Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of endogenous glucagon-like peptide 1 to prandial counterregulatory responses to hypoglycemia after bariatric surgery

Aims/hypothesis: Exogenous glucagon-like peptide 1 (GLP-1) infusion lowers endogenous glucose production (EGP) in euglycemic or hyperglycemic settings. Previously, we have shown that prandial EGP during insulin-induced hypoglycemia is smaller in non-diabetic subjects with gastric bypass (GB) and sleeve gastrectomy (SG), where prandial GLP-1 concentrations are increased by 5-10 fold compared to non-operated controls. The goal of this study was to determine the effect of endogenous GLP-1 on prandial counterregulatory response to hypoglycemia. Methods: Glucose fluxes and islet-cell and gut hormone responses before and after mixed-meal ingestion were compared among 8 subjects with prior GB, 7 with prior SG, and 5 matched non-surgical controls during a hyperinsulinemic (120 mU/min/m2) hypoglycemic (~3.2 mmol/l) clamp with and without a specific GLP-1 receptor (GLP-1R) antagonist exendin-(9-39) (Ex-9). Results: Before meal ingestion, plasma glucagon and glucose fluxes were similar among 3 groups. GLP-1R blockade had no effect on insulin secretion or insulin action before or after meal ingestion whereas prandial glucagon was enhanced in all 3 groups (P < 0.05). Ex-9 infusion raised prandial EGP response to hypoglycemia in surgical groups (P < 0.05) but decreased this parameter in controls (P = 0.08 for interaction). The rates of systemic appearance of ingested glucose or prandial glucose utilization did not differ among 3 groups and between studies with and without Ex-9 infusion. Conclusions/interpretation: Under hypoglycemic condition, the glucagonostatic effect, but not insulinotropic action of GLP-1, is preserved in the prandial condition in humans. Endogenous GLP-1 contributes to the impaired post-meal glucose counterregulatory response to hypoglycemia in non-diabetic subjects after bariatric surgery.
Whole-genome resequencing analysis of the medicinal plant Gardenia jasminoides

Background Gardenia jasminoides is a species of Chinese medicinal plant, which has high medicinal and economic value and rich genetic diversity, but the study on its genetic diversity is far not enough. Methods In this study, one wild and one cultivated gardenia materials were resequenced using IlluminaHiSeq sequencing platform and the data were evaluated to understand the genomic characteristics of G. jasminoides. Results After data analysis, the results showed that clean data of 11.77G, Q30 reached 90.96%. The average comparison rate between the sample and reference genome was 96.08%, the average coverage depth was 15X, and the genome coverage was 85.93%. The SNPs of FD and YP1 were identified, and 3,087,176 and 3,241,416 SNPs were developed, respectively. In addition, SNP non-synonymous mutation, InDel mutation, SV mutation and CNV mutation were also detected between the sample and the reference genome, and KEGG, GO and COG database annotations were made for genes with DNA level variation. The structural gene variation in the biosynthetic pathway of crocin and gardenia, the main medicinal substance of G. jasminoides was further explored, which provided basic data for molecular breeding and genetic diversity of G. jasminoides in the future.
Predictors of underweight among adult patients receiving antiretroviral therapy in Bishoftu general hospital, central Ethiopia: Case-control study

Background Underweight affects the overall clinical outcome and quality of life and increases the risk of mortalities in Human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS) patients. Though studies have examined the various determinants of being underweight in people living with HIV/AIDS (PLHIV), scanty evidence exists about the influence of dietary diversity scores and dietary counseling on underweight HIV patients in Ethiopia. This study aimed to identify the determinants of being underweight among adult patients receiving antiretroviral therapy at Bishoftu General Hospital, central Ethiopia. Methods An institution-based unmatched case-control study was conducted among 279 participants (93 cases and 186 controls) from April to May 2022. Cases were selected consecutively as they occur, and then two subsequent controls that visited the antiretroviral therapy(ART) clinic were interviewed until the sample size was attained. Data were collected using a pretested structured questionnaire and involved patient interviews and chart review. Bivariate and Multivariable logistic regression was used to identify determinants of underweight. The presence of statistically significant association was declared with p-value <0.05, and a 95% confidence interval was used to show the precision in the measure of the strength of association. Results The response rate of participants was 91.2% for each of the cases and controls. Monthly income of patients ≤2000 birr (AOR = 6.63, 95% CI: 2.96–14.85), absence of support giver (AOR = 3.22, 95% CI: 1.38–7.50), being having an eating problem (AOR = 14.48, 95% CI: 5.06–41.40), dietary diversity score of four to five (AOR = 2.36, 95% CI: 1.92, 6.08), not getting dietary counseling support and advice (AOR = 2.53, 95% CI: 1.11, 5.72) and chewing khat (AOR = 3.36, 95% CI: 1.99,11.33) were determinants of underweight in adult HIV patients. Conclusions This study revealed that household dietary diversity, counseling and education on nutrition, monthly income, eating problems, support giver, and khat chewing were predictors of being underweight in HIV patients. This inquires an integrated nutritional intervention including income-generating activities, counseling and education on nutrition and bad habits, and regular monitoring of the nutritional status during clinic visits.
Global outlook of the multiplier effect of research and development on environmental sustainability

