Miracle-dz/UniFAR-FTUs huggingface dataset:
Glomerular filtration rate correlation and agreement between common predictive equations and standard 24-hour urinary creatinine clearance in medical critically ill patients

Background Determining kidney function in critically ill patients is paramount for the dose adjustment of several medications. When assessing kidney function, the glomerular filtration rate (GFR) is generally estimated either by calculating urine creatinine clearance (UCrCl) or using a predictive equation. Unfortunately, all predictive equations have been derived for medical outpatients. Therefore, the validity of predictive equations is of concern when compared with that of the UCrCl method, particularly in medical critically ill patients. Therefore, we conducted this study to assess the agreement of the estimated GFR (eGFR) using common predictive equations and UCrCl in medical critical care setting. Methods This was the secondary analysis of a nutrition therapy study. Urine was collected from participating patients over 24 h for urine creatinine, urine nitrogen, urine volume, and serum creatinine measurements on days 1, 3, 5, and 14 of the study. Subsequently, we calculated UCrCl and eGFR using four predictive equations, the Cockcroft–Gault (CG) formula, the four and six-variable Modification of Diet in Renal Disease Study (MDRD-4 and MDRD-6) equations, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. The correlation and agreement between eGFR and UCrCl were determined using the Spearman rank correlation coefficient and Bland–Altman plot with multiple measurements per subject, respectively. The performance of each predictive equation for estimating GFR was reported as bias, precision, and absolute percentage error (APE). Results A total of 49 patients with 170 urine samples were included in the final analysis. Of 49 patients, the median age was 74 (21–92) years-old and 49% was male. All patients were hemodynamically stable with mean arterial blood pressure of 82 (65–108) mmHg. Baseline serum creatinine was 0.93 (0.3–4.84) mg/dL and baseline UCrCl was 46.69 (3.40–165.53) mL/min. The eGFR from all the predictive equations showed modest correlation with UCrCl (r: 0.692 to 0.759). However, the performance of all the predictive equations in estimating GFR compared to that of UCrCl was poor, demonstrating bias ranged from −8.36 to −31.95 mL/min, precision ranged from 92.02 to 166.43 mL/min, and an unacceptable APE (23.01% to 47.18%). Nevertheless, the CG formula showed the best performance in estimating GFR, with a small bias (−2.30 (−9.46 to 4.86) mL/min) and an acceptable APE (14.72% (10.87% to 23.80%)), especially in patients with normal UCrCl. Conclusion From our finding, CG formula was the best eGFR formula in the medical critically ill patients, which demonstrated the least bias and acceptable APE, especially in normal UCrCl patients. However, the predictive equation commonly used to estimate GFR in critically ill patients must be cautiously applied due to its large bias, wide precision, and unacceptable error, particularly in renal function impairment.
Does Self-Care Make You a Better Leader? A Multisource Study Linking Leader Self-Care to Health-Oriented Leadership, Employee Self-Care, and Health

Leadership plays an important role in employee well-being. In light of a growing research interest in leaders’ resources as determinants of healthy leadership, it is not yet clear how leaders’ behavior regarding their own health (self-care) may trickle down to employees. Drawing on Conservation of Resources Theory and the model of Health-Oriented Leadership, this study tests two mechanisms through which employees may benefit from self-caring leaders: (a) through staff care, that is, concern for their employees’ health (improved leadership hypothesis); and (b) through a direct relationship between leaders’ and employees’ self-care (role-modeling hypothesis). In turn, both staff care and employee self-care would relate positively to employee health. Multilevel path models based on a sample of N = 46 supervisors and 437 employees revealed that leader self-care was positively related to leader-rated staff care at Level 2, which was positively related to employee-rated staff care at Level 1. In turn, employee-rated staff care was positively related to employee health. The findings support the improved leadership hypothesis and underline the importance of leader self-care as a determinant of healthy leadership.
The status and distribution of three species of Sternula terns on the eastern coast of Africa and in the western Indian Ocean, with two species new for Mozambique

