Miracle-dz/UniFAR-FTUs huggingface dataset:
Sodium Butyrate Attenuated Diabetes-Induced Intestinal Inflammation by Modulating Gut Microbiota

Background Diabetes mellitus (DM) continues to be one of the world's most costly and complex metabolic disorders. Accumulating evidence has shown that intestinal dysbiosis and associated inflammation can facilitate the onset and progression of DM. In this work, our goal was to investigate how sodium butyrate (SB) controls the gut microbiota to reduce the intestinal inflammation brought on by diabetes. Methods Male KK-Ay mice were randomized into two groups: the DM model group (intragastric administration of 0.9% normal saline) and the SB treatment group (intragastric administration of 1,000 mg/kg/d SB). The C57BL/6J mice were used as the control group (intragastric administration of 0.9% normal saline). These mice were administered via gavage for 8 weeks. Results The results revealed that SB-treated mice significantly reduced fasting blood glucose (FBG), body weight, 24 h food and water intake, and improved islet histopathology in DM model mice. SB reduced TNF-α, IL-1β, and iNOS, whereas it enhanced the expression of the anti-inflammatory Arg-1 marker on intestinal macrophages and the secretion of anti-inflammatory IL-10. Specifically, SB was linked to a marked drop in the expression of the Th17 marker RORγt and a substantial increase in the expression of the Treg marker Foxp3. SB treatment was associated with significant reductions in the levels of Th17-derived cytokines such as IL-17 and IL-6, whereas anti-inflammatory Treg-derived cytokines such as TGF-β were increased. Additionally, the analysis results from 16S rDNA sequencing suggested that SB significantly reversed the variations in intestinal flora distribution and decreased the relative abundance of Weissella confusa and Anaerotruncus colihominis DSM 17241 at the species level as well as Leuconostocaceae, Streptococcaceae, and Christensenellaceae at the family, genus, and species levels. These distinct florae may serve as a diagnostic biomarker for DM-induced intestinal inflammation. In addition, the heat map of phylum and OTU level revealed a close relationship between DM-induced intestinal inflammation and intestinal microbiota. Conclusions The present study suggested that SB may reduce DM-induced intestinal inflammation by regulating the gut microbiota.
Smart Technologies for Water Resource Management: An Overview

The latest progress in information and communication technology (ICT) and the Internet of Things (IoT) have opened up new opportunities for real-time monitoring and controlling of cities’ structures, infrastructures, and services. In this context, smart water management technology provides the data and tools to help users more effectively manage water usage. Data collected with smart water devices are being integrated with building management systems to show how much water is used by occupants as well as to identify the consumption areas to use water more efficiently. By this approach, smart buildings represent an innovative solution that enhances a city’s sustainability and contributes to overcoming environmental challenges due to increasing population and climate change. One of the main challenges is resource-saving and recovery. Water is an all-important need of all living beings, and the concerns of its scarcity impose a transition to innovative and sustainable management starting from the building scale. Thus, this manuscript aims to provide an updated and valuable overview for researchers, consumers, and stakeholders regarding implementing smart and sustainable technologies for water resource management, primarily for building-scale uses.
Potential Targets for Noninvasive Brain Stimulation on Depersonalization-Derealization Disorder

Introduction: Non-invasive brain stimulation seems to be beneficial for DPD patients. However, the sites used in previous studies were empirical. Exploring new stimulation locations via functional magnetic resonance imaging may improve the efficacy. Objectives: The objective was to find potential locations for non-invasive brain stimulation on the depersonalization-derealization disorder. Methods: We explored the potential brain surface regions from three pipelines: pipeline 1: activation likelihood estimation meta-analysis (five studies with 36 foci included); pipeline 2: functional connectivity analysis based on DPD-network (76 subjects included); and pipeline 3: functional connectivity analysis based on DPD regions of interest from the meta-analysis. Potential targets were the 10–20 system coordinates for brain surface regions. Results: We identified several potential brain surface regions, including the bilateral medial prefrontal cortex, dorsal lateral prefrontal cortex, superior parietal gyrus, superior temporal gyrus, and right ventrolateral prefrontal cortex as potential sites. Conclusion: Our findings of the potential stimulation targets might help clinicians optimize the application of non-invasive brain stimulation therapy in individuals with DPD.
Deep Expectation-Maximization for Single-Pixel Image Reconstruction With Signal-Dependent Noise

