Postdoc publications and citations link to academic retention and faculty success.

Postdoctoral training is a career stage often described as a demanding and anxiety-laden time when many promising PhDs see their academic dreams slip away due to circumstances beyond their control. We use a unique dataset of academic publishing and careers to chart the more or less successful postdoctoral paths. We build a measure of academic success on the citation patterns two to five years into a faculty career.

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network.

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.

Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids.

Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from both models)-to enhance the predictive accuracy for xylazine detection in illicit opioid samples. Three chemometric approaches-random forest, support vector machine, and -nearest neighbor algorithms-were employed and optimized using a 5-fold cross-validation grid search for all fusion strategies. Validation results identified the random forest classifier as the optimal model for all fusion strategies, achieving high sensitivity (88% for hybrid, 92% for mid-level, and 96% for high-level) and specificity (88% for hybrid, mid-level, and high-level).

χ-sepnet: Deep Neural Network for Magnetic Susceptibility Source Separation.

Magnetic susceptibility source separation (χ-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill-conditioned problem of dipole inversion, suffering from so-called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation ( ) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for (e.

Time-Varying Spatial Propagation of Brain Networks in fMRI Data.

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags.

Determining the risk factors of malaria and anemia in children between 6 and 59 months using the joint generalized linear mixed model on the 2021 Nigeria Malaria Indicator Survey dataset.

Background: Malaria and anemia are significant public health concerns that contribute to child mortality in African. Despite global efforts to control the two diseases, their prevalence in high-risk regions like Nigeria remains high. Understanding socioeconomic, demographic, and geographical factors associated with malaria and anemia, is critical for effective intervention strategies.

SCN1A Genetic Alterations and Oxidative Stress in Idiopathic Generalized Epilepsy Patients: A Causative Analysis in Refractory Cases.

Single Nucleotide Polymorphisms (SNPs) have found it be associated with drug resistance in epilepsy. The purpose of this study was to determine the role of SCN1A gene polymorphism in developing drug resistance in idiopathic generalized epilepsy (IGE) patients, along with increased oxidative stress. The study was conducted at a tertiary care hospital in Delhi, India.

Prediction of Preeclampsia Using Machine Learning: A Systematic Review.

Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality. Early prediction is the need of the hour so that interventions like aspirin prophylaxis can be started. Nowadays, machine learning (ML) is increasingly being used to predict the disease and its prognosis.

Connectional differences between humans and macaques in the MT+ complex.

MT+ is pivotal in the dorsal visual stream, encoding tool-use characteristics such as motion speed and direction. Despite its conservation between humans and monkeys, differences in MT+ spatial location and organization may lead to divergent, yet unexplored, connectivity patterns and functional characteristics. Using diffusion tensor imaging, we examined the structural connectivity of MT+ subregions in macaques and humans.

Exploring the antifungal potential of -derived stilbenoids and cannabinoids against novel targets through protein interaction profiling.

Cannabinoid and stilbenoid compounds derived from were screened against eight specific fungal protein targets to identify potential antifungal agents. The proteins investigated included Glycosylphosphatidylinositol (GPI), Enolase, Mannitol-2-dehydrogenase, GMP synthase, Dihydroorotate dehydrogenase (DHODH), Heat shock protein 90 homolog (Hsp90), Chitin Synthase 2 (CaChs2), and Mannitol-1-phosphate 5-dehydrogenase (M1P5DH), all of which play crucial roles in fungal survival and pathogenicity. This research evaluates the binding affinities and interaction profiles of selected cannabinoids and stilbenoids with these eight proteins using molecular docking and molecular dynamics simulations.

The role of fMRI in the mind decoding process in adults: a systematic review.

Background: Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.

Methodology: A systematic search was conducted across Web of Science, PubMed, and Google Scholar.

Prospective Trial of a Passive Diversion Device to Reduce Blood Culture Contamination.