Summary. The status of three Sternula terns in southern and eastern Africa and the Indian Ocean is updated based on observations in Mozambique during October 2009–August 2021. Damara Tern S. balaenarum and Saunders's Tern S. saundersi, both new to Mozambique, were found at the San Sebastian Peninsula in August 2018 and have been recorded regularly since. Damara Tern arrived in May, numbers were lower June–July, with a peak of 100+ in August–October. Breeding plumage was assumed by late October and all departed in early November; those aged were all adults. Observations of ringed birds suggested they originated from colonies in South Africa. Numbers of Damara Tern reached the Key Biodiversity Area designation threshold population for the site in 2020. These are the first records of large numbers of Damara Tern in the Indian Ocean, whilst timings suggest the species is partly transient at the site and may also occur further north. Saunders's Tern was recorded at San Sebastian in all months except March and April, with 5–80 regularly present, and peak numbers perhaps in October–December. Birds were observed in a range of plumages in all months, from non-breeding, transitional to full breeding. Courtship feeding was observed in September–November suggesting at least some attempt to breed locally, although disturbance is high. Birds in non-breeding plumage may stem from Northern Hemisphere breeding localities. Little Tern S. albifrons was a numerous migrant in southern Mozambique in October–May, peaking in April when large numbers were observed during pelagic trips to the continental shelf edge, and at onshore roost sites including San Sebastian. Smaller numbers fed in coastal lagoons and freshwater lakes. Three Little Terns had been colour-ringed in Israel.
A system for continuous sampling of bioaerosols generated by a postal sorting machine

A System for Continuous Sampling of Bioaerosols Generated by a Postal Sorting Machine. (August 2003) Mathews Sears Richardson, B.S., University of Texas, Austin Chair of Advisory Committee: Dr. Andrew McFarland In this study, a system for the collection of bioaerosols emitted from the mail sorting process was designed and characterized. Two different wetted-wall cyclones, the JBPDS cyclone and the glass cyclone sampler (GCS), were evaluated as system collection devices. These devices operate at 780 L/min and have a D50 of ∼1 μm. A trimming impactor with a D50 of 10 μm was used upstream of the collection devices. Using two reference probes, the cyclone liquid outputs were compared with aerosol collected on filters and the output of an Aerosol-to-Hydrosol Transfer Stage (AHTS). The mass emission rate of the postal sorting machine was 3.15 mg/min and found not to vary significantly with flow rates above 700 L/min. On average, greater than 66% of the mass collected had a Da < 10 μm. Using a Coulter Counter, the volume median diameter (volume equivalent) for both device hydrosol outputs was 4.18 μm. For the effluent aerosol, the volume median diameter was 12.5 μm. For a bioaerosol release, this study found that greater than 65% (by volume) of the material released had a Da greater than 7.2 μm. Using filters, it was found that on average, 95% of the bioaerosol particles emitted had a Da less than 10 μm. According to the reference data, the expected number of bioaerosol particles in 1.5 times that collected by the GCS and 5.5 times that collected by the JBPDS cyclone for a one milligram release. The time constant for the system in response to a letter release was found to be 1.3 minutes for the GCS and 1.75 minutes for the JBPDS cyclone.
Occurrence of phytophagous and predacious mites in two fig cultivars with population dynamics of the most abundant species in relation to weather factors and plant phenology at Sohag governorate, Egypt

A survey and ecological studies were carried out on mite species inhabiting fig, Ficus carica L. (Moraceae), an important fruit crop in Egypt, at Sohag governorate during 2020–2021. The occurrence of species inhabiting leaves, debris and soil of “Sultani” and “Condria” fig cultivars were considered. Results revealed the occurrence of 51 mite species. Nine phytophagous mite species presented nine genera and five families (i.e., Diptilomiopidae, Eriophyidae, Tarsonemidae, Tenuipalpidae and Tetranychidae). Thirty-one species of predaceous mites (25 genera, 13 families) and 11 species (10 genera and 8 families) miscellaneous-feeding habits were recorded. Population dynamics of Tetranychus urticae Koch, Euseius scutalis (Athias-Henriot), Tydeus oregonensis Baker, Agistemus exsertus Gonzalez and Aceria ficus (Cotté) were found to be affected by both weather factors and plant phenology. Tetranychus urticae had two peaks in mid-Jul. and early Oct. on “Sultani” and “Condria” cultivars during the two successive years. The fig bud mite, A. ficus was recorded with a high numbers in Jun. on both cultivars. Predacious mites were found to be in relation to occurring phytophagous ones. The population dynamics of phytophagous mites with the relation of temperature, relative humidity and plant age (i.e., plant phenology to emulating plant nutritional value dynamic) were fitted to a polynomial model including weather factors and plant age as third degree of polynomial over two intervals presenting two phonological intervals of plant growth pattern. Results indicated that the change in the nutritional value of the host plant was more effective on mite population dynamics than weather factors.
Modelling of nitrates in River Nzoia using MIKE 11

