Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of machine learning to predict somatic cell count status in dairy cows post-calving

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow’s risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.
“Health Outcomes of Grandparents Caring for Double Orphans in South Africa”: What Are the Determinants?

In the 21st century, grandparenthood is a significant phenomenon in the fields of demography, gerontology, and sociology. It is mainly explored in the context of ageing, as it is poised to become one of the most significant demographic phenomena and social issues in contemporary South Africa. Therefore, this study examined the determinants associated with grandparents who are parenting as caregivers and the health challenges they are exposed to as caregivers. The National Income Dynamics Study (NIDS) Wave 5 dataset was utilised, and a total of 302,476 grandparents aged 25 years and older, who were reported to be primary caregivers of double orphans, were included in the analysis. Both bivariate and multivariate binary logistic regressions were performed to determine the predictors of the determinants of grandparents parenting as caregivers and their health challenges in South Africa. Estimated odds ratios (ORs) with 95% confidence intervals (CIs) were used, and the threshold for statistical significance was established at ρ < 0.05. A majority of the male and female grandparent caregivers were aged 24–34 years, were Black Africans (69.8%), had secondary education (46.9%), reported health challenges (HC) (59.7%), with 26.4% reporting headaches in the last 30 days. Logistic regression revealed that grandparent caregivers aged 55–64 years were 8.9 times more likely to report health challenges compared to those aged 25–34 years. Non-Black African grandparent caregivers were found to be 0.61 times less likely to report health challenges, compared to Black African grandparent caregivers. Those with perceived poor health status were 3.3 times more likely to report health challenges, compared to those with excellent perceived health status. Therefore, there is an urgent need to redesign health interventions to address these health burdens among grandparent caregivers and to take cognisance of providing economic and social support for these vulnerable populations.
A Practical Adversarial Attack Against Sequence-Based Deep Learning Malware Classifiers

Sequence-based deep learning models (e.g., RNNs), can detect malware by analyzing its behavioral sequences. Meanwhile, these models are susceptible to adversarial attacks. Attackers can create adversarial samples that alter the sequence characteristics of behavior sequences to deceive malware classifiers. The existing methods for generating adversarial samples typically involve deleting or replacing crucial behaviors in the original data sequences, or inserting benign behaviors that may violate the behavior constraints. However, these methods that directly manipulate sequences make adversarial samples difficult to implement or apply in practice. In this paper, we propose an adversarial attack approach based on Deep Q-Network and a heuristic backtracking search strategy, which can generate perturbation sequences that satisfy practical conditions for successful attacks. Subsequently, we utilize a novel transformation approach that maps modifications back to the source code, thereby avoiding the need to directly modify the behavior log sequences. We conduct an evaluation of our approach, and the results confirm its effectiveness in generating adversarial samples from real-world malware behavior sequences, which have a high success rate in evading anomaly detection models. Furthermore, our approach is practical and can generate adversarial samples while maintaining the functionality of the modified software.
A Longitudinal Examination of Multiple Forms of Stigma on Minority Stress, Belongingness, and Problematic Alcohol Use

Abstract College students who experience stigma report problematic alcohol use. However, the stigma-health link focuses on one form of stigma, thereby excluding the intersectional oppression of experiencing multiple forms of stigma. The present work has two primary aims: 1) evaluating whether additive intersectional minority stress confers greater problematic alcohol use among multiply-stigmatized college students one year later, and 2) whether that link can be explained by 1) lower belongingness and 2) greater drinking to cope motives. Students (N=427) ranging in stigmatized identities (14.3% zero; 46.4% one; 29.5% two; 9.8% three or more), participated in an annual health survey at two subsequent fall semesters (2020 to 2021). Structural equation modeling tested the hypothesized model on relations between number of stigmatized identities, minority stressors, belongingness, and coping motive on problematic drinking (risky and problem drinking) one year later. As hypothesized, holding more stigmatized identities predicted higher minority stress, which in turn predicted less belonging. Partially consistent with expectations, lower belonging predicted more problem drinking, but less risky drinking. As expected, higher minority stress predicted higher drinking to cope motives, which in turn, predicted more problem drinking, and risky drinking. In conclusion, belongingness and drinking to cope may be potential mechanisms through which multiply-stigmatized students experience future problem drinking, but that may not always confer to more risky drinking. Implications for universities include implementation of 1) campus-wide belonging interventions for students facing stigma, and 2) initiatives to teach alternative coping strategies that reduce drinking to cope as a strategy to reduce the impact of minority stressors.
CECNet: Coordinate Encoding Competitive Neural Network For Palm Vein Recognition by Soft Large Margin Centralized Cosine Loss

