subjective ratings, with dynamicity information characterized by spatial and temporal information. After, It will be possible to compare the results of the subjective evaluation of a selected sample of our respondents with the work of other researchers who can evaluate with a different sample of evaluators.

Addressing real-time aircraft target detection in microsatellite-based visible light remote sensing video imaging requires considering the limitations of imaging payload resolution, complex ground backgrounds, and the relative positional changes between the platform and aircraft. These factors lead to multi-scale variations in aircraft targets, making high-precision real-time detection of small targets in complex backgrounds a significant challenge for the detection algorithms. Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the YOLOv7 framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) with a lightweight convolutional SEConv to reduce the computational parameters and accelerate the detection process of small aircraft targets, thus enhancing real-time onboard processing capabilities. Besides, the SEConv-based SPPCSPC (Spatial Pyramid Pooling and Connected Spatial Pyramid Convolution) module extracts image features. It improves detection accuracy while the feature fusion section integrates the CBAM hybrid attention network, forming the CBAMCAT module. Furthermore, it optimizes small aircraft target features in channel and spatial dimensions, improving the model's feature fusion capabilities. Experiments on public remote sensing datasets reveal the proposed SE-CBAM-YOLOv7 improves detection accuracy by 3% and mAP value by 4.2% compared to YOLOv7, significantly enhancing the detection capability for small-sized aircraft targets in satellite remote sensing imaging.

With the development of economy, maritime transportation has increased rapidly, which has had a significant impact on the marine environment and resources. In recent years, Synthetic Aperture Radar (SAR) has been widely used to observe maritime environmental incident. Through the large coverage area of SAR, marine environmental emergencies, especially oil pollution, can be better monitored and responded to. This study first proposed an improved Full-scale Aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas. The convolutional block attention module (CBAM) in FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model’s overconfidence in majority class samples while improving the model’s generalization ability. The detection performance of the improved FA-MobileUNet model reached 84.55% mIoU (mean Intersection over Union), which was 17.15% higher than that of the original U-Net model. The improved FA-MobileUNet model was then examined by some oil pollution incidents. According to the detection results, if the oil spill was suspected to be caused by a ship, the corresponding Automatic Identification System (AIS) data were collected based on the acquisition time and geo-location of the SAR image to search for the ship suspected of causing oil pollution. Finally, some oil pollution incidents in Taiwan marine area were collected to verify the effectiveness of the proposed model for marine oil pollution monitoring. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.

Background: Metastatic tumors to the oro-facial tissues are rare, with an incidence ranging between 1% and 8% of all oral malignant tumors. Generally reported with a peak of incidence in the 5th–7th decades, but possibly occurring at any age, metastases may represent the first sign of an occult cancer or manifest in patient with an already known history of a primary carcinoma, mostly from the lungs, kidney, prostate, and colon-rectum in males, or uterus, breast, lung, and ovary in females. In the oro-facial tissues the sites mostly involved are the oral mucosa, gingiva/jawbones, tongue, and the salivary glands. Methods: A systemic Review of the literature on the oro-facial metastases from Renal Cell Carcinoma has been conducted by searching the most used databases, also focusing the attention on the Clear Cell histological variant, representing the most frequent one. Results: Among the 160 analyzed studies, 211 cases of oral metastases of renal cancer were found (148 in males, 70.1%; 63 in females, 29.8%), with an average age of 59.8 years. In almost 40% of cases, metastasis represented the first clinical manifestation of the primary tumor and 122 were histologically diagnosed as ccRCCs (57.8%). The tongue was involved in most of the cases (26% - 55 cases), followed by mandible (18.9% - 40 cases), gingiva (18.4% - 39 cases), maxilla (10.9% - 23 cases), parotid gland (10.4% - 22 cases), buccal mucosa (5.2% - 11 cases), lips (3.3% - 7 cases), hard palate (2.8% - 6 cases), soft palate, masticatory space and submandibular gland (0.9% - 2 cases), lymph nodes, tonsil and oral floor (0.4% - 1 case). Among the 122 ccRCCs (84 were males, 68.8 %; 38 females, 31.1%), with an average age of 60.8 years and representing in 33.6% the first clinical manifestation, the tongue remained the most involved site (25.4% - 31 cases), followed by the gingiva (17.2% - 21 cases), parotid gland (13,1% - 16 cases), mandibular bone (12.2% - 15 cases), maxillary bone (11.4% - 14 cases), buccal mucosa and lips (4.9% - 6 cases), hard palate (4% - 5 cases), submandibular gland and soft palate (1.6% - 2 cases), lymph nodes, tonsil, oral floor and masticatory space (0.8%. 1 case). The clinical presentation in soft tissues was represented by a fast-growing exophytic mass, sometimes accompanied by pain. Instead inn bone appeared as radiolucent lesions, with ill-defined borders and cortical erosion. Conclusions: Our data focus the attention on the incidence of Renal cancer Metastases in the oro-facial tissues. Their early identification surely represents a diagnostic challenge, especially when the clinical work-up is limited to the cervico-facial region. Nevertheless, early diagnosis and recently introduced adjuvant therapies may represent the key for better outcomes. Therefore, general guidelines about clinical and radiological identification of oro-facial potentially malignant lesions should be part of the cultural background of any dentists.

The purposes of this study were to identify clinician’s characteristics associated with higher prescription rates of long-acting injectable (LAI) antipsychotics, as well as the information sources influencing medical decision-making about treatment of schizophrenia. We surveyed 202 psychiatrists during 6 regional French conferences (Bordeaux, Lyon, Marseille, Nice, Paris, Strasbourg). Data on the characteristics of practice, prescription rates of antipsychotic and information sources about their clinical decisions were collected. Most of psychiatrists used second-generation antipsychotic (SGA), and preferentially an oral formulation, in the treatment of schizophrenia. SGA LAI was prescribed to 30.4% of schizophrenic patients. The duration and the type of practice did not influence the class or formulation of antipsychotics used. The clinicians following the higher percentage of schizophrenic patients were associated with the higher use of LAI antipsychotics and the lower use of oral SGA. Personal experience, government regulatory approval and guidelines for the treatment of schizophrenia were the 3 main contributing factors guiding the clinical decision-making of clinicians about treatment of schizophrenia. The more clinicians follow schizophrenic patients, the more they use LAI antipsychotic. The development of specialised programmes with top specialists should lead to better use of LAI in the treatment of schizophrenia.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

The coexistence of SAH with T2DM is a common comorbidity. In this present study, we investigated the link between altered plasma antioxidant trace elements (ATE: Manganese, Selenium, Zinc and Copper) and fatty acids ratio (FAR: polyunsaturated/saturated) imbalance as transition biomarkers between vascular pathology (SAH) to metabolic pathology (T2DM). Our data revealed strong correlation between plasma ATE and FAR profile which is modified during SAH-T2DM association compared to healthy group. This relationship is mediated by lipotoxicity (simultaneously prominent visceral adipose tissue lipolysis, significant flow of non-esterified free fatty acids release, TG-Chol-dyslipidemia, high association of Total SFA, Palmitic acid, Arachidonic acid and PUFA 6/ PUFA 3; drop in tandem of PUFA / SFA and EPA+DHA); oxidative stress (lipid peroxidation confirmed by TAS depletion and MDA raise, concurrently drop of Zn/Cu-SOD, GPx, GSH, Se, Zn, Se/Mn, Zn/Cu; concomitantly enhance Cu, Mn and Fe); endothelial dysfunction (endotheline-1 increase); athero-thrombogenesis risk (concomitantly raise of ApoB100/ApoA1, Ox-LDL, tHcy and Lp(a)) and inflammation (higher of Hs-CRP, Fibrinogen and Ferritin). Our study open to new therapeutic targets and to better dietary management, such as to establish dietary ATE and PUFA 6 / PUFA 3 or PUFA / SFA reference values for atherosclerotic risk prevention in hypertensive-diabetics patients.

