Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring odontocete depredation rates in a pelagic longline fishery

Several odontocete species depredate catch and bait from fishing gear, resulting in their bycatch and causing substantial economic costs. There are no known mitigation methods for odontocete depredation in pelagic longline fisheries that are effective, do not harm odontocetes and are commercially viable. Understanding odontocetes’ depredation strategies can contribute to mitigating this human-wildlife conflict. Using observer data from the Hawaii-based tuna longline fishery, this study summarized teleost and elasmobranch species-specific mean posterior odontocete depredation rates using a simple Bayesian binomial likelihood estimator with a Bayes-Laplace prior. Depredation rates of species with sufficient sample sizes ranged from a high of 1.2% (1.1 to 1.3 95% highest posterior density interval or HDI) for shortbill spearfish to a low of 0.002% (0.001 to 0.003 95% HDI) for blue shark. Depredation of catch is a rare event in this fishery, occurring in about 6% of sets. When depredation did occur, most frequently odontocetes depredated a small proportion of the catch, however, there was large variability in depredation rates between teleost species. For example, bigeye tuna was two times more likely to be depredated than yellowfin tuna (odds ratio = 2.03, 95% CI: 1.8–2.3, P<0.0001). For sets with depredation, 10% and 2% of sets had depredation of over half of the captured bigeye tuna and combined teleosts, respectively. All elasmobranch species had relatively low depredation rates, where only 7 of almost 0.5M captured elasmobranchs were depredated. Odontocetes selectively depredate a subset of the teleost species captured within sets, possibly based on net energy value, chemical, visual, acoustic and textural characteristics and body size, but not median length, which was found to be unrelated to depredation rate (Pearson’s r = 0.14, 95% CI: -0.26 to 0.50, p = 0.49). Study findings provide evidence to support the identification and innovation of effective and commercially viable methods to mitigate odontocete depredation and bycatch.
RTC_TongueNet: An improved tongue image segmentation model based on DeepLabV3

Objective Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_TongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. Methods In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. Results The RTC_TongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_TongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_TongueNet model performed best on both datasets. Conclusion In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_TongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.
Factors associated with hepatitis A susceptibility among men who have sex with men using HIV pre-exposure prophylaxis in Northeastern Brazil: A cross-sectional study

Hepatitis A virus (HAV) infection has disproportionately affected more men who have sex with men (MSM), occurring in outbreaks, despite being vaccine-preventable. We determined the prevalence and factors associated with HAV susceptibility among cisgender MSM on HIV pre-exposure prophylaxis (PrEP) in Northeastern Brazil. From September 30, 2021 to June 19, 2023, 282 cisgender MSM receiving HIV PrEP were enrolled into this cross-sectional study. Sociodemographic and clinical information were collected. Blood samples were collected for screening of sexually transmitted infections (STIs) and serum samples were tested for IgM and total anti-HAV antibodies. Non-reactive results for total anti-HAV antibodies were found in 106 of 282 (37.6%) participants. Factors associated with HAV susceptibility included age <30 years (prevalence ratio [PR]: 2.02; 95% confidence interval [95% CI]: 1.61–2.53), having health insurance (PR: 1.39; 95% CI: 1.19–1.64), sex only with cisgender men (PR: 1.52; 95% CI: 1.23–1.89), non-steady partner (PR: 1.20; 95% CI: 1.01–1.43) and no lifetime history of STIs (PR: 1.25; 95% CI: 1.03–1.53). Identifying clinical correlates of HAV susceptibility in key populations is a fundamental step towards development of public policy focused on prevention, especially following the recent hepatitis A outbreak in Brazil.
Changes in the intestinal microbiota of individuals with non-alcoholic fatty liver disease based on sequencing: An updated systematic review and meta-analysis

