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Embedding professional development within the curriculum of graduate programs: An impact survey from biomedical departments in a faculty of medicine
Although the career pathways of biomedical PhDs are increasingly diverse, graduate education continues to rely upon an apprenticeship model, whereby supervisors train the next generation of researchers. To broaden the core competency skills for research productivity and career development across the Temerty Faculty of Medicine’s departments and institutes, a for-credit curricular Graduate Professional Development (GPD) course has been implemented as an integral part of the graduate curriculum in multiple departments. We used three key principles to advance this integrated professional development curriculum: i) a train-the-trainers method for implementation, ii) direct student engagement via a students-as-partners model, one-on-one consultations and videos, and iii) a faculty development program. While programming of this sort has direct benefits, less is known about how students themselves experience curricular GPD. To address the gap in understanding the impact, research was undertaken to inquire about students’ perceived skills development and attitudes towards career choices after taking the GPD course. According to our findings, students see the curricular GPD course as a valuable complement to their graduate education and feel more prepared for their next steps in their career exploration. With buy-in from students, faculty and academic leaders, embedded curricular GPD has transformed the academic culture of graduate education in our faculty, helping our students with skills that empower them to be prepared for their own career development to meet the competitive career realities of our time.
Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials
Catalytic Enantioselective Nucleophilic Desymmetrization at Phosphorus(V): A Three-Phase Strategy for Modular Preparation of Phosphoramidates
The turbulent soundscape of intertidal oyster reefs
Turbulence and sound are important cues for oyster reef larval recruitment. Numerous studies have found a relationship between turbulence intensity and swimming behaviors of marine larvae, while others have documented the importance of sounds in enhancing larval recruitment to oyster reefs. However, the relationship between turbulence and the reef soundscape is not well understood. In this study we made side-by-side acoustic Doppler velocimeter turbulence measurements and hydrophone soundscape recordings over 2 intertidal oyster reefs (1 natural and 1 restored) and 1 adjacent bare mudflat as a reference. Sound pressure levels (SPL) were similar across all three sites, although SPL > 2000 Hz was highest at the restored reef, likely due to its larger area that contained a greater number of sound-producing organisms. Flow noise (FN), defined as the mean of pressure fluctuations recorded by the hydrophone at f < 100 Hz, was significantly related to mean flow speed, turbulent kinetic energy, and turbulence dissipation rate (ε), agreeing with theoretical calculations for turbulence. Our results also show a similar relationship between ε and FN to what has been previously reported for ε vs. downward larval swimming velocity ( w b ), with both FN and w b demonstrating rapid growth at ε > 0.1 cm 2 s −3 . These results suggest that reef turbulence and sounds may attract oyster larvae in complementary and synergistic ways.
Identification of potential shared gene signatures between periodontitis and breast cancer by integrating bulk RNA-seq and scRNA-seq data
Imine-Based Transient Supramolecular Polymers
Detection of microplastics in the feline placenta and fetus
The present study aimed to detect microplastics in feline placentas and fetuses in the early stage of pregnancy. For this study, 8 pregnant queens were evaluated. A standardized protocol for the digestion of biological matter was used, as well as a plastic-free approach for sample collection and manipulation. Microplastics were investigated by means of Raman spectroscopy, with the aim of identifying their composition. Four of eight animals were contaminated, with a total of 19 microplastics detected in both fetal and placental samples. Specifically, fetuses from cats 4 and 7 were contaminated, as were the placentas from cats 5, 6, and 7. This work demonstrates that microplastics can accumulate in feline placentas even at the early stage of pregnancy. Moreover, preliminary results of the presence of microplastics in feline fetuses are shown, suggesting that microplastics can cross the placental barrier.