This investigation pioneers an examination of the scarcely explored terrain of social diversity in medical education, assessing the complex impacts of socioeconomic status (SES), prior working experiences, and completed apprenticeships on a spectrum of psychological and academic facets among first-year medical students (n = 336) in an urban university setting. By utilizing a systematic and detailed approach, the study illuminates prior anatomical knowledge and various psycholog-ical constructs, marking a significant stride into a domain where knowledge remains profoundly limited. Analyses indicated that SES significantly affects financial anxiety (F(4, 331) = 17.391, p < .001) and academic behavioral confidence (F(4, 331) = 4.323, p = 0.002). Students with prior working experience reported higher self-perceived clinical experience but lower A-level grades (t(279.459) = -6.690, p < .001), competence in natural sciences (t(321.045) = -3.178, p = 0.002), and online competence (t(319.429) = -2.026, p = 0.044). Those who completed an apprenticeship showed higher resilience, academic confidence, self-efficacy, and greater concerns about balancing studies and work (t values ranging from 2.020 to 3.158, p < 0.05). Correlation analysis revealed a positive relationship between resilience (RQS) and coping with academic stress (CAS) (r = 0.632, p < .001), as well as between resilience and academic behavioral confidence (ABC) (r = 0.608, p < .001). Negative correlations were observed between resilience, coping with academic stress, academic confidence, and cognitive test anxiety (CTA) (r values from -0.235 to -0.404, p < .001). The findings emphasize the need for tailored support for students from diverse SES backgrounds and with varying expe-riences. The study highlights the value of a comprehensive approach in medical education, con-sidering the diverse backgrounds and experiences of students. Future research should explore the long-term impact of these factors on professional competencies and patient care, leveraging the diversity of the student body for a holistic educational experience.

One of the most crucial elements in deeply understanding humans on a psychological level is manifested through facial expressions. The analysis of a human behavior can be informed by their facial expressions, making it essential to employ indicators such as expression (Expr), valence-arousal (VA), and action units (AU). In this paper, we introduce the method proposed in the Challenge of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) at CVPR 2024. Our proposed method utilizes the multi-modal Aff-wild2 dataset, which is splitted into spatial and audio modalities. For the spatial data, we extract features using a SimMiM model that was pre-trained on a diverse set of facial expression data. For the audio data, we extract features using a WAV2VEC model. To fusion the extracted spatial and audio features, we employed the cascaded cross-attention mechanism of a transformer.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

Emotion detection holds significant importance in facilitating human-computer interaction, enhancing the depth of engagement. By integrating this capability, we pave the way for forthcoming AI technologies to possess a blend of cognitive and emotional understanding, bridging the divide between machine functionality and human emotional complexity. This progress has the potential to reshape how machines perceive and respond to human emotions, ushering in an era of empathetic and intuitive artificial systems. This paper introduces a novel approach to multimodal emotion recognition, seamlessly integrating speech and text modalities to accurately infer emotional states. Employing CNNs, we meticulously analyze speech using Mel spectrograms, while a BERT-based model processes the textual component, leveraging its bidirectional layers for profound semantic comprehension. The outputs from both modalities are combined using an attention-based fusion mechanism that optimally weighs their contributions. The proposed method undergoes meticulous testing on two distinct datasets: Carnegie Mellon University’s Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset and the Multimodal Emotion Lines Dataset (MELD). The results demonstrate superior efficacy compared to existing frameworks, achieving an accuracy of 88.4% and an F1-score of 87.9% on the CMU-MOSEI dataset, and a notable weighted accuracy (WA) of 67.81% and a weighted F1 (WF1) score of 66.32% on the MELD dataset. This comprehensive system offers precise emotion detection and introduces several significant advancements in the field.

The neural system has limited resources to interact with the environment, and this is where attention comes into play. Attention is a complex process that provides numerous benefits, but there has been a longstanding debate about the mechanisms involved in attentional orientation. Two widely proposed mechanisms are top-down and bottom-up, which relate to the organism's current behavioral goal and the salience of environmental stimuli, respectively. However, distinguishing between these mechanisms can be challenging in both behavioral and neural studies. This review introduces a new conceptualization in which both top-down and bottom-up mechanisms are viewed as aspects of the same process. This unified approach aims to prepare the organism to face stimuli and events based on its prior experiences, immediate (the current task that he/she is aware of), and innate (to be alive) demands.

Transformer models have emerged as frontrunners in the field of natural language processing, primarily due to their adept use of self-attention mechanisms to grasp the semantic linkages between words in sequences. Despite their strengths, these models often face challenges in single-task learning scenarios, particularly when it comes to delivering top-notch performance and crafting strong latent feature representations. This challenge is more pronounced in the context of smaller datasets and is particularly acute for under-resourced languages such as Arabic. In light of these challenges, this study introduces a novel methodology for text classification of Arabic texts. This method harnesses the newly developed Reverse Positional Encoding (RPE) technique. It adopts inductive-transfer learning (ITL) framework combined with a switching self-attention shared encoder, thereby increasing the model's adaptability and improving its sentence representation accuracy. The integration of Mixture of Experts (MoE) and Reverse Positional Encoding (RPE) techniques empowers the model to process longer sequences more effectively. This enhancement is notably beneficial for Arabic text classification, adeptly supporting both the intricate five-point and the simpler ternary classification tasks. The empirical evidence points to its outstanding performance, achieving accuracy rates of 87.20% for the HARD dataset, 72.17% for the BRAD dataset, and 86.89% for the LABR dataset, as evidenced by the assessments conducted on these datasets.

Background: Accurate sales forecasting in the mobile phone industry is critical to informed decision making by both the government and corporations. This study seeks to enhance mobile phone sales predictions by incorporating brand exposure as a predictive factor, hence addressing the gap left by the existing forecasting models that do not consider the impact of brand awareness. Method: This study thereby indicates a quantification method for brand exposure that merges word-of-mouth reviews with online search data. Word2Vec is used to extract initial keywords from a word-of-mouth corpus, whereas time-lag correlation analysis refines the core keywords. Later, PCA synthesizes brand exposure. The attention-LSTM framework hereby develops a forecasting model of mobile phone sales, effectively combining long short-term memory networks with the ability to capture time series dependencies and an attention mechanism to reduce data redundancy. Results: By considering brand exposure, the experiments demonstrate that the attention-LSTM model decreases the RMSE and MAPE indicators by 2.02% and 0.96%, respectively. In addition, compared with the ARIMA, SVR, BP neural network, and LSTM models, the attention-LSTM model decreases the average percentage errors by 6.52%, 3.42%, 2.56%, and 0.81%, respectively. Conclusion: Attention-LSTM captures dynamic sales trends very well and holds great significance over traditional models. Overall, including brand exposure, this model is very strong for the interpretation of mobile phone sales predictions, which has important implications in terms of policy as well as corporate strategies.

The objective was to investigate associations of serum vitamin D concentration with depressive symptoms and assess the impact vitamin D concentration has on the occurrence of depressive symptoms in 20–44-year-old pregnant mothers, postpartum mothers, women (non-pregnant/postpartum women), and men, including a separate subgroup analysis of postpartum breastfeeding and non-breastfeeding mothers. The study populations were selected from 2007-2018 NHANES public data. Subjective interview data and objective laboratory data, including depressive symptoms, serum vitamin D concentration, nutrient intake, and demographic information were utilized. Two diet patterns were created using principal component analysis and a Bayesian multinomial model was fit to predict depression outcomes for each subpopulation. The estimates for the log vitamin D slope parameter were negative for all cohorts; as vitamin D increased, the probability of having no depression increased, while the probability of depression decreased. The pregnant cohort had the steepest vitamin D slope, followed by postpartum, then other women and men. Higher vitamin D concentration had more impact on decreasing depression risk in pregnant and postpartum mothers compared to other women and men. Among postpartum mothers, higher vitamin D concentration had a greater influence on decreasing breastfeeding mothers' depression risk than non-breastfeeding mothers.