In a time of severe environmental problems and growing public and private expenditure to promote a sustainable society, studies on the impact of Research and Development (R&D) on environmental sustainability hardly ever exist. This study looked at how R&D promotes the attainment of environmentally sustainable goals, globally. Data at the country level was compiled from the United Nations-Sustainable Development Goals (UN-SDG) database and the World Bank, then processed and analysed using spatial techniques such as spatial merge, autofill, autocorrelation, and geographic weighted regression. Expenditure on R&D and publications in scientific journals had a positive impact on SDGs 8 and 11 for all nations. R&D expenditure on SDG 8 was higher for Southern African countries. The impact of scientific and technical journal articles was greater for Middle East countries. Also, scientific and technical journal articles had greater effects on attainment of SDG 11 in Africa. Exports of high technology from South America and Europe were important for achieving SDG 15. However, the effect of patent and trademark applications had minimal effect on achieving environmentally sustainable goals. The study recommends boosting R&D expenditure by governments and international organisations, particularly in Africa because the multiplier impact extends beyond economic development to environmental sustainability which is necessary for the continent to abate the challenges of climate change.
Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

Several automated aesthetic assessment models were developed to assist artists in producing artwork with high aesthetic appeal. However, most of them focused on static visual art, such as photographs and paintings, and evaluations of dynamic art received less attention. Dynamic visual art, especially computer-based art, includes diverse forms of artistic expression and can enhance an audience’s aesthetic experience. A model for evaluating dynamic visual art can provide valuable feedback and guidance for improving artistic skills and creativity, thereby benefiting audiences. In this study, we created eight generative artworks with dynamic art forms based on a commonly used method. We established a time-dependent model to predict the aesthetics based on visual features. We quantified the artworks according to selected image features and found that these features could effectively capture the characteristics of the changing visual forms during the generation process. To explore the effects of time-varying features on aesthetic appeal, we built a panel regression model and found that the aesthetic appeal of the generated artworks was significantly affected by their skewness of the luminance distribution, vertical symmetry, and mean hue value. Furthermore, our study demonstrated that the aesthetic appeal of dynamic generative artworks could be predicted by integrating image features into the temporal domain.
Prediction of respiratory failure risk in patients with pneumonia in the ICU using ensemble learning models

The aim of this study was to develop early prediction models for respiratory failure risk in patients with severe pneumonia using four ensemble learning algorithms: LightGBM, XGBoost, CatBoost, and random forest, and to compare the predictive performance of each model. In this study, we used the eICU Collaborative Research Database (eICU-CRD) for sample extraction, built a respiratory failure risk prediction model for patients with severe pneumonia based on four ensemble learning algorithms, and developed compact models corresponding to the four complete models to improve clinical practicality. The average area under receiver operating curve (AUROC) of the models on the test sets after ten random divisions of the dataset and the average accuracy at the best threshold were used as the evaluation metrics of the model performance. Finally, feature importance and Shapley additive explanation values were introduced to improve the interpretability of the model. A total of 1676 patients with pneumonia were analyzed in this study, of whom 297 developed respiratory failure one hour after admission to the intensive care unit (ICU). Both complete and compact CatBoost models had the highest average AUROC (0.858 and 0.857, respectively). The average accuracies at the best threshold were 75.19% and 77.33%, respectively. According to the feature importance bars and summary plot of the predictor variables, activetx (indicates whether the patient received active treatment), standard deviation of prothrombin time-international normalized ratio, Glasgow Coma Scale verbal score, age, and minimum oxygen saturation and respiratory rate were important. Compared with other ensemble learning models, the complete and compact CatBoost models have significantly higher average area under the curve values on the 10 randomly divided test sets. Additionally, the standard deviation (SD) of the compact CatBoost model is relatively small (SD:0.050), indicating that the performance of the compact CatBoost model is stable among these four ensemble learning models. The machine learning predictive models built in this study will help in early prediction and intervention of respiratory failure risk in patients with pneumonia in the ICU.
Assessing the validity of fecal sampling for characterizing variation in threespine stickleback’s gut microbiota

