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Daily briefing: Mice give unconscious friends first aid

LDMP-RENet: Reducing intra-class differences for metal surface defect few-shot semantic segmentation

Given their fast generalization capability for unseen classes and segmentation ability at pixel scale, models based on few-shot segmentation perform well in solving data insufficiency problems during metal defect detection and in delineating refined objects under industrial scenarios. Extant researches fail to consider the inherent intra-class differences in data about metal surface defects, so that the models can hardly learn enough information from the support set for guiding the segmentation of query set. Specifically, it can be categorized into two types: the semantic intra-class difference induced by internal factors in metal samples and the distortion intra-class difference caused by external factors of surroundings. To address these differences, we introduce a Local Descriptor-based Multi-Prototype Reasoning and Excitation Network (LDMP-RENet) to learn the two-view guidance, i.e., the local information from the graph space and the global information from the feature space, and fuse them to segment precisely. Given the contribution of relational structure of graph space-embedded local features to the Semantic Difference obviation, a multi-prototype reasoning module is utilized to extract local descriptors-based prototypes and to assess relevance between local-view features in support-query set pairs. Meanwhile, since global information helps obviate Distortion Difference in observations, a multi-prototype excitation module is employed for capturing global-view relevance in the above pairs. Lastly, an information fusion module is employed to integrate the learned prototypes in both global and local views, thereby creating pixel-level masks. Thorough experiments are conducted on defect datasets, revealing the superiority of proposed network to extant benchmarks, which sets a new state-of-the-art.

A malware classification method based on directed API call relationships

In response to the growing complexity of network threats, researchers are increasingly turning to machine learning and deep learning techniques to develop advanced models for malware detection. Many existing methods that utilize Application Programming Interface (API) sequence instructions for malware classification often overlook the structural information inherent in these sequences. While some approaches consider the structure of API calls, they typically rely on the Graph Convolutional Network (GCN) framework, which tends to neglect the sequential nature of API interactions. To address these limitations, we propose a novel malware classification method that leverages the directed relationships within API sequences. Our approach models each API sequence as a directed graph, incorporating node attributes, structural information, and directional relationships. To effectively capture these features, we introduce First-order and Second-order Graph Convolutional Networks (FSGCN) to approximate the operations of a directed graph convolutional network (DGCN). The resulting directed graph embeddings from the FSGCN are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN). Additionally, to mitigate the effects of imbalanced datasets, we employ the Synthetic Minority Over-sampling Technique (SMOTE), ensuring that underrepresented classes receive adequate attention during training. Our method has been rigorously evaluated through extensive experiments on two real-world malware datasets. The results demonstrate the effectiveness and superiority of our approach compared to traditional and graph-based malware classification techniques.

‘Science is happening’: Israeli and Palestinian scientists continue collaborations amid conflict

An efficient detection of Sinkhole attacks using machine learning: Impact on energy and security

In the realm of Wireless Sensor Networks (WSNs), the detection and mitigation of sinkhole attacks remain pivotal for ensuring network integrity and efficiency. This paper introduces SFlexCrypt, an innovative approach tailored to address these security challenges while optimizing energy consumption in WSNs. SFlexCrypt stands out by seamlessly integrating advanced machine learning algorithms to achieve high-precision detection and effective mitigation of sinkhole attacks. Employing a dataset from Contiki-Cooja, SFlexCrypt has been rigorously tested, demonstrating a detection accuracy of 100% and a mitigation rate of 97.31%. This remarkable performance not only bolsters network security but also significantly extends network longevity and reduces energy expenditure, crucial factors in the sustainability of WSNs. The study contributes substantially to the field of IoT security, offering a comprehensive and efficient framework for implementing Internet-based security strategies. The results affirm that SFlexCrypt is a robust solution, capable of enhancing the resilience of WSNs against sinkhole attacks while maintaining optimal energy efficiency.

How animal ethics committees make decisions – a scoping review of empirical studies

Objectives The aim of the scoping review is to explore the decision-making process for the evaluation of animal research proposals within Animal Ethics Committees (AEC) and Institutional Animal Care and Use Committees (IACUC), and to critically summarize the available empirical literature on the different factors influencing, or likely to influence, decision-making by AECs when evaluating animal research proposals. Methods A systematic search of empirical literature published between 01.12.2012 and 03.06.2024 in PubMed, Scopus, and Web of Science, was performed. Results Twelve papers were included in the final results, four of which were quantitative, five qualitative, and three were mixed methods. Qualitative content analysis revealed deficits in the assessment of the 3Rs (Replacement, Reduction or Refinement) or the weighing of harms and benefits. Factors related to the review process, applicants, and committees were found to influence this process. Conclusion The findings prompt pragmatic strategies to improve the decision making process of Animal ethics committees. Registration The protocol for this review was registered with Open Science Framework (OSF) with the following DOI: https://doi.org/10.17605/OSF.IO/GZJMB

I spent two years organizing an international conference. How do I get compensated for my work?

TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems

Large-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. This paper proposes an improved sparrow search algorithm (SSA) that manages convergence and diversity separately for solving LSMaOPs, called two-stage sparrow search algorithm (TS-SSA). In the first stage of TS-SSA, this paper proposes a many-objective sparrow search algorithm (MaOSSA) to mainly manages the convergence through the adaptive population dividing strategy and the random bootstrap search strategy. In the second stage of TS-SSA, this paper proposes a dynamic multi-population search strategy to mainly manage the diversity of the population through the dynamic population dividing strategy and the multi-population search strategy. TS-SSA has been experimentally compared with 10 state-of-the-art MOEAs on DTLZ and LSMOP benchmark test problems with 3-20 objectives and 300-2000 decision variables. The results show that TS-SSA has significant performance and efficiency advantages in solving LSMaOPs. In addition, we apply TS-SSA to a real case (automatic test scenarios generation), and the result shows that TS-SSA outperforms other algorithms on diversity.

Performance measurement and evaluation of health practitioner regulation: A scoping review protocol

Health practitioner regulation plays a fundamental role in public protection by overseeing and governing healthcare professionals to ensure they deliver safe health services. It also serves as a strategic lever to strengthen broader health system goals such as improving the accessibility of services, the sustainability of health workforces, and health system resilience. Although the goals of health practitioner regulation are easily articulated, achieving and evaluating these goals are far more challenging. Performance measurement and evaluation of professional regulators and regulatory systems are critical to improving regulatory processes and functions. This is especially important where there is rising government, public, and professional skepticism and mistrust of the effectiveness and efficiency of regulators across global jurisdictions. Although there is evidence that some health practitioner regulators and regulatory systems engage in performance measurement and evaluation, the similarities and differences remain unclear. The objective of this scoping review is to explore the nature, extent, and range of scholarship related to health practitioner regulatory performance measurement and evaluation. It will explore existing performance measurement and evaluation frameworks; the key principles and areas of focus of these frameworks; and the indicators, metrics and outcomes used to evaluate performance. The review will be conducted in accordance with the JBI guidelines for scoping reviews and will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Database searches will include Ovid MEDLINE, Ovid EMBASE, CINAHL, Scopus, and Web of Science Core Collection. Gray literature will be identified through leading regulatory organizations, consortiums, and think tanks. Two independent reviewers will screen titles and abstracts followed by full-text and disagreements will be resolved by a third reviewer. Data will be analyzed using descriptive statistics and conventional content analysis. Results will be presented using evidence tables and a narrative summary. Open Science Framework Registration: https://doi.org/10.17605/OSF.IO/WABTF

A dynamic and adaptive class-balanced data augmentation approach for 3D LiDAR point clouds

3D LiDAR point clouds, obtained through scanning by LiDAR devices, contain rich information such as 3D coordinates (X, Y, Z), color, classification values, intensity values, and time. However, the original collected 3D LiDAR point clouds often exhibit significant disparities in instance counts, which can hinder the effectiveness of point cloud segmentation. PolarMix, a data augmentation algorithm for 3D LiDAR point cloud datasets, addresses this issue by rotating and pasting selected class instances around the Z axis multiple times to enrich the distribution of the point cloud. However, PolarMix does not adequately consider the substantial variations in instance counts within the original point clouds, leading to an imbalance in the dataset. To address this limitation, we propose a modified version of PolarMix’s instance-level rotation and pasting method that dynamically adjusts the number of rotations and pastes based on the proportion of each instance’s point cloud count relative to the total. This adaptive class-balancing approach ensures a more balanced distribution of instances across the entire dataset. We term our new algorithm Dynamic Adaptive Class-Balanced PolarMix (DACB-PolarMix). Experimental results demonstrate the effectiveness of DACB-PolarMix in balancing class distribution and enhancing model performance. The results on the SemanticKitti dataset are particularly significant. Under the MinkNet model, our method improved the mIoU from 65% to 67.9%, and under the SPVCNN model, our method increased the mIoU from 66.2% to 67.5%.