Palm vein recognition plays a crucial role in identity verification, requiring highly discriminative features. However, for touchless palm vein datasets, selecting a suitable and fixed Region of Interest (ROI) is challenging due to variations in capture scales and anisotropy. Moreover, manually annotating ROIs for each palm vein image is a time-consuming and labor-intensive task. To address these challenges, the method based on competitive mechanism is currently a popular approach for palm vein recognition. However, traditional competitive mechanisms only focus on selecting winners from different channels without considering the spatial information of features. In this paper, we reformulate the traditional competition mechanism and propose a Coordinate Encoding Competitive Neural Network (CECNet). Our method takes into account the spatial competition relationship between features, which means we pay attention to features of different directions and scales. We also perform spatial encoding on the competitive features to extract a more comprehensive set of competitive features. To extract the textures, the CECNet employs three parallel Adaptive Gabor Filter Encoders (AGFEs) to learn features of different directions and scales, effectively capturing the variations present in palm vein images. To enhance feature discrimination, the Soft Large Margin Centralized Cosine Loss (SLMCCL) function is utilized, taking into account with inter-class separation and introducing centralized cosine similarity to achieve better intra-class similarity. By optimizing this loss function, the network learns to prioritize and rank features based on their importance. Experimental results on public palm vein datasets demonstrate the effectiveness of the proposed approach.
Imaging in interventional oncology, the better you see, the better you treat

Imaging and image processing is the fundamental pillar of interventional oncology in which diagnostic, procedure planning, treatment and follow‐up are sustained. Knowing all the possibilities that the different image modalities can offer is capital to select the most appropriate and accurate guidance for interventional procedures. Despite there is a wide variability in physicians preferences and availability of the different image modalities to guide interventional procedures, it is important to recognize the advantages and limitations for each of them. In this review, we aim to provide an overview of the most frequently used image guidance modalities for interventional procedures and its typical and future applications including angiography, computed tomography (CT) and spectral CT, magnetic resonance imaging, Ultrasound and the use of hybrid systems. Finally, we resume the possible role of artificial intelligence related to image in patient selection, treatment and follow‐up.
Modeling the cybersecurity of hospitals in natural and man-made hazards

Abstract Hospital cybersecurity has become a growing concern with an increasing number of cyberattacks against hospitals and health care facilities. We aim to tackle this issue by developing a model to capture the vulnerabilities of cyber capabilities of hospitals during hazards and proposing novel techniques to address the vulnerabilities. In this paper, we propose a novel technique, called self-protecting electronic medical records (EMRs), to provide Medical Records Services in hospitals with higher cyber capabilities against communications failure and cyber terrorism. We use fault tree analysis, a top–down deductive risk assessment tool, to analyze the failure mechanism of Medical Records Services in hospitals with and without using the self-protecting EMR technique against hazards. This work provides a refined understanding of the interactions between cyberinfrastructure and hospital functioning in natural and man-made hazards, and contributes to preventing cascading failures in hospital functionality and enhancing resilience of health care systems and communities.
Posttranslational modifications of ACE2 protein: Implications for SARS‐CoV‐2 infection and beyond

The present worldwide pandemic of coronavirus disease 2019 (COVID‐19) has highlighted the important function of angiotensin‐converting enzyme 2 (ACE2) as a receptor for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) entry. A deeper understanding of ACE2 could offer insights into the mechanisms of SARS‐CoV‐2 infection. While ACE2 is subject to regulation by various factors in vivo, current research in this area is insufficient to fully elucidate the corresponding pathways of control. Posttranslational modification (PTM) is a powerful tool for broadening the variety of proteins. The PTM study of ACE2 will help us to make up for the deficiency in the regulation of protein synthesis and translation. However, research on PTM‐related aspects of ACE2 remains limited, mostly focused on glycosylation. Accordingly, a comprehensive review of ACE2 PTMs could help us better understand the infection process and provide a basis for the treatment of COVID‐19 and beyond.
A Switched Control Strategy for Swing-Up and State Regulation for the Rotary Inverted Pendulum