Clustered regularly interspaced short palindromic repeats (CRISPR) based gene-editing has begun to transform the treatment landscape of genetic diseases. The history of the discovery of CRISPR/CRISPR-associated (Cas) proteins/single guide RNA (sgRNA)-based gene-editing since the first report of repetitive sequences of unknown significance in 1987 is fascinating, instructive, and inspiring for future advances. The recent approval of CRISPR-Cas9-based gene therapy to treat patients with severe sickle cell anemia and transfusion-dependent beta thalassemia has renewed hope for treating other hematologic diseases, including patients with germline predisposition to hematologic malignancies, who would benefit greatly from the development of CRISPR-based gene therapies. The purpose of this manuscript is three-fold: first, a chronological description of the history of CRISPR-Cas9-sgRNA-based gene editing; second, a brief description of the current state of clinical research in hematologic diseases, including selected applications in treating hematologic diseases with CRISPR-based gene therapy; and third, the current progress in gene therapies in inherited hematologic diseases and bone marrow failure syndromes, to hopefully stimulate efforts towards developing these therapies for patients with inherited bone marrow failure syndromes and other inherited conditions with germline predisposition to hematologic malignancies.

Making sustainable business models, especially in manufacturing, is a key approach for organizations to adopt in order to create more sustainable processes. New technologies are needed to implement new policies that can minimize the economic, environmental, and social consequences of industrial activity. Sustainable maintenance is decisive to guarantee the availability, reliability, and safety of assets, surpassing traditional maintenance policies and embracing the principles of the circular economy. The main objective of Maintenance 4.0 is to increase the lifespan and availability of the equipment by preventing unplanned downtime, reducing planned shutdowns, and improving safety. This research focuses on the integration of advanced Industry 4.0 tools with conventional monitoring and control techniques in process industries. The article suggests that while complex neural networks might provide good predictions, simpler solutions can generalize well and have lower prediction errors because they can capture the underlying relationships between variables in a specific asset better. The combination of maintenance function, Industry 4.0 tools, and advanced statistical solutions to obtain sustainable processes can facilitate organizational change and aid decision-making in equipment management. These solutions seek to reduce interruptions and expenses through the unnecessary utilization of labor and spare parts. All these considerations improve communication and data processing, resulting in improved maintenance of a hydrogen compressor in the petrochemical industry.

Post-acute COVID-19 vaccination syndrome (PACVS) is distinguished from the normal post-vaccination state by altered receptor antibodies (Semmler et al. Vaccines 2023, 11, 1642). Here, we explore the symptoms registry and standard serum markers acquired in the above study to delineate the disease phenotype of PACVS. Symptoms reported by the participants overlapped with established complex dysautonomia syndromes (ME/CFS, POTS, MCAS, SFN). However, many participants were qualified for several (131/191) or none (31/191) of these syndromes. Unbiased clustering (modified Jaccard index) revealed a prevalent cross-cohort symptomatology of malaise and chronic fatigue (&gt; 80% of cases). Overlapping clusters of (i) peripheral nerve dysfunction, dysesthesia, motor weakness, pain, vasomotor dysfunction, (ii) cardiovascular impairment, and (iii) cognitive impairment, headache, optic, oculo-motoric and acoustic dysfunctions were also frequently represented. Conspicuous serum markers encompassed increases in interleukins 6 and 8 and low fT3 levels (&gt; 80%), IgG-subclass imbalances and impaired iron-storage (&gt; 50%), and increases in sNFL (&gt; 30%). Serum markers were not correlated to specific symptoms or symptom clusters. A single and unique cause (i. e. SARS-CoV-2 vaccination) is improbable to trigger several distinct etiologies. Therefore, the symptoms and serological features observed in our study cohort probably represent facets of a single complex syndrome.

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes 1 from a single image has long been a challenging task. While deep learning networks enable us to 2 approximate the shape of unseen sides, capturing the texture details of the non-visible side remains 3 difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) 4 to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles 5 on the model’s surface. However, we have identified challenges with existing normal prediction 6 networks when dealing with complex scenes, such as a lack of focus on local features and insufficient 7 modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced 8 Multi-scale Attention-driven Single Image 3D Human Reconstruction. This approach incorporates 9 a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate 10 features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention 11 mechanisms by adaptively adjusting the weights between features, enabling the network to more 12 effectively leverage information across various scales.Furthermore, we have improved the feature 13 fusion method to better integrate representations from different scales. This enhanced feature fusion 14 allows the network to more comprehensively understand both fine details and global structures 15 within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention 16 mechanism and feature fusion method, optimizing the network’s training process and enhancing the 17 quality of reconstruction results.Our network demonstrates significant improvements in performance 18 for 3D human model reconstruction. Experimental results show that our method exhibits greater 19 robustness to challenging poses compared to traditional single-scale approaches.

Loss of function of the hippocampus or frontal cortex is associated with reduced performance on memory tasks in which subjects are incidentally exposed to cues at specific places in the environment and are subsequently asked to recollect the location at which the cue was experienced. Here, we examined the roles of the rodent hippocampus and frontal cortex in cue-directed attention during encoding of memory for the location of a single incidentally experienced cue. During a spatial sensory preconditioning task, rats explored an elevated platform while an auditory cue was incidentally presented at one corner. The opposite corner acted as an unpaired control location. The rats demonstrated recollection of location by avoiding the paired corner after the auditory cue was in turn paired with shock. Damage to either the dorsal hippocampus or the frontal cortex impaired this memory ability. However, we also found that hippocampal lesions enhanced attention directed towards the cue during the encoding phase while frontal cortical lesions reduced cue-directed attention. These results suggest that the deficit in spatial sensory preconditioning caused by frontal cortical damage may be mediated by inattention to the location of cues during the latent encoding phase, while deficits following hippocampal damage must be related to other mechanisms such as generation of neural plasticity.

The COVID-19 pandemic crisis has brought about unprecedented economic and social consequences worldwide. The implementation of isolation measures, including national and international travel restrictions, significantly affected contact-intensive sectors such as tourism. During this lockdown period, Information and Communication Technology (ICT) was crucial in maintaining connectivity and enabling remote activities. Governments across the world implemented discretionary measures to mitigate the crisis's adverse effects on economic activity. This paper aims to analyze whether the level of ICT adoption and the importance of tourism influence the fiscal policy efforts pursued by governments. Based on the results of a linear regression analysis for 154 countries, the findings suggest that in countries with higher international tourism receipts, the governments had to promote higher fiscal stimulus. At the same time, ICT development diminished the efforts of the fiscal policy response to the crisis since it facilitated economic resilience. The findings of this study can contribute to understanding how governments responded to the pandemic crisis and the factors that influenced their fiscal policy decisions.

Our study is part of the ongoing discussion concerning influencer marketing as there seems to have been a shift and social media marketing is bigger than ever. Social media outlets are excellent vehicles for fostering relationships with consumers who can be susceptible to influencer advertising. Therefore, companies now pay for posts on users’ profiles to generate popularity for products. How this approach affects consumers’ buying behavior is an interesting research question. We reference Herbert Simon’s economics of attention as an appropriate theoretical construct for describing the reality of the internet. Based on unique survey data from 513 Polish respondents, the study applies econometric methods and supervised and unsupervised machine learning techniques to identify the primary consumer groups with different characteristics of engagement and sensitivity to influencer activities, revealing the possible drivers of purchase intent. The findings provide valuable insights into consumers’ informational preferences, demonstrating how social media influencers may attract attention and drive sales.

I present the first complete theory of ADHD. ADHD occurs when the organism adapts to try to maintain higher glucose levels than normal. The main mechanism via which this is done is by faster opening of KATP channels, mainly Kir6.2/SUR1, which increases the cell's intrinsic \xtblty. I show that this induces all of the symptoms of ADHD. The optimal treatment for ADHD already exists, since drugs of the sulfonylurea family close KATP channels and have been used for safely treating people for dozens of years.

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.

Polypharmacy, the use of multiple medications, is a prevalent issue globally that contributes to increased healthcare costs and places additional burdens on patients' organs. This study aims to reduce the incidence of polypharmacy in the emergency department. We conducted a retrospective review of randomly selected medical records from the Internal Medicine Department of Taipei MacKay Emergency Department, spanning from August 1, 2023, to October 31, 2023. For cases identified as involving polypharmacy, pharmacists provided medication education using the TRM Polypharmacy Interview Guide, while social workers contacted patients via phone to recommend follow-up visits for deprescribing. Patients experiencing polypharmacy were significantly older than those on appropriate medication regimens (79.8 vs. 67.3 years, p=0.002). After deprescribing interventions, the average number of medications for polypharmacy patients was 5.9 higher than for those receiving appropriate prescriptions (9.2 vs. 3.3, p=0.001). The most common conditions associated with polypharmacy were diabetes mellitus, hypertension, and arrhythmia. Polypharmacy poses a significant medication management challenge, with affected patients taking an average of 6.9 more medications than those on appropriate treatment regimens. Our team’s resource management module successfully reduced the incidence of polypharmacy by 8.4% in the emergency department.