Study on the effect of CeO2 NP on combustion, emission and performance in Gunt CT110 diesel engine running with castor biodiesel blend
Intramolecular Singlet Fission in Individual Graphene Nanoribbons─Competition with a Charge Transfer
Study on the resistivity characteristics and mechanism of silt clay under different initial conditions
By taking silt clay as the research object, two-phase electrode resistivity tests under different water content and dry density conditions were carried out to clarify the resistivity variation law and influence mechanism of silt clay. The results show that the resistivity of the soil decreases sharply in the low moisture content section then tends to stabilize with the change of moisture content, and decreases continuously with the increase of dry density. There are three phases of a medium, namely soil, water and air, in unsaturated soil, so there are mainly three conductive paths: between soil particles, between pore fluids, and between soil-water coupling. Under different moisture content and dry density conditions, there are obvious differences in the effective contact area, and the types and numbers of conductive paths, which in turn affect the resistivity of the soil. The water status and pore structure of the silt clay samples were analyzed by hydrogen nuclear magnetic resonance (1H-NMR) results to clarify the conductive mechanism of unsaturated silt clay. Finally, a volumetric moisture content and resistivity model is established to unify the effects of moisture content and dry density on resistivity, providing a theoretical reference for the construction and operation safety of silt clay engineering.
Investigation on potential bias factors in histopathology datasets
Heteroepitaxial Growth of Narrow Band Gap Carbon-Rich Carbon Nitride Using In Situ Polymerization to Empower Sunlight-Driven Photoelectrochemical Water Splitting
Tetrafluororesorcin[4]arene Hexameric Capsule Enables the Expansion of the Reactivity Space in Supramolecular Catalysis
Socio-economic gradients in hypertension and diabetes management amid the COVID-19 pandemic in India
This study examines socio-economic inequalities in the prevalence and treatment of hypertension and diabetes among adults in India, utilising data from the National Family Health Survey (NFHS) collected before and during the COVID-19 pandemic. Disparities associated with individual demographic and socio-economic characteristics are measured, with the level of inequality quantified using the dissimilarity index and contributing factors analysed through decomposition analysis. The results reveal significant socio-economic gradients, with wealthier individuals more likely to have elevated blood pressure and blood glucose levels and to treat them. Socio-economic gradients in treatment are even steeper among middle-aged groups during the pandemic. These wealth- and education-related disparities become more pronounced with age. This study highlights the need for targeted interventions and policies to address socio-economic disparities in access to essential care for socio-economically disadvantaged populations.
Revision of the Formycin A and Pyrazofurin Biosynthetic Pathways Reveals Specificity for <scp>d</scp>-Glutamic Acid and a Cryptic <i>N</i>-Acylation Step During Pyrazole Core Formation
Socioeconomic disparities in anthropometric status among primary school children: A potential association with school meals
Objective To assess the growth and nutritional status of children in primary schools across different socioeconomic groups in Wad-Madani City, Central Sudan, and map it to World Health Organization (WHO) standards; and to investigate a potential association between school meal intake and nutritional status. Methods This cross-sectional anthropometric study involved a randomly selected sample of 506 children from 10 primary schools in the city. Height and weight were measured following WHO standards and converted into Z-scores for weight-for-age (WAZ), height-for-age (HAZ), and BMI-for-age (BAZ). We compared the mean Z-scores between children in the private and public school sectors, adjusting for ethnicity and other potential predictors. Statistical analyses included multivariate linear regression to assess predictors of growth and nutritional status, alongside group comparisons using appropriate statistical tests. Results Children in public schools had significantly lower BAZ and HAZ levels compared to both WHO standards and private school children. The mean BAZ was -1.0 (SD = 1.23) for public school children and -0.13 (SD = 1.40) for private school children (p = 0.001), with 17.8% (n = 57) of public school children classified as thin (wasted) or severely wasted. The median HAZ was -0.20 (95% CI: -0.34, -0.02) for public school children and 0.19 (95% CI: 0.03, 0.40) for private school children (p < 0.001). Additionally, children in suburban public schools had a significantly lower mean HAZ (-0.46, SD = 11.33) compared to those in urban public schools (p = 0.009). Compared to WHO growth standards, public school children had significantly lower mean WAZ (p < 0.001), HAZ (p = 0.002), and BAZ (p < 0.001). Children who received school meals had significantly higher WAZ (mean difference = 0.619, p = 0.001), HAZ (mean difference = 0.401, p = 0.010), and BAZ (mean difference = 0.588, p = 0.003) across the entire sample. Even within the public-school subgroup, while statistical significance was not reached, all three parameters—WAZ (mean difference = 0.334, p = 0.074), HAZ (mean difference = 0.262, p = 0.123), and BAZ (mean difference = 0.299, p = 0.132)—remained consistently higher among those who received school meals. Conclusion Public school children exhibit unfavorable growth and nutritional status, which may be attributed to inadequate nutritional and calorie intake. School meals may improve nutritional outcomes. We propose urgent intervention through the provision of nutritionally adequate school meals.
