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Using DNA metabarcoding and direct behavioural observations to identify the diet of proboscis monkeys (Nasalis larvatus) in the Kinabatangan Floodplain, Sabah
Characterizing the feeding ecology of threatened species is essential to establish appropriate conservation strategies. We focused our study on the proboscis monkey (Nasalis larvatus), an endangered primate species which is endemic to the island of Borneo. Our survey was conducted in the Lower Kinabatangan Wildlife Sanctuary (LKWS), a riverine protected area that is surrounded by oil palm plantations. We aimed to determine the diet of multiple proboscis monkey groups by using two methods. First, we conducted boat-based direct observations (scan and ad libitum sampling) and identified 67 plant species consumed by the monkeys at their sleeping sites in early mornings and late afternoons. Secondly, we used the DNA metabarcoding approach, based on next-generation sequencing (NGS, MiSeq Illumina) of faecal samples (n = 155), using the short chloroplast sequence, the trnL (UAA) P6 loop. In addition, we built a DNA reference database with the local plants available in the LKWS. When combining feeding data from both methods, we reported a diverse dietary ecology in proboscis monkeys, with at least 89 consumed plant taxa, belonging to 76 genera and 45 families. Moreover, we were able to add 22 new genera as part of the diet of this endangered colobine primate in the LKWS. The two methods provided congruent and complementary results, both having their advantages and limitations. This study contributed to enhance the knowledge on the feeding ecology of proboscis monkeys, highlighting the significance of several plant species that should further be considered in habitat restoration plans or corridor establishment.
Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning
AbstractInterstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the most common type of ILD, the pathogenesis of idiopathic pulmonary fibrosis (IPF) has not been fully elucidated. In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan–Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database. CCL2 was identified as a common characteristic gene of IPF and SSc. The genes associated with CCL2 expression in both diseases were enriched mainly in chemokine-related pathways and lipid metabolism-related pathways according to Gene Set Enrichment Analysis. Single-cell RNA sequencing (sc-RNAseq) revealed a significant difference in CCL2 expression in alveolar epithelial type 1/2 cells, mast cells, ciliated cells, club cells, fibroblasts, M1/M2 macrophages, monocytes and plasma cells between IPF patients and healthy donors. Statistical analyses revealed that CCL2 was negatively correlated with lung function in IPF patients and decreased after mycophenolate mofetil (MMF) treatment in SSc patients. Finally, we identified CCL2 as a common biomarker from IPF and SSc, revealing the common mechanism of these two diseases and providing clues for the study of the treatment and mechanism of these two diseases.
Automated CAD system for early detection and classification of pancreatic cancer using deep learning model
Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems. In the preprocessing stage, the input image resizes into 227 × 227 dimensions then converts the RGB image into a grayscale image, and enhances the image by removing noise without blurring edges by applying anisotropic diffusion filtering. In the segmentation stage, the preprocessed grayscale image a binary image is created based on a threshold, highlighting the edges by Sobel filtering, and watershed segmentation to segment the tumor region and we also implement the U-Net method for segmentation. Then refine the geometric structure of the image using morphological operation and extracting the texture features from the image using a gray-level co-occurrence matrix computed by analyzing the spatial relationship of pixel intensities in the refined image, counting the occurrences of pixel pairs with specific intensity values and spatial relationships. The detection stage analyzes the tumor region’s extracted features characteristics by labeling the connected components and selecting the region with the highest density to locate the tumor area, achieving a good accuracy of 99.64%. In the classification stage, the system classifies the detected tumor into the normal, pancreatic tumor, then into benign, pre-malignant, or malignant using a proposed reduced 11-layer AlexNet model. The classification stage attained an accuracy level of 98.72%, an AUC of 0.9979, and an overall system average processing time of 1.51 seconds, demonstrating the capability of the system to effectively and efficiently identify and classify pancreatic cancers.