In this study, we aimed to explore the oral bacteria and fungi that can help discern oral squamous cell carcinoma (OSCC) and investigate the correlations between multiple key pathogens. Methods: Twelve participants (8 females and 4 males; mean age, 54.33 ± 20.65 years) were prospectively recruited into three groups: Group 1: healthy control, Group 2: patients with stomatitis, and Group 3: patients with OSCC, with 4 individuals in each group. Unstimulated whole saliva samples from these participants were analyzed using real-time PCR to assess the presence and abundance of 14 major oral bacterial species and Candida albicans. Results: The analysis revealed significant differences for certain microorganisms, namely Treponema denticola (T. denticola), Lactobacillus casei (L. casei), and Candida albicans. T. denticola was most abundant in the OSCC group (5,358,692.95 ± 3,540,767.33), compared to the stomatitis (123,355.54 ± 197,490.86) and healthy control (9,999.21 ± 11,998.40) groups. L. casei was undetectable in the healthy control group, but was significantly more abundant in the stomatitis group (1,653.94 ± 2,981.98) and even higher in the OSCC group (21,336.95 ± 9,258.79) (p = 0.001). A similar trend was observed for C. albicans, with DNA copy numbers rising from the healthy control (464.29 ± 716.76) to the stomatitis (1,861.30 ± 1,206.15) to the OSCC group (9,347.98 ± 5,128.54) (p = 0.006). The amount of T. denticola was positively correlated with L. casei (r=0.890, p<0.001) and C. albicans (r=0.724, p=0.008). L. casei's DNA copy number was strongly correlated with C. albicans (r=0.931, p<0.001). These three oral microbes exhibited strong positive correlations with each other and had various direct or indirect relationships with other species. Conclusions: In the OSCC group, T. denticola, L. casei, and C. albicans exhibited strong positive correlations with one another, further emphasizing the need for a deeper understanding of the complex microbial interactions in the OSCC environment.

The detection accuracy of bridge superficial defect using the deep neural network approach decreases significantly under light variation and weak texture condition. To address these issues, an enhanced intelligent detection method based on the YOLOv8 deep neural network is proposed in this study. Firstly, multi-branch coordinate attention (MBCA) is proposed to improve the accuracy of coordinate positioning by introducing a global perception module in coordinate attention mechanism. Furthermore, a deformable convolution based on MBCA is developed to improve the adaptability for complex feature shapes. Lastly, the deformable convolutional network-attention-YOLO (DAC-YOLO) detection algorithm is formed by replacing the deep C2F structure in the YOLOv8 architecture with deformable convolution. A supervised dataset consisting of 4794 bridge surface damage images is employed to verify the proposed method, and results show that it achieves improvements of 2.0% and 3.4% in mAP and R. Meanwhile, the model complexity decreases by 1.2G, increasing the detection speed by 3.5/f·s-1.

Focus Group Discussions (FGDs) are widely employed in qualitative research to delve into complex social issues, yet their application in conservation studies lacks standardised guidelines and thorough evaluation. This review assesses the utilisation of FGDs in biodiversity and conservation research from 2000 to April 2022, identifying 170 pertinent articles, primarily published between 2015 and 2022. FGDs, often integrated with other methodologies, predominantly sought to understand community perspectives, evaluate conservation practices, explore challenges, and document indigenous knowledge. Despite their extensive use, methodological intricacies were frequently underreported. This paper underscores the necessity for enhanced reporting standards and comprehensive guidelines to optimise the effectiveness and reliability of FGDs in conservation research. By means of critically investigating existing literature, this study contributes critical important insights into the strengths and limitations of FGDs, ultimately advocating for greater methodological rigour and transparency in their application within the realm of conservation science.

Cardiomyopathies (CMPs) are a group of myocardial disorders that are characterized by structural and functional abnormalities of the heart muscle. These abnormalities occur in the absence of coronary artery disease (CAD), hypertension, valvular disease, and congenital heart disease. CMPs are an increasingly important topic in the field of cardiovascular diseases due to the complexity of their diagnosis and management. In 2023, the ESC guidelines on cardiomyopathies were first published, marking significant progress in the field. The growth of techniques such as cardiac magnetic resonance imaging (CMR) and genetics has been fueled by the development of multimodal imaging approaches. For the diagnosis of CMPs, a multimodal imaging approach, including CMR, is recommended. CMR has become the standard for non-invasive analysis of cardiac morphology and myocardial function. This document provides an overview of the role of CMR in CMPs, with a focus on tissue mapping. CMR enables the characterization of myocardial tissues and the assessment of cardiac functions. CMR sequences and techniques, such as late gadolinium enhancement (LGE) and parametric mapping, provide detailed information on tissue composition, fibrosis, edema, and myocardial perfusion. These techniques offer valuable insights for early diagnosis, prognostic evaluation, and therapeutic guidance of CMPs. The use of quantitative CMR markers enables personalized treatment plans, improving overall patient outcomes. This review aims to serve as a guide for the use of these new tools in clinical practice.

The genus Strophantus belongs to the Apocynaceae family of flowering plants which grows primarily in tropical Africa. The plants are widely used in traditional herbal medicine. S. sarmentosus, in particular, is used for the treatment of, e.g., joint pain and rheumatoid arthritis, wound infections, head lice, diarrhoea, snake bite, and eye conditions. Despite the widespread use, dedicated research characterizing bioactive plant components is scarce. Investigations focussed mainly on the cardenolides, because of their cardioactivity and historical use as cardiotonic. There are also studies concerning the antibacterial, antioxidant and anti-inflammatory activity of plant extracts. This review summarizes the present knowledge about the biochemical and analytical reserach on Strophantus, in general, and S. sarmentosus, in particular, and describes the current state-of-the-field based on the available sientific literature.

Aims and Background: People living with mental disorders experience a disproportionately higher burden of tobacco-related disease than the general population. Long-term substitution with less harmful nicotine products could reduce the tobacco-related harm among this population. This study investigated the views and preferences of people with mental health disorders about different nicotine products and their use as long-term substitutes for cigarettes. Methods: Semi-structured focus group discussion followed by a brief questionnaire. The discussion transcripts were analysed for content and themes and quantitative data summarised with descriptive statistics. Results: Twenty-nine participants took part in four focus groups. Vaping devices were the most acceptable nicotine products discussed, however preferences for nicotine products were idiosyncratic and varied along aesthetic, pragmatic, sensory and symbolic dimensions. The concept of tobacco harm reduction was unfamiliar to participants, however they generally agreed with the logic of replacing cigarettes with less harmful nicotine products. Barriers to activating tobacco harm reduction included the symbolism of smoking and quitting; the importance placed on health; the consumer appeal of alternatives; and cost implications. Discussion and Conclusion: Engaging this population in tobacco harm reduction options will require communication that challenges black and white thinking (a conceptual framework in which smoking cigarettes or quitting all nicotine are the only legitimate options) as in practice this serves to support the continuance of smoking. Consumers should be encouraged to trial a range of nicotine products to find the most acceptable alternative to smoking that reduces health harms. Providing incentives to switch to nicotine products could help overcome barriers to using less harmful nicotine products among mental health consumers.

The electric network frequency (ENF), often referred to as the industrial heartbeat, plays a crucial role in the power system. In recent years, it has found applications in multimedia evidence identification for court proceedings and audio-visual temporal source identification. This paper introduces a ENF region classification model named UniTS-SinSpec within the UniTS framework. The model integrates the si-nusoidal activation function and spectral attention mechanism while also redesigning the model frame-work. Training is conducted using a public dataset on the open science framework platform (OSF), with final experimental results demonstrating that after parameter optimization, the UniTS-SinSpec model achieves an average validation accuracy of 97.47%, surpassing current state-of-the-art and baseline models. Accurate classification can significantly aid in ENF temporal source identification. Future re-search will focus on expanding dataset coverage and diversity to verify the model's generality and ro-bustness across different regions, time spans, and data sources. Additionally, it aims to explore extensive application potential of ENF region classification in preventing crimes such as telecommunications fraud, terrorism, and child pornography.