Eutrophication is a serious problem in Lake Victoria as a result of enrichment by nutrients transported by the rivers draining into it. River Nzoia is one of the main rivers draining into the lake. The main aim of this study was to simulate the level of nitrates using MIKE 11 and to establish relationship between nitrogen and phosphorus. The model was calibrated using water quality data for 2009 and validated with March 2013 data and then it was used to simulate nitrate concentration for the wet month of April 2013. The model performance was good with R 2 values of between 0.87 and 0.98 and EF values of between 0.73 and 0.96. From the simulations, the effluent discharge from municipal and industrial wastewater ponds elevated the concentration of the nitrates in the river. Analysis of the concentrations of nitrates for wet and dry periods showed significant variations indicating significant contributions from the catchment through run-off. The relationship between total nitrogen and total phosphorus was analysed and found to have a strong positive correlation (r = 0.714, p
The Effect of Firm Size on Tax Aggressiveness with Moderating Role of Earnings Management

This study aims to find empirical evidence of the effect of firm size on tax aggressiveness and the moderating role of earnings management on this effect. The population in this study is the restaurant, hotel, tourism and transportation sub-sector companies listed on the Indonesia Stock Exchange (IDX) for the 2013-2019 period. The sampling technique used was purposive sampling, so that the sample data obtained was 80. Regression analysis and moderate regression analysis used to analyze the data. The results of this study indicate that firm size has no effect on tax aggressiveness. Earnings management moderate the effect of firm size on tax aggressiveness. Earnings management is a pure moderation on the effect of firm size on tax aggressiveness.
Tooth-Specific Streptococcus mutans Distribution and Associated Microbiome

Dental caries is multifactorial and polymicrobial in nature and remains one of the most common oral diseases. While caries research has focused on Streptococcus mutans as the main etiological pathogen, its impact at the tooth level is not fully understood. In this cross-sectional study, the levels and distribution of S. mutans in the posterior teeth at different dentition stages were investigated along with the corresponding tooth-specific microbiome. Occlusal plaque samples of 87 individual posterior teeth were collected from thirty children in three dentition stages (primary, mixed, and permanent). The S. mutans levels in the occlusal plaque of individual posterior teeth were quantified with qPCR, and those with preferential colonization were selected for tooth-specific microbiome analysis using 16S rRNA sequencing. Results: Quantification of S. mutans levels in the occlusal plaque confirmed the preferential colonization on the first primary and permanent molars. These teeth were selected for further tooth-specific microbiome sequencing, as they also displayed high caries experience. There were significant differences in the relative abundance of the four most abundant genera: Neisseria, Streptococcus, Rothia, and Veillonella. Furthermore, the tooth-level caries experience was correlated with a reduction in the microbiome diversity. Analyzing the different tooth-associated microbial communities, distinct tooth-specific core microbiomes were identified. Conclusions: Our findings suggest that caries susceptibility at the tooth level, depending on tooth type and dentition stage, is influenced by individual species as well as plaque community.
Gut Steroids and Microbiota: Effect of Gonadectomy and Sex

Sex steroids, derived mainly from gonads, can shape microbiota composition; however, the impact of gonadectomy and sex on steroid production in the gut (i.e., gut steroids), and its interaction with microbiota composition, needs to be clarified. In this study, steroid environment and gut steroidogenesis were analysed by liquid chromatography tandem mass spectrometry and expression analyses. Gut microbiota composition as branched- and short-chain fatty acids were determined by 16S rRNA gene sequence analysis and gas chromatography flame ionisation detection, respectively. Here, we first demonstrated that levels of pregnenolone (PREG), progesterone (PROG), and isoallopregnanolone (ISOALLO) were higher in the female rat colon, whereas the level of testosterone (T) was higher in males. Sexual dimorphism on gut steroidogenesis is also reported after gonadectomy. Sex, and more significantly, gonadectomy, affects microbiota composition. We noted that a number of taxa and inferred metabolic pathways were associated with gut steroids, such as positive associations between Blautia with T, dihydroprogesterone (DHP), and allopregnanolone (ALLO), whereas negative associations were noted between Roseburia and T, ALLO, PREG, ISOALLO, DHP, and PROG. In conclusion, this study highlights the novel sex-specific association between microbiota and gut steroids with possible relevance for the gut-brain axis.
“She Went Above and Beyond”: How Comprehensive School Counseling Programs Benefit First-Generation College Students through Personalized Support

Abstract This qualitative research study contains a focus on first-generation students’ perceptions of their school’s comprehensive school counseling program and their school counselor. Through a social capital theory lens, three themes emerged, (a) received school counselors’ emotional, motivational, and tailored support, (b) increased social capital through accessing school counselors’ knowledge & skills, and (c) benefited from varied modalities of structured support. Implications for school counselors and counselor educators to improve first- generation student outcomes are included.
Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.
On-Court Activity and Game-Related Statistics during Scoring Streaks in Basketball: Applied Use of Accelerometers