The gut microbiota is crucial for many aspects of their hosts’ biology, and it has been characterized for many species across the animal kingdom. Yet, we still don’t have a good understanding of whether non-lethal sampling can accurately capture the diversity of gut-associated bacterial communities, as estimated from lethal sampling of intestinal tissue. We further lack knowledge on whether non-lethal sampling methods are suitable for detecting gut microbiota shifts associated with changes in environmental factors (e.g., diet). We addressed these questions in threespine stickleback fish, a model system for evolutionary ecology, by comparing bacterial communities from intestinal tissue and feces. Despite some differences in community composition between the two sample types and considerable temporal variation among fecal samples, bacterial communities appear to largely overlap. Further, we detected consistent and significant changes of fecal bacterial communities associated with an experimental diet manipulation. This suggests that fecal sampling can represent an adequate non-lethal method to characterize the gut microbiota of threespine stickleback, but additional studies will be necessary before drawing general conclusions regarding the validity of fecal sampling for gut microbiota studies. To this end, we give recommendations to improve the characterization of the gut microbiota via fecal sampling. Fecal sampling allows studying temporal gut microbiota shifts associated with environmental change at the individual level, which increases opportunities for future experimental gut microbiota research.
A high-content screen reveals new regulators of nuclear membrane stability

Nuclear membrane rupture is a physiological response to multiple in vivo processes, such as cell migration, that can cause extensive genome instability and upregulate invasive and inflammatory pathways. However, the underlying molecular mechanisms of rupture are unclear and few regulators have been identified. In this study, we developed a reporter that is size excluded from re-compartmentalization following nuclear rupture events. This allows for robust detection of factors influencing nuclear integrity in fixed cells. We combined this with an automated image analysis pipeline in a high-content siRNA screen to identify new proteins that both increase and decrease nuclear rupture frequency in cancer cells. Pathway analysis identified an enrichment of nuclear membrane and ER factors in our hits and we demonstrate that one of these, the protein phosphatase CTDNEP1, is required for nuclear stability. Further analysis of known rupture contributors, including a newly developed automated quantitative analysis of nuclear lamina gaps, strongly suggests that CTDNEP1 acts in a new pathway. Our findings provide new insights into the molecular mechanism of nuclear rupture and define a highly adaptable program for rupture analysis that removes a substantial barrier to new discoveries in the field.
Characterization and prediction of individual functional outcome trajectories in schizophrenia spectrum disorders (PREDICTS study): Study protocol

Schizophrenia spectrum disorders (SSDs) are associated with significant functional impairments, disability, and low rates of personal recovery, along with tremendous economic costs linked primarily to lost productivity and premature mortality. Efforts to delineate the contributors to disability in SSDs have highlighted prominent roles for a diverse range of symptoms, physical health conditions, substance use disorders, neurobiological changes, and social factors. These findings have provided valuable advances in knowledge and helped define broad patterns of illness and outcomes across SSDs. Unsurprisingly, there have also been conflicting findings for many of these determinants that reflect the heterogeneous population of individuals with SSDs and the challenges of conceptualizing and treating SSDs as a unitary categorical construct. Presently it is not possible to identify the functional course on an individual level that would enable a personalized approach to treatment to alter the individual’s functional trajectory and mitigate the ensuing disability they would otherwise experience. To address this ongoing challenge, this study aims to conduct a longitudinal multimodal investigation of a large cohort of individuals with SSDs in order to establish discrete trajectories of personal recovery, disability, and community functioning, as well as the antecedents and predictors of these trajectories. This investigation will also provide the foundation for the co-design and testing of personalized interventions that alter these functional trajectories and improve outcomes for people with SSDs.
Description of a common stauromedusa on the Pacific Coast of the United States and Canada, Haliclystus sanjuanensis new species (Cnidaria: Staurozoa)

Haliclystus “sanjuanensis” nomen nudum is the most common staurozoan on the west coast of the United States and Canada. This species was described in the M.S. Thesis by Gellermann (1926) and although that name has been in use nearly continuously since that time, no published description exists. Furthermore, the most popular operative name for this species has varied between several related species names over time, resulting in confusion. Herein, we provide a detailed description and synonymy of Haliclystus sanjuanensis n. sp. , whose distribution is verified from Unalaska Island in the Aleutians (53.4° N, 166.8° W) in the northwest, to Santa Barbara County, California, just north of Point Conception (34.5° N, 120.5° W), in the south. Haliclystus sanjuanensis n. sp. is compared with the twelve other described species of Haliclystus and illustrations of both macroscopic and microscopic anatomy are provided. Haliclystus sanjuanensis n. sp. is unique among species of Haliclystus in the arrangement of the bright-white nematocyst spots in its calyx and the pattern of dark stripes running the length of the stalk and up the outside of the calyx.
Slow and Steady: A Slowly Progressing Neuroendocrine Tumor

ABSTRACT Neuroendocrine tumors (NETs) are rare malignant tumors that arise from neuroendocrine cells throughout the body, most commonly in the gastrointestinal and respiratory tracts. We report a case of well-differentiated grade 2 NET with a computed tomography scan showing multiple liver lesions consistent with the liver lesions seen 11 years before diagnosis. This case highlights the possibility of an indolent or prolonged clinical course of metastatic NET with an unknown primary vs primary hepatic NET.
Relationship between gait parameters and cognitive indexes in adult aging