The aim of the present study was to predict the response of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitors (ICIs) by leveraging computed tomography (CT) images using deep learning techniques. Retrospectively, 624 sequential CT images were gathered from 156 patients at Jiangsu Province Hospital, along with their clinical data. The dataset was subsequently partitioned into three groups: training (n=547), validation (n=64), and test (n=64). Moreover, an external validation cohort included 37 CT images from patients at Nanjing Pukou Peoples' Hospital, accompanied by comprehensive clinical data. An advanced Video Vision Transformer (ViViT) model incorporating global self-attention was utilized to analyse patients treated with ICIs and predict their response. The ViViT model's efficacy was evaluated using a confusion matrix and a receiver operating characteristic curve (ROC). Notably, the ViViT model demonstrated predictive prowess for ICIs response, yielding respective areas under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.69-0.78), 0.74 (95% CI: 0.61-0.86), 0.76 (95% CI: 0.62-0.88), and 0.69 (95% CI: 0.5-0.87) in the training, validation, test, and external validation cohorts. The present study illustrates how a deep learning model can provide a non-invasive means to predict clinical outcomes in NSCLC patients undergoing ICIs, potentially transforming personalized treatment approaches for individuals with NSCLC.

Novel therapies for treatment of familial dilated cardiomyopathy (DCM) are lacking. Triggers for the progression of the disorder commonly occur due to specific gene variants that affect the production of sarcomeric/cytoskeletal proteins. Generally these variants cause a decrease in tension by the myofilaments, resulting in signaling abnormalities within the micro-environment which over time result in structural and functional maladaptations leading to heart failure (HF). Current concepts support the hypothesis that the mutant sarcomere proteins induce a causal depression in the tension time integral (TTI) of linear preparations of cardiac muscle. However, molecular mechanisms underlying tension generation particularly in relation to mutant proteins and their impact on sarcomere molecular signaling, are currently the subject of controversy. Thus, there is a need for clarification as to how mutant proteins affect sarcomere molecular signaling in the etiology and progression of DCM. A main topic in this controversy is the control of the number of tension generating myosin heads reacting with the thin filament. One line of investigation proposes that this number is determined by changes in the ratio of myosin heads in a sequestered super-relaxed state (SRX) or in a disordered relaxed state (DRX) poised for force generation upon Ca-activation of the thin filament. Contrasting evidence from nanometer–micrometer-scale x-ray diffraction in intact trabeculae indicates that the SRX/DRX states may have a lesser role. Instead, the proposal is that myosin heads are in a basal OFF state in relaxation then transfer to an ON state through a mechano-sensing mechanism induced during early thin filament activation and increasing thick filament strain. Recent evidence about the modulation of these mechanisms by protein phosphorylation has also introduced a need for reconsidering control of tension. We discuss these mechanisms that lead to different ideas related to how tension is controlled and disturbed by mutant sarcomere proteins linked to DCM including cardiac myosin, myosin-binding protein C, titin filaments, and thin filaments. Resolving the various mechanisms and incorporating them into a unified concept is crucial for gaining a comprehensive understanding of DCM. This deeper understanding is not only important for diagnosis and treatment strategies with small molecules, but also for understanding the reciprocal signaling processes that occur between cardiac myocytes and their micro-environment. By unraveling these complexities, we can pave the way for improved therapeutic interventions for managing DCM.

Background: The absence of interdental papillae in dental prosthetics often results in unsatisfactory esthetic outcomes, such as black triangles and elongated clinical crowns. Although previous research has shown that gingival papillae can regenerate in a coronal direction, the mechanisms behind this phenomenon are not fully understood. Various theories have been proposed as potential explanations. Materials and Methods: This study evaluated several factors potentially influencing papilla growth, including adjacent elements, buc-cal-lingual papilla width, contact point position, convergent neck, crown overcontour, and intraoral negative pressure. A conceptual map was created to organize these factors, followed by their classification using a modified Overton Window. The principle of parsimony (Occam's Razor) was applied to identify the most plausible cause-related factors, categorizing them as causal relationship factors, filtered out factors, essential factors, or influencing factors. A mind map was used for visual grouping. Results: The analysis identified intraoral negative pressure as the most plausible cause-related factor for spontaneous papilla growth. Factors such as inflammation and drug-induced hyperplasia were excluded due to their association with unhealthy tissue. Other factors were classified as essential or influencing, but without direct causal links to papilla growth. Conclusions: Intraoral negative pressure is likely a key contributor to spontaneous papilla growth around implants. This finding, supported by abductive reasoning and the exclusion of less plausible factors, offers a new direction for enhancing esthetic outcomes in dental prosthetics, though further research is needed to confirm this causal relationship.

This research investigates the relationship between the long-term orientation of top managers and their strategic decision-making processes, emphasising the comprehensiveness, speed, and inventiveness of decisions. The study employed structural equation modelling to look into the suggested connections using a sample of CEOs. The findings demonstrated a high and positive correlation between long-term orientation and decision comprehensiveness and speed, but not for decision creativity. The study contributes to the growing body of research on how human variations, such as temporal orientation, affect business outcomes and strategic decision-making. Moreover, the fact that a number of the results differed from those of other studies carried out in other contexts highlights how important it is to consider the industry and cultural environment when assessing these connections.

Simulated (sim) racing is an emerging esport that has garnered much interest in recent years and has been a relatively under-researched field in terms of expertise and performance. When examining expertise, visual attention has been of particular interest to researchers, with eye tracking technology commonly used to assess visual attention. In this study, we examined the visual attention allocation of high and low skilled sim racers during a time trial task using Tobii 3 glasses. Participants were allocated to either group according to their fastest lap times. Our results indicate that when eye tracking metrics were normalised to lap time, there was no difference in the relative length of attention allocation (fixation behaviour), although lower skilled racers did have significantly greater fixation counts and durations across laps overall. Interestingly, high and low skilled sim racers differed in where they allocated their attention during the task, with high skilled sim racers allocating significantly less attention to the track relative to other areas of the display. This would allow higher skilled racers to obtain relatively more information from heads-up display elements in-game, all whilst driving at faster speeds. This study provides evidence that high and low skilled sim racers show similar lap time-corrected fixation behaviour, but how they allocate attention while driving differs.

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78\% accuracy (and precision), and 76\% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.

Objectives: The aim of this study was to determine why and how adults with attention deficit hyperactivity disorder (ADHD) experience variable impairment and identify the processes and strategies adults with ADHD use to develop positive self-regulation skills. Methods: A mixed cohort of 11 participants (6 female and 5 male) from a university, an adult ADHD clinic and an ADHD support group in the UK were interviewed online between September 2021 and February 2022. Data were collected and analysed simultaneously, inspired by constructivist grounded theory methodology. Results: Participants described a “polar awareness of difference” from others in terms of engagement and ADHD characteristics, and a “polar awareness of consciousness” experienced as the states of chaotic attention and hyperfocus, both of which impact core perceptions of self. Using an infinity paradigm, the results demonstrate unskilled attempts to self-regulate within and between these states using self-absorption or self-transcendence strategies including their inherent challenges and energetic cost. Our results further indicate that at the centre of this dynamic paradigm creative awareness strategies exist, which exemplify polarity awareness and the regulation of that awareness supported by an authentic inner compass (AIC). Conclusions: This paper presents the empirical foundation for the ADHD Creative Awareness Theory (CAT) – a new theory for understanding the experience of ADHD consciousness and environmental engagement. Practice implications are explored, and recommendations include use of the CAT as a framework for understanding and development of inherent self-regulation skills for adults with ADHD.