Background: Blood culture contaminants can lead to inappropriate antibiotic use, prolonged length of stay, and additional hospital costs. Several devices have been developed to reduce the risk of blood culture contamination by diverting a portion of the initial blood sample from the blood culture bottle. We assessed the effectiveness of 1 blood diversion device (BDD) in a prospective trial performed at the 2 separate emergency departments (EDs) of an academic medical center.

Research on upper limb rehabilitation assessment model based on belief rule base.

Rehabilitation assessments hold an irreplaceable role in the field of rehabilitative therapy. However, due to the subjectivity of traditional physicians and the variability of patient conditions, this leads to a lack of detailed grading and inaccurate assessment results. To address this issue, we developed an upper limb rehabilitation evaluation model.

BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches.

Background: Cancer rates are rising rapidly, causing global mortality. According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020.

Differential regulation of apoptosis-related genes during long-term culture and differentiation of canine adipose-derived stem cells - a functional bioinformatical analysis.

Introduction: Stem cells derived from adipose tissue are gaining popularity in the field of regenerative medicine due to their adaptability and clinical potential. Their rapid growth, ability to differentiate, and easy extraction with minimal complications make adipose-derived stem cells (ADSCs) a promising option for many treatments, particularly those targeting bone-related diseases. This study analyzed gene expression in canine ADSCs subjected to long-term culture and osteogenic differentiation.

S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE.

Background: The realization of many protein functions requires binding with ligands. As a significant protein-binding ligand, ATP plays a crucial role in various biological processes. Currently, the precise prediction of ATP binding residues remains challenging.

TransferBAN-Syn: a transfer learning-based algorithm for predicting synergistic drug combinations against echinococcosis.

Echinococcosis is a zoonotic parasitic disease caused by the larvae of echinococcus tapeworms infesting the human body. Drug combination therapy is highly valued for the treatment of echinococcosis because of its potential to overcome resistance and enhance the response to existing drugs. Traditional methods of identifying drug combinations via biological experimentation is costly and time-consuming.

NutriBase - management system for the integration and interoperability of food- and nutrition-related data and knowledge.

Introduction: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.

Methods: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge.

Design and process optimization of a new reaction chamber for microwave synthesis of MOFs materials.

Based on the application demand of metal-organic framework (MOFs) materials in environmental science, energy conversion, biomedicine and other fields, its efficient synthesis method has attracted much attention. Microwave method has become one of the most competitive and potential methods because of its low cost, high efficiency and green environmental protection. However, the traditional microwave assisted synthesis of MOFs materials mostly uses microwave oven as the reaction chamber, or small-scale microwave reactor.

Constructing a knee osteoarthritis transplant via the neutrality aggregation operators under spherical fuzzy Z-numbers framework.

Understanding the biomechanics of osteoarthritis is necessary for designing a biomedical knee implant to reduce pain, increase mobility, and enhance the patient's quality of life. The most appropriate implant design may be chosen by using Multi-Attribute Group Decision-Making (MAGDM) techniques, which include a number of variables including material characteristics, biomechanical performance, cost-effectiveness, and patient-specific requirements. Compared to conventional fuzzy set structures, Spherical Fuzzy -Number Sets ( S) provide an enhanced method for resolving uncertainty in MAGDM and are more suited for handling complicated decision-making situations.

Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm.

Purpose: To investigate the changes in meibomian gland (MG) structure in allergic conjunctivitis (AC) patients using an intelligent quantitative analysis algorithm and to explore the relationship between these changes and clinical parameters.

Methods: A total of 252 eyes from patients with AC and 200 eyes from normal controls were examined. Infrared meibography was performed using the non-contact mode of the Keratograph 5M.

Variation in faecal testosterone levels in male gray whales on a foraging ground relative to maturity and timing.

Understanding wildlife reproductive seasonality is crucial for effective management and long-term monitoring of species. This study investigates the seasonal variability of testosterone in male Pacific Coast Feeding Group (PCFG) gray whales, using an eight-year dataset (2016-2023) of individual sightings, drone-based photogrammetry and endocrine analysis of faecal samples. We analyzed the relationship between faecal testosterone levels and total body length (TL), body condition (body area index, BAI), sexual maturity and day of the year using generalized additive mixed models.