Purpose This study aimed to identify the most effective summary cognitive index predicted from spatio-temporal gait features (STGF) extracted from gait patterns. Methods The study involved 125 participants, including 40 young (mean age: 27.65 years, 50% women), and 85 older adults (mean age: 73.25 years, 62.35% women). The group of older adults included both healthy adults and those with Mild Cognitive Impairment (MCI). Participant´s performance in various cognitive domains was evaluated using 12 cognitive measures from five neuropsychological tests. Four summary cognitive indexes were calculated for each case: 1) the z-score of Mini-Mental State Examination (MMSE) from a population norm (MMSE z-score); 2) the sum of the absolute z-scores of the patients’ neuropsychological measures from a population norm (ZSum); 3) the first principal component scores obtained from the individual cognitive variables z-scores (PCCog); and 4) the Mahalanobis distance between the vector that represents the subject’s cognitive state (defined by the 12 cognitive variables) and the vector corresponding to a population norm (MDCog). The gait patterns were recorded using a body-fixed Inertial Measurement Unit while participants executed four walking tasks (normal, fast, easy- and hard-dual tasks). Sixteen STGF for each walking task, and the dual-task costs for the dual tasks (when a subject performs an attention-demanding task and walks at the same time) were computed. After applied Principal Component Analysis to gait measures (96 features), a robust regression was used to predict each cognitive index and individual cognitive variable. The adjusted proportion of variance (adjusted-R2) coefficients were reported, and confidence intervals were estimated using the bootstrap procedure. Results The mean values of adjusted-R2 for the summary cognitive indexes were as follows: 0.0248 for MMSE z-score, 0.0080 for ZSum, 0.0033 for PCCog, and 0.4445 for MDCog. The mean adjusted-R2 values for the z-scores of individual cognitive variables ranged between 0.0009 and 0.0693. Multiple linear regression was only statistically significant for MDCog, with the highest estimated adjusted-R2 value. Conclusions The association between individual cognitive variables and most of the summary cognitive indexes with gait parameters was weak. However, the MDCog index showed a stronger and significant association with the STGF, exhibiting the highest value of the proportion of the variance that can be explained by the predictor variables. These findings suggest that the MDCog index may be a useful tool in studying the relationship between gait patterns and cognition.
Analysing urban and peri-urban youth employment in agribusiness in Malawi

The study aims to analyse key determinants of urban and peri-urban youth employment in agribusiness in Malawi to support youth policies. A mixed-methods approach is used, which combines both quantitative and qualitative analyses. The quantitative method involved a Bivariate Logit Model and Multinomial Logit Model to analyse nationally representative survey data from the Fourth Integrated Household Survey in 2016–2017. The qualitative method employed thematic analysis to data generated through Focus Group Discussions and Key Informant Interviews for key stakeholders involved in agri-business in Lilongwe district. The qualitative analysis, which focuses on a case study for urban and peri-urban youth in agribusiness, was used to validate, and provide context for the quantitative analysis. The results revealed that a majority of the urban and peri-urban youth engaged in agribusiness across Malawi work in sole farming (family farms or ganyu); in addition, women outnumber men in terms of engagement in agribusiness, and this stems from tradition. In addition, the determinants that affect youth’s engagement in agribusiness consist of demographic factors, institutional support, assets, and shocks. It was also shown that men were more likely than women to be engaged in sole farming, but they were as likely as women to be engaged in other forms of agribusiness. The policy recommendation from this study is that programs aimed at supporting youth engagement in agribusiness should consider a variety of factors; If resources are limited, the programs should ensure that they offer capacity strengthening for the youth in the form of extension services and practical training in agribusiness.
The assessment of the match performance of association football referees: Identification of key variables

The purpose of this study was to understand the contribution of each performance indicator to evaluate match performance of football referees. Thirty-four elite Referee Match Observers (RMOs) from the Portuguese FA participated voluntarily in the study. From the official assessment sheet of each game, the referee’s game score was categorized in two groups according to referee’s game score: i) Referees with a Low Score (LFS) and ii) Referees with High Score (HFS). A multivariable binary logistic regression model was used to assess the relationship between the Performance Indicators (PIs) of the dimensions i) game difficulty and game intervention and ii) disciplinary actions and game management in relation to the referee’s match assessment score. The model proposed revealed that only the PIs negative application of the laws of the game and referee teamwork, from the dimension disciplinary actions and game management, significantly defined the final game referee’s assessment score. This study suggests that the PIs scored by the RMOs contributed in a different way to the referee’s game score. The model explains 60.4% of the referee’s game score based on the variables referee teamwork and negative application of the laws of the game.