Image reconstruction from a sequence of a few linear measurements that are corrupted by signal-dependent normally distributed noise is an inverse problem with many biomedical imaging applications, such as computerized tomography and optical microscopy. In this study, we focus on single-pixel imaging, where the set-up acquires a down-sampled Hadamard transform of an image of the scene. Deep learning is a computationally efficient framework to solve inverse problems in imaging. Several neural-network architectures provide a link between deep and optimization-based image reconstruction methods. These deep-learning methods rely on a forward operator and lead to more interpretable networks. Here, we propose a novel network architecture obtained by unrolling an heuristic expectation-maximization algorithm. In particular, we compute the maximum a posteriori estimate of the unknown image given measurements corrupted by normally distributed signal-dependent noise. We show that the so-called expectation-maximization reconstruction network (EM-Net) applies to mixed Skellam-Gaussian noise models that are common in single-pixel imaging. We present reconstruction results from simulated and experimental single-pixel acquisitions. We show that EM-Net generalizes very well to noise levels not seen during training, despite having fewer learned parameters than alternative methods. The proposed EM-Net generally reconstructs images with fewer artifacts and higher signal-to-noise ratios, in particular in high-noise situations compared to other state of the art reconstruction algorithms that do not estimate the noise covariance.
Construction of A GBS-Based High-Density Genetic Map and Flower Color-Related Loci Mapping in Grasspea (Lathyrus sativus L.)

Grasspea (Lathyrus sativus L.), a legume crop with excellent resistance to a broad array of environmental stressors, has, to this point, been poorly genetically characterized. High-density genetic linkage maps are critical for draft genome assembly, quantitative trait loci (QTLs) analysis, and gene mining. The lack of a high-density genetic linkage map has limited both genomic studies and selective breeding in grasspea. Here, we developed a high-density genetic linkage map of grasspea using genotyping-by-sequencing (GBS) to sequence 154 grasspea plants, comprising 2 parents and 152 F2 progeny. In all, 307.74 Gb of data was produced, including 2,108,910,938 paired-end reads, as well as 3536 SNPs mapped to seven linkage groups (LG1–LG7). With an average length of 996.52 cM per LG, the overall genetic distance was 6975.68 cM. Both the χ2 test and QTL analysis, based on the Kruskal–Wallis (KW) test and interval mapping (IM) analysis, revealed the monogenic inheritance of flower color in grasspea, with the responsible QTL located between 308.437 cM and 311.346 cM in LG4. The results can aid grasspea genome assembly and accelerate the selective breeding of new grasspea germplasm resources.
Antepartum Antibiotic Therapy under 34 Weeks of Gestation and Its Impact on Early-Onset Neonatal Infection and Maternal Vaginal Microbiota

The use of prenatal antibiotics should be carefully considered, owing to their potential adverse effects on neonatal outcomes. This study aimed to identify the contributing factors to early-onset neonatal infection and to determine the influence of antepartum antibiotics on women and neonates. This study included 127 pregnant women without obvious intra-amniotic infection on admission, who delivered under 34 weeks of gestation. Information on maternal and neonatal characteristics was obtained from their medical charts. Vaginal swabs were taken from all women on admission. In total, 29 (22.8%) neonates developed early-onset infection. Multivariate analysis revealed that antepartum antibiotics were the most strongly associated factor for early-onset neonatal infection (odds ratio, 11.2; 95% confidence interval, 4.08–31.02). The frequency of early-onset neonatal infection was significantly higher in women who received antibiotic therapy than in those who did not; no significant difference in prolonging their gestation or neonatal morbidities was observed. The prevalence of women who hosted vaginal microorganisms on admission was similar to that in women whose infants subsequently developed early-onset neonatal infection compared with that of women whose infants did not. Among infants of the 40 women who received antepartum antibiotic therapy, 21 developed early-onset infection. Of the women who delivered these 21 infants, 62% (13/21) showed reduced lactobacilli and 43% (9/21) had resistant bacterial strains in their vaginal microbiota at the time of delivery. The use of antepartum antibiotics is the most strongly associated factor in early-onset neonatal infection; it does not prolong gestation and would change the vaginal environment.
Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli

While naturalistic stimuli, such as movies, better represent the complexity of the real world and are perhaps crucial to understanding the dynamics of emotion processing, there is limited research on emotions with naturalistic stimuli. There is a need to understand the temporal dynamics of emotion processing and their relationship to different dimensions of emotion experience. In addition, there is a need to understand the dynamics of functional connectivity underlying different emotional experiences that occur during or prior to such experiences. To address these questions, we recorded the EEG of participants and asked them to mark the temporal location of their emotional experience as they watched a video. We also obtained self-assessment ratings for emotional multimedia stimuli. We calculated dynamic functional the connectivity (DFC) patterns in all the frequency bands, including information about hubs in the network. The change in functional networks was quantified in terms of temporal variability, which was then used in regression analysis to evaluate whether temporal variability in DFC (tvDFC) could predict different dimensions of emotional experience. We observed that the connectivity patterns in the upper beta band could differentiate emotion categories better during or prior to the reported emotional experience. The temporal variability in functional connectivity dynamics is primarily related to emotional arousal followed by dominance. The hubs in the functional networks were found across the right frontal and bilateral parietal lobes, which have been reported to facilitate affect, interoception, action, and memory-related processing. Since our study was performed with naturalistic real-life resembling emotional videos, the study contributes significantly to understanding the dynamics of emotion processing. The results support constructivist theories of emotional experience and show that changes in dynamic functional connectivity can predict aspects of our emotional experience.
The Study of Nutrient Intake and Adolescent Girls’ Quality of Life in a Rural Area of Indonesia