Background Alterations in the composition and abundance of the intestinal microbiota occur in non-alcoholic fatty liver disease (NAFLD). However, the results are inconsistent because of differences in the study design, subject area, and sequencing methodology. In this study, we compared the diversity and abundance of the intestinal microbiota of patients with NAFLD and healthy individuals through a systematic review and meta-analysis. Methods Three databases (PubMed, EMBASE, and Cochrane Library) were searched from their inception to March 20, 2023. A meta-analysis was performed using Stata software to analyze variations in the richness and abundance of the intestinal microbiota in patients with NAFLD. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used for quality assessment. Results A total of 28 articles were included. Shannon diversity was reduced in patients with NAFLD (SMD = -0.24 (95% CI -0.43–0.05, I2 = 71.7%). The relative abundance of Ruminococcus, Faecalibacterium, and Coprococcus all decreased, with total SMDs of -0.96 (95% CI -1.29 to -0.63, I2 = 4.8%), -1.13 (95% CI -2.07 to -0.19, I2 = 80.5%), and -1.66 (95% CI -3.04 to -0.28, I2 = 91.5%). Escherichia was increased in individuals with NAFLD (SMD = 1.78, 95% CI 0.12 to 3.45, I2 = 94.4%). Conclusion Increasing the species diversity and altering the abundance of specific gut microbiota, including Coprococcus, Faecalibacterium, Ruminococcus, and Escherichia, may be beneficial for improving NAFLD.
Predicting Fault-Prone Software Modules with Rank Sum Classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.
Field Dependence-Independence and Eye Movement Patterns: Investigating Users' Differences Through an Eye Tracking Study

The notion that users’ characteristics such as the cognitive ability affect their visual information processing is continuously increasing. This eye tracking user study investigated the association between adults’ (N = 54) cognitive style and eye movement (EM) patterns while interacting with a set of visual tasks. Users’ cognitive type was measured with the use of the Hidden Figures Test (HFT), which classifies them into field dependent (FD), field neutral (FN) and field independent (FI) individuals. Overall, the psychometric measures and the eye tracking-derived data were statistically examined revealing significant differences and large effect sizes among the two variables tested. Particularly, the findings demonstrated that the field dependence group exemplifies a more disoriented and disorganized EM activity. Hence, greater numbers of fixations and saccades are generated, contrary to the FN or FI users’. This research study adds further to the evidence and theory base of human–computer interaction for applications in the user-centred design by identifying how users’ differences in cognitive style can be manifested in eye gaze patterns.
Morality, self-control, age, type of offence and sentence length as predictors of psychopathy amongst female incarcerated offenders in South Africa

There has been an increase in female incarcerated offenders nationally and internationally. Despite this trend, literature and research on female offenders remain limited compared to their male counterparts. Evidence of the relationship between certain personality disorders and offending behaviour has led numerous countries to prioritise identifying and assessing personality disorders among the offender population. Psychopathic personality traits may contribute to women’s risk factors for expressing antisocial behaviours, resulting in their potential future incarceration. Thus, a need exists to understand possible factors that may predict the expression of psychopathic traits in females, which may have notable utility among female offenders. This study aimed to investigate possible predictor variables of psychopathy amongst incarcerated female offenders in South Africa. A quantitative research approach, non-experimental research type, and correlational research design were employed. A convenience sampling technique was used. The sample consisted of 139 (N = 139) female offenders housed in two correctional centres in South Africa who voluntarily participated in this study. Correlation analyses and hierarchical regression analysis procedures were conducted to analyse the results. Results indicated (i) a certain combination of predictor variables that statistically and practically significantly explained both primary and secondary psychopathy and (ii) individual predictor variables (e.g., Impulsivity, Simple Tasks, Risk-Seeking, and Self-Centredness) that explained both primary and secondary psychopathy statistically and practically significantly. This study provides valuable information about the possible predictor variables of psychopathy amongst female offenders within the context of South Africa. However, further research must be conducted to validate these findings and advance our knowledge on this topic.
Molecular targeted agents as second-line treatment for hepatocellular carcinoma: a meta-analysis and review

It is unclear whether targeted agents can produce survival advantage in patients with advanced HCC previously treated with sorafenib. We performed this meta-analysis of randomized trials and reviewed clinical outcomes of molecular targeted agents in the second-line treatment for advanced HCC. A systematic computerized search of the electronic databases PubMed, Embase, Google Scholar, and Cochrane Library (up to May 2017) was carried out. From six studies, 2,388 patients were included in the meta-analysis. Almost all patients were treated with sorafenib as first-line therapy. Compared with placebo, targeted agents significantly improved time-to-progression (hazard ratio = 0.62, 95% confidence interval: 0.49–0.78, P < 0.0001). In terms of overall survival, targeted therapy tended to improve prognosis (hazard ratio = 0.86, 95% confidence interval: 0.74–1.01, P = 0.06). In conclusion, this meta-analysis indicates that molecular targeted agents have a potential to improve prognosis after failure of first-line treatment with sorafenib in patients with advanced HCC.
Occlusion handling for medical augmented reality using a volumetric phantom model