Canagliflozin alleviates acetaminophen-induced renal and hepatic injury in mice by modulating the p-GSK3β/Fyn-kinase/Nrf-2 and p-AMPK-α/STAT-3/SOCS-3 pathways
AbstractDespite the fact that canagliflozin (Cana), a sodium-glucose cotransporter 2 inhibitor, is an anti-diabetic medication with additional effects on the kidney, there is limited experimental data to deliberate its hepato-reno-protective potentiality. Acetaminophen (APAP) overdose remains one of the prominent contributors to hepato-renal damage. Aim: Our study assessed the novel effect of Cana against APAP-induced toxicities. Main methods: mice were randomized into five groups: negative control, Cana25, APAP, Cana10 + APAP, and Cana25 + APAP. Cana was given for 5 days; a single dose of APAP was injected on the 6th day, followed by the scarification of animals 24 h later. Key findings: Pre-treatment with Cana ameliorated hepatic and renal functions, whereas, on the molecular levels, Cana promoted hepatic/renal P-AMP-activated protein kinase-α/ protein kinase B (p-Akt)/Glycogen synthase kinase (p-GSK3β) protein expression. Alternatively, Cana dampened the expression of STAT-3 and Fyn-kinase genes with a subsequent increase in the contents of suppressor of cytokine signaling (SOCS)-3 and also boosted the contents of the nuclear factor erythroid related factor 2 (Nrf-2)/heme oxygenase (HO)-1/ NADPH quinone oxidoreductase (NQO)-1 axis. The crosstalk between these paths ameliorated the APAP-induced hepatorenal structural alterations. Significance: Cana hepatorenal protective impact was provoked partly through modulating p-AMPK-α /SOCS-3/STAT-3 and GSK3β/Fyn-kinase signaling for its anti-inflammatory and antioxidant effects.
The survey of vaccination hesitancy among the residents in Jinan
Introduction Vaccination is an important way to prevent disease, but vaccine hesitancy will impact vaccine coverage and indirectly affect health. This study aims to survey the status of vaccine hesitancy among adults in Jinan. Methods A cross-sectional study was conducted using the vaccine hesitancy scale among the parents of children and teenagers at hospitals in Jinan, China. We described the attitude of the parents to the vaccination through the dimensions of confidence (items: L1-L7) and the risk (items:L8-L10).The participants will be regarded as lacking confidence if the score is over 21 among the items (L1-L7), and participants will consider the vaccination to be a “Risk” if the score is over 9 among the items (L8-L10). Using the chi-square test to analyse the differences of attitude between different participants. Results 202 individuals were enrolled, and most respondents (88.70%) agreed that vaccines are important for their child’s health. 33.50% agreed and strongly agreed that new vaccines carried more risks than older vaccines. The average score for the lack of confidence in the vaccination was 11±0.25. The average score for risk for vaccination was 9.92±0.04. Participants aged below 30 years, females, those with lower education, and those without medical workers in the family were more concerned about the risks of vaccines. Conclusions Participants were confident about the vaccination. But they were also concerned about the risks of vaccines. A lack of vaccine knowledge may led the participants to have hesitancy about vaccinations.
Assessing the potential impact of grasshopper outbreaks on Patagonian wetlands through mathematical modelling
Mental health in Germany before, during and after the COVID-19 pandemic
Based on nationally representative panel data (N person-years = 40,020; N persons = 18,704; Panel Labour Market and Social Security; PASS) from 2018 to 2022, we investigate how mental health changed during and after the COVID-19 pandemic. We employ time-distributed fixed effects regressions to show that mental health (Mental Health Component Summary Score of the SF-12) decreased from the first COVID-19 wave in 2020 onward, leading to the most pronounced mental health decreases during the Delta wave, which began in August 2021. In the summer of 2022, mental health had not returned to baseline levels. An analysis of the subdomains of the mental health measure indicates that long-term negative mental health changes are mainly driven by declines in psychological well-being and calmness. Furthermore, our results indicate no clear patterns of heterogeneity between age groups, sex, income, education, migrant status, childcare responsibilities or pre-COVID-19 health status. Thus, the COVID-19 pandemic appears to have had a uniform effect on mental health in the German adult population and did not lead to a widening of health inequalities in the long run.