In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated promising performance in various applications. However, its ability to incorporate feature information from nodes beyond the immediate neighborhood is limited, leading to degraded performance on heterophilic data. To address this limitation, this thesis proposes a novel attention-based model, namely the Directional Graph Attention Network (DGAT). This model combines the feature-based attention with the global directional information extracted from the graph topology, as inspired by the Directional Graph Network (DGN). A new class of Laplacian matrices is proposed and an existing theoretical result on DGN is extended. This extension bridges a gap in the literature. The experimental results presented in the thesis, based on nine real-world benchmarks and ten synthetic data sets, demonstrate the superiority of the proposed DGAT model compared to the GAT baseline model. Particularly on heterophilic data sets, DGAT showed a notable average increase of approximately 35% in node classification tasks across all heterophilic real-world data sets. In addition, DGAT outperforms GAT by an average margin of around 51% in all ten synthetic data sets with various levels of heterophily

To efficiently use its finite resources, the visual system selects for further processing only a subset of the rich sensory information. Visual masking and spatial attention control the information transfer from visual sensory-memory to visual short-term memory. There is still a debate whether these two processes operate independently or interact, with empirical evidence supporting both arguments. However, recent studies pointed out that earlier studies showing significant interactions between common-onset masking and attention suffered from ceiling and/or floor effects. Our review of previous studies reporting metacontrast-attention interactions revealed similar artifacts. Therefore, we investigated metacontrast-attention interactions by using an experimental paradigm in which ceiling/floor effects were avoided. We also examined whether metacontrast masking is differently influenced by endogenous and exogenous attention. We analyzed mean absolute-magnitude of response-errors and their statistical distribution. Our results support the hypothesis that metacontrast and endogenous/exogenous attention are largely independent with negligible likelihood for interactions. Moreover, statistical modeling of the distribution of response-errors suggests weak interactions modulating the probability of “guessing” behavior for some observers in both types of attention. Nevertheless, our data suggest that any joint effect of attention and metacontrast can be adequately explained by their independent and additive contributions.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Abstract: Background: Irreversible electroporation (IRE) is a form of non-thermal ablation that delivers pulses of high voltage electrical current between electrodes. The role of IRE in the treatment of colorectal hepatic metastases is unestablished. This study is an international ques-tionnaire survey of the use of IRE for colorectal hepatic metastases. Methods: A questionnaire addressing views on the use of IRE for colorectal liver metastases was circulated to clinicians with an interest and/or expertise in this technique. Results: 64 clinicians from 17 different countries replied to the questionnaire. Thirty-six (56%) had experience of treating patients with colorectal liver metastases with IRE. The preferred mode of delivery of IRE was percutaneous treatment under CT guidance. Thirty-three (70% respondents) used IRE exclusively for lesions in proximity to inflow or outflow structures. Twenty (43% respondents) used IRE as their sole ablative treat-ment whilst 19 (40% respondents) used IRE in combination with thermal ablation. The maximum number of lesions that could be treated by IRE was two and preferred size of lesion was

Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is a growing momentum towards the adoption of intelligent transportation systems (ITS) and sustainable transportation solutions. This research aims to explore the potential of renewable energy sources to power electric vehicles (EVs) and flying automobiles, thereby accelerating the transition towards a low-carbon future. Objectives: The primary objectives of this research are as follows: - Investigate the role of renewable fuels in powering both EVs and flying cars to reduce reliance on fossil fuels and mitigate carbon emissions. - Analyze the dynamic growth and sustainability implications of advancements in battery technology and alternative renewable energy sources, such as Hydrogen Fuel Cells. - Evaluate the challenges and opportunities associated with the integration of flying vehicles into existing transportation infrastructure, focusing on safety, airspace control, and infrastructure development. - Review insights and guidance to stakeholders on promoting sustainable transportation solutions for EVs and flying cars through the utilization of renewable energy sources and the reduction of carbon emissions from the literature. Methodology: This research employs a mixed-method approach, combining literature review, and causal loop diagram analysis. The literature review provides a comprehensive understanding of current trends, challenges, and opportunities in sustainable transportation and renewable energy technologies. A causal loop diagram is utilized to visualize the dynamic interactions between various factors influencing the adoption of EVs and flying cars, including technological advancements, policy interventions, and market dynamics. Results and Discussion: The findings of this research highlight the significant potential of renewable energy sources, such as solar, wind, and hydrogen, in powering both EVs and flying cars. Advances in battery technology and hydrogen fuel cells are identified as key drivers in accelerating the transition towards sustainable transportation systems. However, challenges remain in terms of infrastructure development, safety regulations, and airspace management for flying vehicles. Nevertheless, there is a noticeable advancement in reducing carbon emissions and reliance on fossil fuels with the widespread adoption of EVs. By harnessing renewable energy and implementing effective policy measures, stakeholders might able to promote the widespread adoption of sustainable transportation solutions, thereby contributing to environmental protection and societal well-being. Conclusion: Literally, this research underscores the importance of sustainable transportation solutions in addressing urban congestion, environmental pollution, and energy security concerns. By leveraging renewable energy sources and embracing technological advancements, such as EVs and flying cars, stakeholders pave the way for a more sustainable and resilient transportation system. However, concerted efforts are needed to overcome existing challenges and ensure the successful integration of these technologies into existing infrastructure. Through collaborative partnerships and innovative initiatives, a low-carbon future for transportation logistics can be achieved, benefiting both the environment and society as a whole.

This paper aims to examine the potential for ecotourism development in the Pulicat Lake, a proposed Ramsar site situated in the Indian states of Andhra Pradesh and Tamil Nadu. Pulicat Lake has enormous potential for tourism and recreation; however, it is inevitable to ensure sustainable development in the region. The data for the study was collected using various methods, including case study techniques, qualitative interviews, observations, and information obtained from secondary sources. The study aimed to identify the sustainability dimensions of the location, including economic impact and income generation potentials through employment opportunities like field guides and travel companions. It is also examined the social impact on the local community and the development efforts made to reduce environmental pressures while conserving the flora and fauna. Additionally, the study discusses the possibilities and strategies for ornithological ecotourism at Pulicat Lake.

The taxonomy of the Fabeae (Vicieae) has long been problematic, but an analysis by Schaefer et al in 2012 gave an exceptionally clear view of the tribe and noted the possibility that some nomenclatural adjustments may be required at some future date. These authors suggested several options, expressing some preferences. However, there has been a recent change to formally accepted names implementing one of these possibilities, but without any additional relevant information. This change seems unjustified and unhelpful. We therefore present an argument for the retention, or re-instatement, of the genera Pisum, Vavilovia and Lens until such time as new data supports this requirement and there is no nomenclatural solution that is both accurate and convenient.

Candida auris is considered an emerging Candida species that has rapidly spread all over the world. The evidence of its origin and emerging resistance is still unclear. The severe infection by this species causes significant mortality and morbidity among the elderly and immune compressed individuals. Development of drug resistance is the major factor associated with therapeutic failure of existing antifungal agents. Previous studies addressed the antifungal resistance profile and drug discovery for C. auris. However, comprehensive coverage of these information in a single investigation yet to be available. In this review, we have mainly focused on recent development in therapeutic strategy against C. auris. Based on the available information, several differential approaches were discussed, including existing antifungal drugs, chemical compounds, essential oils, natural products, antifungal peptides, immunotherapy, antimicrobial photodynamic therapy, drug repurposing, and drug delivery systems. Among them, chemical medications, natural products, and antifungal peptides are the prime contributors. However, a limited number of resources are available to prove the efficiency of these potential therapies in clinical usage. Hence, we hope that the data gathered in this review can encourage in vivo studies and clinical trials.

Background. The role of exercise programs during Substance Use Disorder (SUD) treatment is considered particularly supportive in fostering positive psychological and behavioral changes. The aim of this feasibility study was to explore the acute effects of cycling exercise in an immersive virtual reality (IVR) environment on attentional control and self-efficacy expectations of indi-viduals undergoing SUD treatment. Methods. A total of 20 individuals (mean age of 37.75 years) enrolled in a SUD treatment program participated in the study. They were instructed to complete a single session of cycling with a self-selected duration within the IVR system. Results. Following the cycling session, pre-post measures indicated a statistically significant im-provement in self-efficacy expectations and attentional control measured with the Stroop test. The results revealed that participants expressed high levels of intention for future use, inter-est/enjoyment, and positive attitudes toward the IVR exercise system. Qualitative data indicated that participants found the IVR exercise system enjoyable, expressed a strong intention to use it, and did not encounter technical difficulties or negative emotions. Conclusions: In conclusion, it seems that engaging in a brief period of self-selected exercise within a virtual environment can result in temporary activation of cognitive changes, heightened self-efficacy expectations, and a motivating approach to increasing physical activity participation among individuals with SUD, thus suggesting the feasibility of this approach.