The rotary inverted pendulum (RIP) is an underactuated mechanical system that can be balanced in the upward position, by applying the linear control theory based on a nonlinear dynamical model linearized around that position. Its structure makes it possible to implement controllers designed for energy minimization in the non-linearizable region. Due to the limited validity of the resulting linear model, a nonlinear control is needed to automatically move the pendulum close to the linearized region, starting from its stable downward position. A RIP platform has been built to experimentally test a switched control strategy design, which combines an energy-based nonlinear controller for swinging up the pendulum and a linear quadratic regulator for the balancing task. Experimental results validate the performance of this approach on the physical prototype.
The TiHoCL panel for canine lymphoma: a feasibility study integrating functional genomics and network biology approaches for comparative oncology targeted NGS panel design

Targeted next-generation sequencing (NGS) enables the identification of genomic variants in cancer patients with high sensitivity at relatively low costs, and has thus opened the era to personalized human oncology. Veterinary medicine tends to adopt new technologies at a slower pace compared to human medicine due to lower funding, nonetheless it embraces technological advancements over time. Hence, it is reasonable to assume that targeted NGS will be incorporated into routine veterinary practice in the foreseeable future. Many animal diseases have well-researched human counterparts and hence, insights gained from the latter might, in principle, be harnessed to elucidate the former. Here, we present the TiHoCL targeted NGS panel as a proof of concept, exemplifying how functional genomics and network approaches can be effectively used to leverage the wealth of information available for human diseases in the development of targeted sequencing panels for veterinary medicine. Specifically, the TiHoCL targeted NGS panel is a molecular tool for characterizing and stratifying canine lymphoma (CL) patients designed based on human non-Hodgkin lymphoma (NHL) research outputs. While various single nucleotide polymorphisms (SNPs) have been associated with high risk of developing NHL, poor prognosis and resistance to treatment in NHL patients, little is known about the genetics of CL. Thus, the ~100 SNPs featured in the TiHoCL targeted NGS panel were selected using functional genomics and network approaches following a literature and database search that shielded ~500 SNPs associated with, in nearly all cases, human hematologic malignancies. The TiHoCL targeted NGS panel underwent technical validation and preliminary functional assessment by sequencing DNA samples isolated from blood of 29 lymphoma dogs using an Ion Torrent™ PGM System achieving good sequencing run metrics. Our design framework holds new possibilities for the design of similar molecular tools applied to other diseases for which limited knowledge is available and will improve drug target discovery and patient care.
Development of a multifunctional bioreactor to evaluate the promotion effects of cyclic stretching and electrical stimulation on muscle differentiation

Abstract A multifunctional bioreactor was fabricated in this study to investigate the facilitation efficiency of electrical and mechanical stimulations on myogenic differentiation. This bioreactor consisted of a highly stretchable conductive membrane prepared by depositing polypyrrole (PPy) on a flexible polydimethylsiloxane (PDMS) film. The tensile deformation of the PPy/PDMS membrane can be tuned by adjusting the channel depth. In addition, PPy/PDMS maintained its electrical conductivity under continuous cyclic stretching in the strain range of 6.5%–13% for 24 h. This device can be used to individually or simultaneously perform cyclic stretching and electrical stimulation. The results of single stimulation showed that either cyclic stretching or electrical stimulation upregulated myogenic gene expression and promoted myotube formation, where electrical stimulation improved better than cyclic stretching. However, only cyclic stretching can align C2C12 cells perpendicular to the stretching direction, and electrical stimulation did not affect cell morphology. Myosin heavy chain (MHC) immunostaining demonstrated that oriented cells under cyclic stretching resulted in parallel myotubes. The combination of these two stimuli exhibited synergetic effects on both myogenic gene regulation and myotube formation, and the incorporated electrical field did not affect the orientation effect of the cyclic stretching. These results suggested that these two treatments likely influenced cells through different pathways. Overall, the simultaneous application of cyclic stretching and electrical stimulation preserved both stimuli's advantages, so myo‐differentiation can be highly improved to obtain abundant parallel myotubes, suggesting that our developed multifunctional bioreactor should benefit muscle tissue engineering applications.
Review of Analytical Methods for Chlorine Isotopes in Chlorinated Volatile Organic Compounds and Application in Groundwater Contamination