Clustering of > 145,000 symptom logs reveals distinct pre, peri, and menopausal phenotypes
Cell recruitment and the origins of Anterior-Posterior asymmetries in the Drosophila wing
The mechanisms underlying the establishment of asymmetric structures during development remain elusive. The wing of Drosophila is asymmetric along the Anterior-Posterior (AP) axis, but the developmental origins of this asymmetry is unknown. Here, we investigate the contribution of cell recruitment, a process that drives cell fate differentiation in the Drosophila wing disc, to the asymmetric shape and pattern of the adult wing. Genetic impairment of cell recruitment in the wing disc results in a significant gain of AP symmetry, which results from a reduction of the region between longitudinal vein 5 and the wing margin (L5-M) in the adult wing. Morphometric analysis confirms that blocking of cell recruitment results in a more symmetric wing with respect to controls, suggesting a contribution of cell recruitment to the establishment of asymmetry in the adult wing. In order to verify if this phenotype is originated during the time in which cell recruitment occurs during larval development, we examined the expression of a reporter for the selector gene vestigial (vg) in the corresponding pro-vein regions of the wing disc, but our findings could not explain our findings in adult wings. However, the circularity of the Vg pattern significantly increases in recruitment-impaired wing discs, suggesting that cell recruitment may contribute to AP asymmetries in the adult wing shape by altering the roundness of the Vg pattern. We conclude that cell recruitment, a widespread mechanism that participates in growth and patterning of several developing systems, may contribute, at least partially, to the asymmetric shape of the Drosophila wing.
Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions
Development and validation of the health education demand scale for HPV infected patients based on KANO model
Objective The purpose of this study is to develop and validate the scale of health education demand of patients with HPV infection based on KANO model, so as to provide a tool for further exploring the types of health education demand and influencing factors of patients with HPV infection. Methods This study is a scale development and validation study using a three-stage cross-sectional design. In stage 1, a preliminary item pool is formed using literature review, semi-structured interviews and the Delphi method. In stage 2, six experts were invited to assess content validity. A cross-sectional survey was conducted on 1169 patients with HPV infection, Questionnaire results from 583 patients were used for exploratory factor analysis. In stage 3, the remaining 586 patients to validate the factor structure through confirmatory factor analysis. Results In stage 1, an initial 35-item scale was developed and the items were transformed positive and reverse based on KANO model. In stage 2, Exploratory factor analysis formed a scale of 28 items in 5 factors: disease information demand, social support, emotional demand, family support and health education style demand. Cronbach’s alpha was 0.940 for the entire scale and 0.763~0.908 for the five subscales in the positive items, 0.955 for the entire scale and 0.739~0.946 for the five subscales in reverse items. The content validity index of the scale: S-CVI/UA = 0.91, S-CVI/Ave = 0.98. In stage 3, the confirmatory factor analysis showed that the χ2/df, RMSEA, CFI and TLI of the positive items after four model modifications were 3.650, 0.067, 0.901, 0.888, and the SRMR value was < 0.001. The fitting of the five-factor model was good. Conclusion The KANO model based questionnaire on health education demand of HPV infected patients has good reliability and validity, and is suitable for the investigation of health education demand of HPV infected patients.