An inadequate nutrient intake correlates with malnutrition, a problem affecting many adolescents worldwide. Nutrient intake is associated with quality of life (QoL). Our study analyzed the relationship between nutrient intake and adolescents’ QoL. We conducted a cross-sectional study. Through simple random sampling, 157 adolescent girls were selected. Nutrition status was assessed using anthropometric measurements. Nutrient intake was collected using the food frequency questionnaire (FFQ). WHOQOL BREF was used to explore adolescent girls’ quality of life. The median of nutrient intake: energy (908.25 kcal); protein (24.16 g); carbohydrate (128.89 g); fat (21.89 g); vitamin A (77.10 mg); vitamin E (1.40 mg); vitamin B1 (0.19 mg); vitamin B2 (0.29 mg); vitamin B6 (0.45 mg); folic acid (35.13 mg); vitamin C (12.60 mg); calcium (197.46 mg); magnesium (93.72 mg); iron (2.64 mg); and zinc (2.09 mg). The adolescents’ QoL scores were physical health 44 (25–81), psychological domain 56 (19–94), social relationships 56 (19–94), and environmental domain 56 (31–100). The strongest correlations were between (1) physical health with carbohydrates, vitamin C, and fat; (2) psychological domain with calcium; (3) social relationships with carbohydrates and vitamin C; and (4) environmental domain with BMI and zinc. There was a significant positive correlation between the intake of some nutrients and adolescents’ QoL, despite the observation of some significant negative correlations. The findings of this study indicate that more attention should be focused on adolescents’ nutrient intake in order to improve their QoL.
Sex differences in metastatic surgery following diagnosis of synchronous metastatic colorectal cancer

The aim was to investigate gender differences in the likelihood to receive metastatic surgery, and to compare overall survival between men and women, among patients with synchronous metastatic colorectal cancer (mCRC) in a population‐based setting. All Swedish adult patients diagnosed with synchronous mCRC in 2007‐2016 were identified using the nationwide colorectal cancer database (CRCBaSe). Unadjusted and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were estimated using logistic regression, comparing the odds of receiving treatment. The Kaplan‐Meier method was used to calculate survival proportions and Cox regression models to estimate hazard ratios (HRs) and 95% CIs of all‐cause mortality rates. All multivariable models were adjusted for age, ASA score, Charlson comorbidity index, year of diagnosis, location of primary tumor and single or multiple metastatic locations. A total of 12 201 patients met the study criteria. Women received 23% less metastatic surgery for mCRC (adjusted OR = 0.77, CI:0.69‐0.86) and experienced a slightly higher mortality following diagnosis (adjusted HR = 1.09, CI:1.05‐1.14). In analyses restricted to patients who received metastatic surgery, no significant differences in mortality were found. In conclusion, this population‐based study showed that women less often received metastatic surgery of mCRC and experienced slightly higher all‐cause mortality compared with men. The differences persisted despite adjustments of patient and cancer characteristics. Gender differences in receiving treatment are unacceptable if the underlying explanation cannot be motivated. Further studies are needed to understand if the differences are based on sex (i.e., biology) or gender (including clinically unmotivated differences in treatment approach).
An Exploratory Critical Review on TNF-α as a Potential Inflammatory Biomarker Responsive to Dietary Intervention with Bioactive Foods and Derived Products

This review collects and critically examines data on the levels of tumour necrosis factor-alpha (TNF-α) in lean, overweight and obese subjects, and the effects of intervention with different foods and food products containing bioactive constituents in overweight/obese individuals. We additionally explore the influence of different single nucleotide polymorphisms (SNPs) on TNF-α levels and compare the response to food products with that to some anti-obesity drugs. Our aim was to provide an overview of the variability, consistency, and magnitude of the reported effects of dietary factors on TNF-α, and to envisage the reliability of measuring changes in the levels of this cytokine as a biomarker responsive to food intervention in association with the reduction in body weight. Regarding the circulating levels of TNF-α, we report: (i) a large intra-group variability, with most coefficients of variation (CV%) values being ≥30% and, in many cases, >100%; (ii) a large between-studies variability, with baseline TNF-α values ranging from <1.0 up to several hundred pg/mL; (iii) highly variable effects of the different dietary approaches with both statistically significant and not significant decreases or increases of the protein, and the absolute effect size varying from <0.1 pg/mL up to ≈50 pg/mL. Within this scenario of variability, it was not possible to discern clear differentiating limits in TNF-α between lean, overweight, and obese individuals or a distinct downregulatory effect on this cytokine by any of the different dietary approaches reviewed, i.e., polyunsaturated fatty acids (PUFAs), Vitamin-D (VitD), mixed (micro)nutrients, (poly)phenols or other phytochemicals. Further, there was not a clear relationship between the TNF-α responses and body weight changes. We found similarities between dietary and pharmacological treatments in terms of variability and limited evidence of the TNF-α response. Different factors that contribute to this variability are discussed and some specific recommendations are proposed to reinforce the need to improve future studies looking at this cytokine as a potential biomarker of response to dietary approaches.
Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments

The leaf area index (LAI), commonly used as an indicator of crop growth and physiological development, is mainly influenced by the degree of water and fertilizer stress. Accurate assessment of the LAI can help to understand the state of crop water and fertilizer deficit, which is important for crop management and the precision agriculture. The objective of this study is to evaluate the unmanned aerial vehicle (UAV)-based multispectral imaging to estimate the LAI of maize under different water and fertilizer stress conditions. For this, multispectral imagery of the field was conducted at different growth stages (jointing, trumpet, silking and flowering) of maize under three water treatments and five fertilizer treatments. Subsequently, a stacking ensemble learning model was built with Gaussian process regression (GPR), support vector regression (SVR), random forest (RF), least absolute shrinkage and selection operator (Lasso) and cubist regression as primary learners to predict the LAI using UAV-based vegetation indices (VIs) and ground truth data. Results showed that the LAI was influenced significantly by water and fertilizer stress in both years’ experiments. Multispectral VIs were significantly correlated with maize LAI at multiple growth stages. The Pearson correlation coefficients between UAV-based VIs and ground truth LAI ranged from 0.64 to 0.89. Furthermore, the fusion of multiple stage data showed that the correlations were significantly higher between ground truth LAI and UAV-based VIs than that of single growth stage data. The ensemble learning algorithm with MLR as the secondary learner outperformed as a single machine learning algorithm with high prediction accuracy R2 = 0.967 and RMSE = 0.198 in 2020, and R2 = 0.897 and RMSE = 0.220 in 2021. We believe that the ensemble learning algorithm based on stacking is preferable to the single machine learning algorithm to build the LAI prediction model. This study can provide certain theoretical guidance for the rapid and precise management of water and fertilizer for large experimental fields.
Identifying Key Factors Associated with Green Justice in Accessibility: A Gradient Boosting Decision Tree Analysis

Park green space (PGS) provides numerous environmental and health benefits for urban residents, and raises the issue of green justice for its uneven distribution in cities. Previous studies focus more on the measurements of spatial equity in accessibility, but are limited in exploring its impacts—especially the nonlinear influence. This study first measures accessibility and equity in two traffic modes, and then explores the nonlinear influence of multidimensional factors by using the gradient boosting decision tree (GBDT) model across the central urban area of Wuhan. The results show significant spatial disparities in spatial accessibility and equity by walking and driving within 15 min. Multidimensional factors—including characteristics of PGS, the built environment, and socioeconomic factors—present stronger nonlinear influences on spatial accessibility and equity, and the nonlinear influence indicates that the contributions of the built environment and socioeconomic factors are greater than those of park characteristics, accounting for at least 79.76%. The key variables affecting the accessibility and equity are not completely consistent, leading to synergistic and heterogeneous effects, which may provide policy implications for streets where accessibility and equity are mismatched. These findings could provide guidance for PGS planning by decision-makers to improve the living environment and urban health.
Efficacy and Safety of a Long-Term Multidisciplinary Weight Loss Intervention under Hospitalization in Aging Patients with Obesity: An Open Label Study

The effects of the hypocaloric diet under hospitalization on blood biochemical parameters (lipid, glycaemic, thyroid and liver profiles) were not reported in literature. This study aims to evaluate the efficacy and safety of a hypocaloric diet under hospitalisation in obese patients. A total of 151 obese subjects (49 males and 102 females, aged 69.38 ± 14.1 years, BMI 41.78 ± 7.1) were enrolled in this study. Participants were treated with an hypocaloric diet for a maximum period of 3 months. Outcomes were assessed at the beginning and at the end of the recovery period. The average duration of the hospitalisation was 47.5 days ± 1.3. The effect of the diet on all the outcomes was evaluated using the Analysis of Covariance (ANCOVA) and the predictors of weight loss were identified using linear regression. The diet induced a reduction in the anthropometric (BMI decrease of −2.713 points) and DXA body measurements in addition to serum lipids, glucose, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and C-reactive protein (CRP) levels without affecting the muscle mass, liver and thyroid profiles. During the intervention, there was a positive shift in body composition favouring fat free mass (FFM). Lower insulin but higher serum calcium and potassium levels were predictors of weight loss.