<i>Mycobacterium tuberculosis</i> CrgA Forms a Dimeric Structure with Its Transmembrane Domain Sandwiched between Cytoplasmic and Periplasmic β-Sheets, Enabling Multiple Interactions with Other Divisome Proteins
A dynamic transmission model for assessing the impact of pneumococcal vaccination in the United States
Streptococcus pneumoniae (SP) is a bacterial pathogen that kills more than 300,000 children every year across the globe. Multiple vaccines exist that prevent pneumococcal disease, with each vaccine covering a variable number of the more than 100 known serotypes. Due to the high effectiveness of these vaccines, each new pneumococcal conjugate vaccine (PCV) introduction has resulted in a decrease in vaccine-type disease and a shift in the serotype distribution towards non-vaccine types in a phenomenon called serotype replacement. Here, an age-structured compartmental model was created that reproduced historical carriage transmission dynamics in the United States and was used to evaluate the population-level impact of new vaccine introductions into the pediatric population. The model incorporates co-colonization and serotype competition, which drives replacement of the vaccine types by the non-vaccine types. The model was calibrated to historical age- and serotype-specific invasive pneumococcal disease (IPD) data from the United States. Vaccine-specific coverage and effectiveness were integrated in accordance with the recommended timelines for each age group. Demographic parameters were derived from US-population-specific databases, while population mixing patterns were informed by US-specific published literature on age-group based mixing matrices. The calibrated model was then used to project the epidemiological impact of PCV15, a 15-valent pneumococcal vaccine, compared with the status quo vaccination with PCV13 and demonstrated the value of added serotypes in PCV15. Projections revealed that PCV15 would reduce IPD incidence by 6.04% (range: 6.01% to 6.06%) over 10 years when compared to PCV13.
Enlightened prognosis: Hepatitis prediction with an explainable machine learning approach
Hepatitis is a widespread inflammatory condition of the liver, presenting a formidable global health challenge. Accurate and timely detection of hepatitis is crucial for effective patient management, yet existing methods exhibit limitations that underscore the need for innovative approaches. Early-stage detection of hepatitis is now possible with the recent adoption of machine learning and deep learning approaches. With this in mind, the study investigates the use of traditional machine learning models, specifically classifiers such as logistic regression, support vector machines (SVM), decision trees, random forest, multilayer perceptron (MLP), and other models, to predict hepatitis infections. After extensive data preprocessing including outlier detection, dataset balancing, and feature engineering, we evaluated the performance of these models. We explored three modeling approaches: machine learning with default hyperparameters, hyperparameter-tuned models using GridSearchCV, and ensemble modeling techniques. The SVM model demonstrated outstanding performance, achieving 99.25% accuracy and a perfect AUC score of 1.00 with consistency in other metrics with 99.27% precision, and 99.24% for both recall and F1-measure. The MLP and Random Forest proved to be in pace with the superior performance of SVM exhibiting an accuracy of 99.00%. To ensure robustness, we employed a 5-fold cross-validation technique. For deeper insight into model interpretability and validation, we employed an explainability analysis of our best-performed models to identify the most effective feature for hepatitis detection. Our proposed model, particularly SVM, exhibits better prediction performance regarding different performance metrics compared to existing literature.