The aim of this observational study was to determine if on-court activity and match statistics differed between periods of scoring streaks and regular play in basketball. Thirty-seven basketballers including professional women, semi-professional women and semi-professional men wore accelerometers during competitive matches throughout a season. Accelerometry-derived live-time individual on-court exercise intensity and team game-related statistics were compared between scoring streaks (periods of play where the teams participating in the study scored at least three times in a row), streaks against (periods of play where the opposition teams scored at least three times in a row) and regular play. Few differences existed in the average exercise intensity between streak types. During streaks against, there was a 5–15% lower proportion of 2-point attempts, 0.8–1.3 fewer defensive rebounds per minute and 0.3–1.6 fewer shot attempts per minute compared to regular play and scoring streaks, and there were 0.3 fewer offensive rebounds per minute compared to regular play. During scoring streaks, there were 0.5 more defensive rebounds per minute, 1.3 more shot attempts per minute, a 43% greater shooting percentage and a 10% lower proportion of 3-point attempts compared to regular play. To reduce the chances of streaks against, teams should focus on facilitating 2-point shot attempts and consider implementing a 3:1 ratio of 2-point to 3-point attempts to maximize scoring success, and they should focus on winning rebounds to facilitate more shot attempts.
In-Vitro Endothelialization Assessment of Heparinized Bovine Pericardial Scaffold for Cardiovascular Application

(1) Background: Hemocompatibility is a critical challenge for tissue-derived biomaterial when directly contacting the bloodstream. In addition to surface modification with heparin, endothelialization of the grafted material is suggested to improve long-term clinical efficacy. This study aimed to evaluate the ability to endothelialize in vitro of heparinized bovine pericardial scaffolds. (2) Methods: bovine pericardial scaffolds were fabricated and heparinized using a layer-by-layer assembly technique. The heparinized scaffolds were characterized for heparin content, surface morphology, and blood compatibility. Liquid extraction of the samples was prepared for cytotoxicity testing on human endothelial cells. The in-vitro endothelialization was determined via human endothelial cell attachment and proliferation on the scaffold. (3) Results: The heparinized bovine pericardial scaffold exhibited a heparin coating within its microfiber network. The scaffold surface immobilized with heparin performed good anti-thrombosis and prevented platelet adherence. The proper cytotoxicity impact was observed for a freshly used heparinized sample. After 24 h washing in PBS 1X, the cell compatibility of the heparinized scaffolds was improved. In-vitro examination results exhibited human endothelial cell attachment and proliferation for 7 days of culture. (4) Conclusions: Our in-vitro analysis provided evidence for the scaffold’s ability to support endothelialization, which benefits long-term thromboresistance.
Pear Recognition in an Orchard from 3D Stereo Camera Datasets to Develop a Fruit Picking Mechanism Using Mask R-CNN

In orchard fruit picking systems for pears, the challenge is to identify the full shape of the soft fruit to avoid injuries while using robotic or automatic picking systems. Advancements in computer vision have brought the potential to train for different shapes and sizes of fruit using deep learning algorithms. In this research, a fruit recognition method for robotic systems was developed to identify pears in a complex orchard environment using a 3D stereo camera combined with Mask Region-Convolutional Neural Networks (Mask R-CNN) deep learning technology to obtain targets. This experiment used 9054 RGBA original images (3018 original images and 6036 augmented images) to create a dataset divided into a training, validation, and testing sets. Furthermore, we collected the dataset under different lighting conditions at different times which were high-light (9–10 am) and low-light (6–7 pm) conditions at JST, Tokyo Time, August 2021 (summertime) to prepare training, validation, and test datasets at a ratio of 6:3:1. All the images were taken by a 3D stereo camera which included PERFORMANCE, QUALITY, and ULTRA models. We used the PERFORMANCE model to capture images to make the datasets; the camera on the left generated depth images and the camera on the right generated the original images. In this research, we also compared the performance of different types with the R-CNN model (Mask R-CNN and Faster R-CNN); the mean Average Precisions (mAP) of Mask R-CNN and Faster R-CNN were compared in the same datasets with the same ratio. Each epoch in Mask R-CNN was set at 500 steps with total 80 epochs. And Faster R-CNN was set at 40,000 steps for training. For the recognition of pears, the Mask R-CNN, had the mAPs of 95.22% for validation set and 99.45% was observed for the testing set. On the other hand, mAPs were observed 87.9% in the validation set and 87.52% in the testing set using Faster R-CNN. The different models using the same dataset had differences in performance in gathering clustered pears and individual pear situations. Mask R-CNN outperformed Faster R-CNN when the pears are densely clustered at the complex orchard. Therefore, the 3D stereo camera-based dataset combined with the Mask R-CNN vision algorithm had high accuracy in detecting the individual pears from gathered pears in a complex orchard environment.