Application of a dynamic optimization-based multi-attribute fusion method for fault detection

The focus of oil and gas exploration in the Tarim Basin has shifted from interlayers to fracture-controlled karsts. A significant oilfield characterized by strike-slip faults was discovered in the Yueman area. However, identifying such fault zones is challenging because of the complex and chaotic seismic reflection characteristics, as well as the presence of seismic noise and other discontinuities. To improve oilfield production, the accurate identification of strike-slip fault zones in ultradeep tight limestone is a critical issue. The seismic anomalies of such fault zones exhibit diverse characteristics, with the low-velocity zone of the fault causing a “beaded” reflection pattern. Traditional coherent and curvature attribute methods have large errors in identifying strike-slip faults and cannot adequately characterize the contour features of fracture-karst traps. To address these challenges, this study proposed a multi-attribute optimal surface-based fracture identification technology based on forward simulation records. Seismic attributes that were sensitive to different types of strike-slip faults were selected, and multiple attributes were merged to obtain a fracture distribution map using the best surface voting algorithm. This method effectively suppresses noise that is irrelevant to fractures and is sensitive only to fracture information, allowing for the identification of subtle waveform changes caused by strike-slip faults. Thus, the accuracy and continuity of fracture identification were significantly improved.

How a PhD travel fellowship enriched an international cell-biology meeting

Correction: Impact of corporate social responsibility on employee loyalty: Mediating role of person-organization fit and employee trust

Technostress and its associated factors: Burnout and fatigue among Malaysian healthcare workers (HCWs) in state hospitals

Background Technostress is defined as a psychological state associated with the increased usage of advanced computer technologies on a daily basis. It is also defined as an anxiety feeling or mental strain due to excessive exposure or involvement with technologies. Aim This study aimed to determine the level of technostress associated with burnout and fatigue among healthcare workers (HCWs) in the state hospitals of Malaysia. Methods A cross-sectional study was conducted from September 2022 to November 2023 among HCWs working in the 15-state hospitals in Malaysia. A standardized questionnaire was distributed among the HCWs in the state hospitals in Malaysia. The questionnaire contains information on socio-demography and topic-specific scales on technostress, burnout and fatigue. Results A total of 1620 HCWs were included in the analysis, of which 244 (15%) have high level of technostress, 1089 (67%) have moderate technostress, and 287 (18%) have low technostress. Burnout, and fatigue were significantly associated with technostress. HCWs with moderate burnout were less likely to have high technostress compared to those with high burnout (B =  -0.993, 95% CI; 0.231 - 0.594; p <  0.001). Those with moderate fatigue were less likely to have high technostress (B =  -3.844, 95% CI; 0.003 - 0.162; p <  0.001) compared to those with high fatigue. Conclusions This study found that majority of the HCWs have moderate level of technostress. Technostress has become more common after the COVID-19 pandemic in 2020 drastically altered working conditions and made remote work using information and communication technologies (ICT) a necessity rather than a luxury. Mitigation measures and programs that include psychological support for individuals who are struggling with the technostress and burnout are needed to overcome this issue.

Correction: Self-reported impact of the COVID-19 pandemic, affective responding, and subjective well-being: A Swedish survey

Menstrual cycle effects on cognitive performance: A meta-analysis

Does a woman’s cognitive performance change throughout her menstrual cycle? Menstruation continues to be a taboo topic, subject to myths about how it affects women. Despite the considerable number of empirical studies, there have been few quantitative summaries of what is known. To address this gap, we conducted a meta-analysis of cognitive performance across the menstrual cycle, including the domains of attention, creativity, executive functioning, intelligence, motor function, spatial ability, and verbal ability. We included studies that measured women’s performance at specific points in the cycle for tasks that have objectively correct responses. Our analysis examined performance differences across phases using Hedges’ g as the effect size metric. Across 102 articles, N =  3,943 participants, and 730 comparisons, we observe no systematic robust evidence for significant cycle shifts in performance across cognitive performance. Although two results appeared significant with respect to differences in spatial ability, they arise from a large number of statistical tests and are not supported in studies that use robust methods to determine cycle phase. Through the use of Egger’s test, and examination of funnel plots, we did not observe evidence of publication bias or small-study effects. We examined speed and accuracy measures separately within each domain, and no robust differences across phases appeared for either speed or accuracy. We conclude that the body of research in this meta-analysis does not support myths that women’s cognitive abilities change across the menstrual cycle. Future research should use larger sample sizes and consistent definitions of the menstrual cycle, using hormonal indicators to confirm cycle phase.

Prevalence and associated factors of overweight and obesity among Afghan school children: A cross-sectional analytical study from Kandahar City, Afghanistan