The support of surgical interventions has long been in the focus of application-oriented augmented reality research. Modern methods of surgery, like minimally-invasive procedures, can benefit from the additional information visualization provided by augmented reality. The usability of medical augmented reality depends on a rendering scheme for virtual objects designed to generate easily and quickly understandable augmented views. One important factor for providing such an accessible reality augmentation is the correct handling of the occlusion of virtual objects by real scene elements. The usually large volumetric datasets used in medicine are ill-suited for use as phantom models for static occlusion handling. We present a simple and fast preprocessing pipeline for medical volume datasets which extracts their visual hull volume. The resulting, significantly simplified visual hull iso-surface is used for real-time static occlusion handling in our AR system, which is based on off-the-shelf medical equipment.
Efficacy and safety of biologic agents for IgA nephropathy: A protocol for systematic review and meta-analysis

Background IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis worldwide and a leading cause of chronic kidney failure. There are currently no definitive therapeutic regimens to treat or prevent the progression of IgAN. However, biologic agents offer novel therapeutic approaches that target immunological mechanisms to slow or halt disease progression. The objective of this study is to evaluate the efficacy and safety of biologic agents in patients with IgA nephropathy. Methods We will systematically search PubMed, EMbase, Web of Science, Cochrane Library, and www.clinicaltrials. gov for randomized controlled trials of biologic agents for the treatment of IgA nephropathy. The search period will span from the establishment of each database until October 2023. The quality assessment of included studies will be performed individually using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2), and meta-analysis will be conducted using Revman 5.4. 1 software. Conclusions The results of this study will provide evidence-based medical evidence for the clinical application of biologic agents in patients with IgA nephropathy. Prospero registration number CRD42023400450.
Lyapunov-Function and Proportional-Resonant-Based Control Strategy for Single-Phase Grid-Connected VSI With LCL Filter

This paper presents a new control strategy based on Lyapunov-function and proportional-resonant (PR) controller for single-phase grid-connected LCL-filtered voltage-source inverters (VSIs). While Lyapunov-function-based control guarantees the global stability of the system, the PR controller is employed to process the grid current error and determine the inverter current reference. However, it is shown that the conventional Lyapunov-function-based control (CLFBC) together with the PR control cannot damp the inherent resonance of the LCL filter. Therefore, this control approach is modified by adding a capacitor voltage loop so as to achieve the desired resonance damping. In addition, a transfer function from the reference grid current to actual grid current is formulated in terms of the LCL-filter parameters and their possible variations in the proposed control strategy. An important consequence of using the PR controller is that the need for performing first and second derivative operations in the generation of inverter current reference is eliminated. Also, a zero steady-state error in the grid current is guaranteed in the case of variations in the LCL-filter parameters. The computer simulations and experimental results obtained from a 3.3-kW system show that the proposed control strategy exhibits a good performance in achieving the required control objectives such as fast dynamic response, zero steady-state error, global stability, and sinusoidal grid current with low total harmonic distortion (THD).
Channel selection in the short-time modulation domain for distant speech recognition

Automatic speech recognition from multiple distant microphones poses significant challenges because of noise and reverberations. The quality of speech acquisition may vary between microphones because of movements of speakers and channel distortions. This paper proposes a channel selection approach for selecting reliable channels based on selection criterion operating in the short-term modulation spectrum domain. The proposed approach quantifies the relative strength of speech from each microphone and speech obtained from beamforming modulations. The new technique is compared experimentally in the real reverb conditions in terms of perceptual evaluation of speech quality (PESQ) measures and word error rate (WER). Overall improvement in recognition rate is observed using delay-sum and superdirective beamformers compared to the case when the channel is selected randomly using circular microphone arrays.
Synthesis, Properties and Applications of Biodegradable Polymers Derived from Diols and Dicarboxylic Acids: From Polyesters to Poly(ester amide)s