Dent disease type 1 is a rare inherited renal disorder affecting mainly young males, generally leading to end-stage renal failure and for which there is no cure. It is caused by inactivating mutations in the gene encoding ClC-5, a 2Cl-/H+ exchanger found on endosomes in the renal proximal tubule. This transporter participates in reabsorbing all filtered plasma proteins which justifies why proteinuria is commonly observed when ClC-5 is defective. In the context of Dent disease type 1, a proximal tubule dedifferentiation was shown to be accompanied by a dysfunctional cell metabolism. However, the exact mechanisms linking such alterations to chronic kidney disease are still unclear. In this review, we gather knowledge from several Dent disease type 1 models to summarize the current hypotheses generated to understand the progression of this disorder. We also highlight some urinary biomarkers for Dent disease type 1 suggested in different studies.

This review explores the complex interrelationships between emotion, attention, and behavior, drawing upon empirical evidence from psychological, neuroscientific, and clinical studies. It delves into the neural mechanisms that underlie these interactions, with a focus on key brain regions such as the prefrontal cortex and amygdala. Clinical cases involving stroke and attention-deficit/hyperactivity disorder (ADHD) are examined to illustrate the real-world implications of these cognitive interactions. The review also discusses the impact of effective emotion regulation strategies and the role of attention as a cognitive mechanism that influences both emotion and behavior. Neuroimaging studies, particularly functional MRI, are highlighted for their role in providing a deeper understanding of these intricate relationships. The review concludes by emphasizing the importance of this triadic relationship in adaptive functioning and its broader implications for mental health, interpersonal relationships, and quality of life.

Hypoxia, or reduced oxygen availability, has been studied extensively for its ability to activate specific genes. Hypoxia induced gene expression is mediated by the HIF transcription factors, although not exclusively so. Despite the great knowledge on the mechanisms by which hypoxia activates genes, much less is known about how hypoxia promotes gene repression. In this review, we discuss the potential mechanisms underlying hypoxia-induced transcriptional repression responses. We highlight HIF-dependent and independent mechanisms, but also the potential roles of dioxygenases with functions at the nucleosome and DNA level. Finally, we discuss recent evidence regarding the involvement of transcriptional repressor complexes in hypoxia.

Background: Although many studies analyse gender differences in the clinical expression of Attention Deficit Hyperactivity Disorder (ADHD) and prevalence studies show that girls with ADHD are underdiagnosed, there are no instruments that are sensitive to the detection of girls with ADHD. Objective: The objective of this study is to develop a self-report early detection instrument for ADHD for boys and girls aged 7 to 16, which includes the gender perspective and is sensitive to the detection of girls with ADHD. Methods: The development of the scale and the items that comprise it were created from the thematic analysis of ADHD and its evaluation in children, based on the diagnostic criteria of the DSM-5-TR. A modified e-Delphi method involving a three-round web survey was used to reach a consensus on the content of the scale. Ten experts were recruited into a professional panel. The panel members were asked to assess the differential symptomatology of ADHD in boys and girls, the dimensions to be evaluated, and the importance of the scale items to evaluate the content. Results: Consensus was reached regarding 13 total items distributed in three dimensions: Inattention, Hyperactivity/Impulsivity, and a third dimension, Internalisation, which includes symptoms most present in the expression of ADHD in girls. Conclusions: To the best of our knowledge, the development of this scale using the Delphi method is the first specific scale for identifying ADHD that addresses the gender perspective and the differential symptomatology between boys and girls. Although we must proceed to the analysis of the psychometric properties, we can anticipate that this scale will provide relevant and reliable information for the identification of ADHD in both boys and girls.

Anomaly detection and segmentation aim at distinguishing abnormal images from normal images and further localizing the anomalous regions. Feature reconstruction based method has become one of the mainstream methods for this task. This kind of method has two assumptions: (1) The features extracted by neural network is a good representation of the image. (2) The autoencoder solely trained on the features of normal images cannot reconstruct the features of anomalous regions well. But these two assumptions are hard to meet. In this paper, we propose a new anomaly segmentation method based on feature reconstruction. Our approach mainly consists of two parts: (1) We use a pretrained vision transformer (ViT) to extract the features of the input image. (2) We design a self-attention autoencoder to reconstruct the features. We regard that the self-attention operation which has a global receptive field is beneficial to the methods based on feature reconstruction both in feature extraction and reconstruction. The experiments show that our method outperforms the state-of-the-art approaches for anomaly segmentation on the MVTec dataset. It is both effective and time-efficient.

The work and life routine of long-haul truck drivers involves the use of truck stops and rest areas to meet their basic human needs. These extensions of their workspaces while on the road, do not always offer adequate physical structures and services that drivers need for optimal health. Aim: To evaluate long-haul truck drivers’ perceptions about the services of food, safety, physical activity, rest and personal hygiene offered at truck stops and rest areas in Brazil. Method: Quantitative study conducted with long-haul truck drivers from the Southern region of Brazil. For data collection, a sociodemographic questionnaire and a Likert scale on food, rest, personal hygiene, safety and physical activity services were used, offered in general at the truck stops and rest areas along Brazilian roads, from March to August 2023. The data were analyzed with simple frequency descriptive statistics. Results: 175 long-haul truck drivers participated, 70.29% declared that the services of the truck stop and rest areas were charged; more than half (53.59%) of the professionals evaluated the rest service as “good” or “excellent”; the food services, was “good” or “excellent” for 42.24% of the drivers. The spaces for physical activities were the worst evaluated as “bad” or “terrible”, by 41.61%, followed by bathroom service (28.42%) and safety (34.24%), evaluated as “bad” or “terrible”. Conclusions: In the perception of long-haul truck drivers, the rest and feeding services had better evaluations, on the other hand, the services of bathroom, safety and physical activity presented worse evaluations. The perception of drivers demonstrates the lack of investments to improve basic services such as personal hygiene, safe environment and physical exercises, fundamental to the health of the worker, aiming to reduce vulnerability, sedentary lifestyle, and meeting the basic human needs of long-haul truck drivers.

Different studies corroborate a role for ceramide synthases and their downstream products, ceramides, in modulation of apoptosis and autophagy in the context of cancer. These mechanisms of regulation, however, appear to be context dependent in terms of ceramides’ fatty acid chain length, subcellular localization, and the presence or absence of their downstream targets. Our current understanding of the role of ceramide synthases and ceramides in regulation of apoptosis and autophagy could be harnessed to pioneer the development of new treatments to activate or inhibit a single type of ceramide synthase, thereby regulating the apoptosis induction or cross talk of apoptosis and autophagy in cancer cells. Moreover, the apoptotic function of ceramide suggests that ceramide analogues can pave the way for the development of novel cancer treatments. Therefore, in the current review paper we discuss the impact of ceramide synthases and ceramides in regulation of apoptosis and autophagy in context of different types of cancers. We also briefly introduce the latest methods to analyze the lipids in biological samples. Finally, we discuss the drug development strategies focusing on the ceramide synthases and ceramides as future therapeutic approaches in cancer therapy.

Cancer is often characterised by the presence of hypoxia and inflammation. Paramount to the mechanisms controlling cellular responses under such stress stimuli, are the transcription factor families of Hypoxia Inducible Factor (HIF) and Nuclear Factor of kappa-light-chain-enhancer of activated B cells (NF-кB). Although, a detailed understating of how these transcription factors respond to their cognate stimulus is well established, it is now appreciated that HIF and NF-кB undergo extensive crosstalk, in particular in pathological situations such as cancer. Here, we focus on the current knowledge on how HIF is activated by inflammation and how NF-кB is modulated by hypoxia. We summarise the evidence for the possible mechanism behind this activation and how HIF and NF-кB function impacts cancer, focusing on colorectal, breast and lung cancer. We discuss possible new points of therapeutic intervention aiming to harness the current understanding of the HIF-NF-кB crosstalk.