Background Childhood overweight and obesity is an emerging public health problem in developing countries. This is the first school-based study of its type from Afghanistan to estimate the prevalence and associated factors of overweight and obesity among Afghan school children aged 6–18 years in Kandahar City of Afghanistan. Methods This cross-sectional analytical study was conducted among 2281 school children from January 10–April 15, 2024. Sociodemographic properties, anthropometric measurements, and other data were collected from all the participants. Data were analyzed using descriptive statistics, Chi-square test, and multiple logistic regression analysis. Results Among the 2281 enrolled children, 72.5% were boys, 65.1% going to private schools, and 53.8% poor. The prevalence of overweight and obesity was 11.5% (6.6% were overweight and 4.9% were obese). The mean (SD) age was 12.7 (2.1) years. By logistic regression analysis, statistically significant risk factors associated with overweight and obesity were being boy (AOR 1.5 and 95% CI 1.1–2.0), student of private school (AOR 2.2 and 95% CI 1.5–2.8), belonging to a rich family (AOR 1.9 and 95% CI 1.3–2.7), and parental obesity (AOR 1.5 and 95% CI 1.1–2.0). Conclusion School children of Kandahar city are suffering from overweight/obesity. Overweight/obesity should be dealt with as an emerging problem in school children of Kandahar city. It is recommended that Afghanistan ministries of education and public health, with the help of international donor agencies, such as WHO and UNICEF, work together in controlling overweight and obesity in school children of Kandahar city. Periodic special awareness programs on the prevention and control of overweight/obesity should be conducted in schools, radio, television, and other sources of social media.

In silico identification of novel ligands targeting stress-related human FKBP5 protein in mental disorders

FK506-binding protein 51 (FKBP51 or FKBP5) serves as a crucial stress modulator implicated in mental disorders, presenting a potential target for intervention. Inhibitors like SAFit2, rapamycin, and tacrolimus exhibit promising interactions with this protein. Despite these advances, challenges persist in diversifying FKBP5 ligands, prompting further exploration of interaction partners. Hence, this study aims to identify other potential ligands. Employing molecular docking, we generated complexes with various ligands (rapamycin, tacrolimus, SAFit2-Selective antagonist of FKBP51 by induced fit, ascomycin, pimecrolimus, rosavin, salidroside, curcumin, apigenin, uvaricin, ruscogenin, neoruscogenin, pumicalagin, castalagin, and grandinin). We identified the top 3 best ligands, of which ruscogenin and neoruscogenin had notable abilities to cross the blood-brain barrier and have high gastrointestinal absorption, like curcumin. Toxicity predictions show ruscogenin and neoruscogenin to be the least toxic based on oral toxicity classification (Class VI). Tyrosine (Tyr113) formed consistent interactions with all ligands in the complex, reinforcing their potential and involvement in stress modulation. Molecular dynamic (MD) simulation validated strong interactions between our three key ligands and FKBP5 protein and provided an understanding of the stability of the complex. The binding free energy (ΔG) of the best ligands (based on pharmacological properties) from MD simulation analysis is -31.78 kcal/mol for neoruscogenin, -30.41 kcal/mol for ruscogenin, and -27.6 kcal/mol for curcumin. These molecules, therefore, can serve as therapeutic molecules or biomarkers for research in stress-impacted mental disorders. While offering therapeutic implications for mental disorders by attenuating stress impact, it is crucial to emphasize that these ligands’ transition to clinical applications necessitates extensive experimental research, including clinical trials, to unravel the intricate molecular and neural pathways involved in these interactions.

Echo state property and memory capacity of artificial spin ice

Targeting prostate cancer by new bispecific monocyte engager directed to prostate-specific membrane antigen

Prostate cancer (PCa) ranks as the second leading cause of cancer-related deaths among men in the United States. Prostate-specific membrane antigen (PSMA) represents a well-established biomarker of PCa, and its levels correlate positively with the disease progression, culminating at the stage of metastatic castration-resistant prostate cancer. Due to its tissue-specific expression and cell surface localization, PSMA shows superior potential for precise imaging and therapy of PCa. Antibody-based immunotherapy targeting PSMA offers the promise of selectively engaging the host immune system with minimal off-target effects. Here we report on the design, expression, purification, and characterization of a bispecific engager, termed 5D3-CP33, that efficiently recruits macrophages to the vicinity of PSMA-positive cancer cells mediating PCa death. The engager was engineered by fusing the anti-PSMA 5D3 antibody fragment to a cyclic peptide 33 (CP33), selectively binding the Fc gamma receptor I (FcγRI/CD64) on the surface of phagocytes. Functional parts of the 5D3-CP33 engager revealed a nanomolar affinity for PSMA and FcγRI/CD64 with dissociation constants of K D =  3 nM and K D =  140 nM, respectively. At a concentration as low as 0.3 nM, the engager was found to trigger the production of reactive oxygen species by U937 monocytic cells in the presence of PSMA-positive cells. Moreover, flow cytometry analysis demonstrated antibody-dependent cell-mediated phagocytosis of PSMA-positive cancer cells by U937 monocytes when exposed to 0.15 nM 5D3-CP33. Our findings illustrate that 5D3-CP33 effectively and specifically activates monocytes upon PSMA-positive target engagement, resulting in the elimination of tumor cells. The 5D3-CP33 engager can thus serve as a promising lead for developing new immunotherapy tools for the efficient treatment of PCa.