Poly(alkylene dicarboxylate)s constitute a family of biodegradable polymers with increasing interest for both commodity and speciality applications. Most of these polymers can be prepared from biobased diols and dicarboxylic acids such as 1,4-butanediol, succinic acid and carbohydrates. This review provides a current status report concerning synthesis, biodegradation and applications of a series of polymers that cover a wide range of properties, namely, materials from elastomeric to rigid characteristics that are suitable for applications such as hydrogels, soft tissue engineering, drug delivery systems and liquid crystals. Finally, the incorporation of aromatic units and α-amino acids is considered since stiffness of molecular chains and intermolecular interactions can be drastically changed. In fact, poly(ester amide)s derived from naturally occurring amino acids offer great possibilities as biodegradable materials for biomedical applications which are also extensively discussed.
Piperine alleviates nonalcoholic steatohepatitis by inhibiting NF-κB-mediated hepatocyte pyroptosis

Purpose Nonalcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease (NAFLD), which has a high risk of cirrhosis, liver failure, and hepatocellular carcinoma. Piperine (Pip) is an extract of plants with powerful anti-inflammatory effects, however, the function of Pip in NASH remains elusive. Here, we aim to explore the role of Pip in NASH and to find the possible mechanisms. Methods Methionine and choline-deficient (MCD) diets were used to induce steatohepatitis, methionine- and choline-sufficient (MCS) diets were used as the control. After Pip treatment, H&E staining, Oil Red O staining, hepatic triglyceride (TG) content and F4/80 expression were performed to analysis liver steatosis and inflammation; Masson’s staining, COL1A1 and α-SMA were detected liver fibrosis. Lipopolysaccharide (LPS) -treated AML12 cells were used to as the cell model to induce pyroptosis. Then, pyroptosis-related proteins, IL-1β and LDH release were detected in vivo and in vitro. Finally, NF-κB inhibitor, BAY11-7082, was used to further demonstrate the mechanism of Pip in NASH. Results The study found that Pip alleviated liver steatosis, inflammation, hepatocyte injury, and fibrosis in mice fed with MCD diets. Moreover, the pyroptosis markers (NLRP3, ASC, caspase-1 p20, and GSDMD), IL-1β and LDH release were decreased by Pip treatment. NF-κB activation was suppressed by Pip treatment and pyroptosis-related proteins were down regulated by BAY11-7082. Conclusion Pip ameliorates NASH progression, and the therapeutical effect was associated with inhibition of hepatocyte pyroptosis induced by NF-κB.
Clusters of longitudinal risk profile trajectories are associated with cardiometabolic diseases: Results from the population-based KORA cohort

Background Multiple risk factors contribute jointly to the development and progression of cardiometabolic diseases. Therefore, joint longitudinal trajectories of multiple risk factors might represent different degrees of cardiometabolic risk. Methods We analyzed population-based data comprising three examinations (Exam 1: 1999–2001, Exam 2: 2006–2008, Exam 3: 2013–2014) of 976 male and 1004 female participants of the KORA cohort (Southern Germany). Participants were followed up for cardiometabolic diseases, including cardiovascular mortality, myocardial infarction and stroke, or a diagnosis of type 2 diabetes, until 2016. Longitudinal multivariate k-means clustering identified sex-specific trajectory clusters based on nine cardiometabolic risk factors (age, systolic and diastolic blood pressure, body-mass-index, waist circumference, Hemoglobin-A1c, total cholesterol, high- and low-density lipoprotein cholesterol). Associations between clusters and cardiometabolic events were assessed by logistic regression models. Results We identified three trajectory clusters for men and women, respectively. Trajectory clusters reflected a distinct distribution of cardiometabolic risk burden and were associated with prevalent cardiometabolic disease at Exam 3 (men: odds ratio (OR)ClusterII = 2.0, 95% confidence interval: (0.9–4.5); ORClusterIII = 10.5 (4.8–22.9); women: ORClusterII = 1.7 (0.6–4.7); ORClusterIII = 5.8 (2.6–12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1–15.6); ORClusterIII = 7.5 (2.4–32.7); women: ORClusterII = 5.0 (1.1–34.1); ORClusterIII = 8.0 (2.2–51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham). Conclusions On a population-based level, distinct longitudinal risk profiles over a 14-year time period are differentially associated with cardiometabolic events. Our results suggest that longitudinal data may provide additional information beyond single time-point measures. Their inclusion in cardiometabolic risk assessment might improve early identification of individuals at risk.