Organic contamination in groundwater and the impact on human health have been a growing concern.In the study of organic chlorine pollution,applying chlorine isotope(37Cl) can be applied to identify pollution sources and to study pollution mechanism at the atomic level,providing a favorable tool for more effective work on the organic pollution of groundwater.In this paper,eight kinds of chlorinated VOCs chlorine isotopic testing methods and techniques were discussed on the basis of the literatures information at home and abroad.And some applications of the chlorinated VOCs chlorine isotopes were shown,including the identification of groundwater pollution sources,monitoring the process of degradation of organic pollutants,verifying results of the restoration measures,distinguishing the manufacturers of Chlorinated VOCs and tracing the migration and the mixing process of chlorinated VOCs in soil and water.Advanced testing process should be developed and studies should be carried out on the mechanism of organic chlorine isotopes fractionation as soon as possible in China.At the same time,the use of C and Cl isotopes technology in chlorinated VOCs contamination and restoration should also be enhanced.
The unknown planktonic foraminiferal pioneer Henry A. Buckley and his collection at The Natural History Museum, London

The Henry Buckley Collection of Planktonic Foraminifera at the Natural History Museum in London (NHMUK) consists of 1665 single-taxon slides housing 23 897 individuals from 203 sites in all the major ocean basins, as well as a vast research library of Scanning Electron Microscope (SEM) photomicrographs. Buckley picked the material from the NHMUK Ocean-Bottom Deposit Collection and also from fresh tow samples. However, his collection remains largely unused as he was discouraged by his managers in the Mineralogy Department from working on or publicizing the collection. Nevertheless, Buckley published pioneering papers on isotopic interpretation of oceanographic and climatic change and was one of the first workers to investigate foraminiferal wall structure using the SEM technique. Details of the collection and images of each slide are available via the NHMUK Data Portal (http://dx.doi.org/10.5519/0035055). The Buckley Collection and its associated Ocean-Bottom Deposit Collection have great potential for taxon-specific studies as well as geochemical work, and both collections are available on request.
AJRM as basis for design and construction of more than 70 km of tunnels of the Railway Project Stuttgart‐Ulm

The tunnels of the railway project Stuttgart‐Ulm are located in sedimentary rock of the Keuper Formation, the Black Jurassic, the Brown Jurassic and the White Jurassic formation. The two longest tunnels are excavated by tunnel boring machine over most of their length. The remaining tunnels, which the authors are involved in, are excavated by conventional tunnelling. All tunnels are designed on the basis of the AJRM‐method. This method is based on an anisotropic, elasto‐viscoplastic model for jointed rock and corresponding FE‐software, which has been extended to seepage and swelling. The method has been applied to practical projects for the last 40 years. By means of rock mechanical test programs and back‐analyses of projects in a variety of rocks, the reliability of the predictions is quite high and the application leads to safe and economic solutions.
Challenges and Opportunities in the Process Development of Chimeric Vaccines

Vaccines are integral to human life to protect them from life-threatening diseases. However, conventional vaccines often suffer limitations like inefficiency, safety concerns, unavailability for non-culturable microbes, and genetic variability among pathogens. Chimeric vaccines combine multiple antigen-encoding genes of similar or different microbial strains to protect against hyper-evolving drug-resistant pathogens. The outbreaks of dreadful diseases have led researchers to develop economical chimeric vaccines that can cater to a large population in a shorter time. The process development begins with computationally aided omics-based approaches to design chimeric vaccines. Furthermore, developing these vaccines requires optimizing upstream and downstream processes for mass production at an industrial scale. Owing to the complex structures and complicated bioprocessing of evolving pathogens, various high-throughput process technologies have come up with added advantages. Recent advancements in high-throughput tools, process analytical technology (PAT), quality-by-design (QbD), design of experiments (DoE), modeling and simulations, single-use technology, and integrated continuous bioprocessing have made scalable production more convenient and economical. The paradigm shift to innovative strategies requires significant attention to deal with major health threats at the global scale. This review outlines the challenges and emerging avenues in the bioprocess development of chimeric vaccines.