One of the most leading causes of death worldwide is Colorectal cancer(CRC). Polyp segmentation is the most important detected measure for preventing CRC. However, there is still a missing rate for diminutive polyps and multiple ones. In order to solve the phenomenon, we propose to introduce auxiliary attention module(AAM) that can enhance the learning of features related to multiple and diminutive polyps by focusing more on the located and detailed information. Meanwhile, we design to decrease missed rate of multiple and diminutive polyps by implementing an area adaptive loss(AAL) which adapts the weight according to the area and the number of polyps. Our proposed novel AAM and AAL concentrates on training with hard examples and localized information. To evaluate the effectiveness and generalization ability of our proposed model, We utilize three different datasets of variable sizes and a cross dataset. Our proposed method achieves the best results on the Kvasir-SEG dataset, the CVC-ClinicDB dataset and the cross dataset, particularly for the Kvasir-Sessile dataset consisting of small,flat and diminutive polyps. Extensive experimental results show that our proposed DoubleAANet surpass the performance of all existing state-of-the-art segmentation methods.

Remote sensing technology is crucial for accurate rooftop detection, benefiting urban planning, disaster management, and solar resource estimation. This study employs Efficient Interactive Segmentation (EISEG) to enhance the efficiency of remote sensing image labeling, with a particular focus on rooftop detection. It is necessary to use modern technology because traditional manual labelling methods are labor-intensive and complicated. The study introduces a novel framework on deep learning semantic segmentation models, facilitating an efficient approach to rooftop identification using high-resolution UAV remote sensing datasets. Large dataset of labeled UAV rooftop building images, in which each superpixel region is assigned a binary label indicating rooftop presence. Advanced methods including Asymmetric Neural Network (ANN), Dual At-tention Network (DANet), PP-LiteSeg, and Deeplab3 are implemented for automatic rooftop detection due to their higher performance and advanced architectures. These models are executed on the Baidu deep learning platform PaddlePaddle, generating initial rooftop segmentation maps crucial for estimating photovoltaic resources. The ANN model emerges with the highest accuracy at 96%, followed by DANet at 95.09%, PP-LiteSeg at 94.54%, and Deeplab3 at 81.61%. The outcomes presenting efficient models for automated rooftop identification, and demonstrating the continuous need for improving deep learning techniques in smart and sustainable cities.

Attention Deficit Hyperactivity Disorder (ADHD) it is known as a neuropsychiatric disorder. Anatomicaly components with implications in this previously mentioned disorder, are the anterior cingulate gyrus and dorsolateral prefrontal cortex (DLFPC) that are are found having a role in appearing this disease. ADHD is associated with cognitive and functional deficits that relate to diffuse abnormalities in the brain. ADHD is a specific pathology in children but may persisit to youth and also not so hard to find to adults.Till now, hard to find the curative treatment knowing ADHD as a neuropsychiatric disorder with enlarge unknown scientific points. Genetic and environmental component play an important role in diagnosis. Patients life style, a relevant history of individual geneticsand environmental factors are important in diagnosisThe management of ADHD, is also an important part togeter with future trends.

Teeth segmentation plays a pivotal role in dental diagnosis, treatment, planning, and the development of computer-aided dental systems. It enables precise identification and analysis of dental structures, aiding in detecting dental abnormalities, measuring tooth dimensions, and assessing oral health conditions. Accurate teeth segmentation also facilitates the automation of dental workflows, leading to improved efficiency and reduced human error. Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches developed or enhanced. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net architecture, as previously used in [1], applied to teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts to develop automated systems to assist dental professionals in their clinical practice.

Cheating is a kind of fraud or attempts to get dishonest advantages in an academic institution, it has been increasing dramatically for the last 50 years with the growth of technology. In some countries, this phenomenon has changed to a problematic factor in schools. Plagiarism is also considered as a type of cheating if it occurs intentionally. More efforts are made to mitigate the severity of cheating in an academic institution using technological-based devices. Besides, technology has an effective role in the detection of plagiarism by several detection platforms, however, still, there are some types of plagiarism which cannot be detected by these platforms efficiently, that authors should treat honestly during his/her scientific writing. This work aims to conceptualize and characterize cheating and plagiarism in an academic institution, also it is tried to indicate the relationship of cheating and plagiarism that in which cases, plagiarism becomes cheating. To achieve the purposes, relevant literature including scientific papers, monographs, reports, and authorized website were reviewed, and the literature output are integrated with the observation and experience of author during the teaching and learning in academic institutions.The findings and direct observation from close contact of classrooms show that cheating is felt mostly in classes with large numbers of students, that institution policy, instructor control, and using of technology have the most effect on the controlling cheating. Plagiarism as academic dishonesty is a widespread phenomenon among the students and even the authors. In some cases, the poor understanding of students and authors in scientific writing rules causes plagiarism and is measurable by some available detection platforms.

Malignant pleural mesothelioma (MPM) is a locally aggressive disease related to asbestos exposure with a median survival for untreated patients of 4-8 months. The combination of chemotherapy based in platinum and antifolate is the standard treatment and the addition of bevacizumab adds two months of gain in median survival. Recently in first-line treatment, immunotherapy combining nivolumab with ipilimumab has been shown to be superior to chemotherapy in the CheckMate-743 study in terms of overall survival (18.1 months), leading to its approval by the FDA and EMA. The positive results of this study represent a new standard of treatment for patients with MPM, however, not all patients will benefit from immunotherapy treatment. In an effort to improve the selection of patient candidates for immunotherapy in different tumors, biomarkers that have been associated with a greater possibility of response to treatment have been described. MPM is a type of tumor with low mutational load and neo-antigens, making it a relatively non-immunogenic tumor for T cells and possibly less susceptible to responding to immunotherapy. Different retrospective studies have shown that PD-L1 expression occurs in 20-40% of patients and is associated with a poor prognosis; however, the predictive value of PD-L1 in response to immunotherapy has not been confirmed. The purpose of this work is to review state of art of MPM treatment in the year 2023 focusing in the efficacy results of first-line or subsequent immunotherapy studies in patients with MPM and possible chemo-immunotherapy combination strategies. Additionally, potential biomarkers of response to immunotherapy will be reviewed, such as histology, PD-L1, lymphocyte populations, and TMB.

Thromboprophylaxis of hospitalized patients at risk of venous thromboembolism (VTE) presents challenges owing to patient heterogeneity and lack of adoption of evidence-based methods. Intuitive practices for thromboprophylaxis have resulted in many patients being inappropriately prophylaxed. Multiple interventions for thromboprophylaxis have been tested, including use of multifaceted approaches such as national VTE prevention programs with audits, use of passive alerts (either human or electronic), and more recently use of active clinical decision support (CDS) tools incorporated into electronic health records (EHRs). Multifaceted health-system interventions have shown mixed results in their ability to increase appropriate thromboprophylaxis and reduce VTE, unless mandated through a national VTE prevention program, though the latter approach is potentially costly, effort- and time-dependent. Studies utilizing passive human or electronic alerts have also shown mixed results in increasing appropriate thromboprophylaxis and reducing VTE. The use of active CDS tools holds the greatest promise, especially with further refinement and widespread implementation within various EHRs and clinical workflows. Recent-ly a universal cloud-based and EHR-agnostic CDS VTE tool revealed high adoption and effectiveness in increasing appropriate thromboprophylaxis and reducing major thromboembolism. This narrative review summarizes the literature on system-wide thromboprophylaxis interventions in hospitalized patients, with a focus on CDS tools.

This study explores the application of the Laban/Bartenieff Movement System (® LBMS), particularly its Effort component, to analyze the biomechanical dynamics of the golf swing. Focusing on the initiation of the downswing, this paper investigates how the instantaneous screw axis (ISA) with rotational and linear motions contributes to subsequent movements, such as the hip turn. We propose that the physical forces generated by the golf club, characterized by mass moment inertia, extend beyond the body's boundaries, such as through the hand grip. A skilled golfer’s ability to 'allow' these forces to influence body dynamics without opposing them is analyzed through the lens of LBMS's Free Flow Effort, suggesting an indulgent and expansive use of movement energy.