Association of cardiometabolic index with all-cause and cardiovascular mortality among middle-aged and elderly populations
Suboptimal dietary knowledge predicts lower diet quality for cancer prevention among university students in Beirut
University students are at a pivotal stage of shaping cancer risk factors. Little is known about their dietary behavior in Lebanon, a country heavily burdened by cancer. This cross-sectional study assessed the dietary knowledge of and adherence to cancer prevention guidelines among university students in Beirut, Lebanon. We hypothesized that students would exhibit low knowledge, poor diet quality, and that knowledge predicted diet quality. Dietary knowledge was explored using a dedicated questionnaire, with scores above the 60th percentile considered as Knowledgeable (Kn+), and those below as less knowledgeable (Kn-). Dietary adherence to cancer prevention guidelines and the predictors of the Alternative Healthy Eating Index (AHEI)- a measure of diet quality calculated using the Modified Mediterranean Prime Screen, were also examined. The sample included 300 participants (55% females, mean age: 20 years). The mean knowledge score was 49.5%. Over 50% of students were aware of the association between red and processed meat, sodium, fruits and vegetables, obesity, and cancer. Kn+ group had a higher intake of vegetables and a lower intake of meats and sweetened beverages. Increased knowledge (B = 0.78, 95%CI: 0.18,1.37) and high physical activity (B = 4.62, 95%CI: 1.66,7.59) were associated with elevated AHEI scores. A significant positive interaction was observed between knowledge and enrollment in a health-related major. University students’ dietary knowledge of and adherence to cancer prevention guidelines are suboptimal. Although higher knowledge predicts high-quality diets, the association was weak. Further studies should investigate the food systems influencing university students’ dietary intake of university students in Lebanon and identify effective interventions to enhance health behavior.
Comparison between AI and human expert performance in acute pain assessment in sheep
Correction: Calcium-Activated-Calcineurin Reduces the In Vitro and In Vivo Sensitivity of Fluconazole to Candida albicans via Rta2p
Effect of magnetized water on the fundamental grouting properties of cement grout under varying magnetization conditions
Early neutrophil activation and NETs release in the pristane-induced lupus mice model
Background NETosis is recognized as an important source of autoantigens. Therefore, we hypothesized whether the pristane-induced lupus mice model shows early activation of neutrophils, the presence of low-density granulocytes (LDGs), and neutrophil extracellular traps (NETs) release, which could contribute to the development of a lupus phenotype. Methods Twelve female wild-type Balb/c mice were intraperitoneally injected with pristane (n = 6; pristane group) or saline (n = 6; control group). Five days after the injection, blood, peritoneal lavage, bone marrow, and spleen samples were collected for flow cytometry analyses of activated neutrophils (Ly6G+CD11b+), LDGs (CD15+CD14low), and NETs release (Sytox Green+). Results The pristane-induced mice group had a significantly increased number of blood activated neutrophils and LDGs as well as NETs released by these cells compared to the saline-injected control group and the basal values determined 12 days before the injection. The pristane group also had a significantly increased number of activated neutrophils, LDGs, and NETs released compared to the control group for the peritoneal lavage and bone marrow, except total cell count in spleen. Conclusions We demonstrated early changes in the innate immune response such as an increased number of activated neutrophils and LDGs and mainly increased NETosis in the pristane-induced mice model which may be considered as the primary event triggering lupus development.
Impact of urbanization on antimicrobial resistance in soil microbial communities
Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task
Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention. However, traditional KELM algorithms have some problems when dealing with large-scale data, such as the need to adjust hyperparameters, poor interpretability, and low classification accuracy. To address these problems, this paper proposes an Enhanced Adaptive Whale Optimization Algorithm to optimize Kernel Extreme Learning Machine (EAWOA-KELM). Various methods were used to improve WOA. As a first step, a novel adaptive perturbation technique employing T-distribution is proposed to perturb the optimal position and avoid being trapped in a local maximum. Secondly, the WOA’s position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. EAWOA demonstrates superior optimization accuracy compared to WOA across 21 test functions, with a notable edge on certain functions. The application of the upgraded WOA algorithm in KELM significantly improves the accuracy and efficiency of data classification by optimizing hyperparameters. This paper selects 7 datasets for classification experiments. Compared with the KELM optimized by WOA, the EAWOA optimized KELM in this paper has a significant improvement in performance, with a 5%-6% lead on some datasets, indicating the effectiveness of EAWOA-KELM in classification tasks.