Leaving Academia: Dual-Career Relationships and Partners' Attrition from Academic Careers.

Background: More than one third of academics are coupled with another academic, with more women being in such dual-career relationships. Little is known about how these couples' experiences affect their attrition from or persistence in academia.

Methods: We analyzed survey data of academics at 100 U.

Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases.

Missing data is pervasive in healthcare. Many imputation methods exist to fill in missing values, yet most were evaluated using randomly deleted values rather than the actual mechanisms they were designed to address. We aimed to determine real-world accuracy for missing data imputation with three missing data mechanisms (missing completely at random, MCAR; missing at random, MAR; and not missing at random, NMAR) for state of the art and commonly used imputation methods.

Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-grained Labels.

Sleep apnea, a prevalent sleep-related breathing disorder, often remains undiagnosed and untreated in a large patient population due to the need of extensive manual annotations on various physiological signals for clinical diagnosis. Despite the surge of interest in applying machine learning to automate apnea detection, the effectiveness of existing techniques highly relies on strongly supervised learning that requires massive finely labeled training data for sufficiently short time intervals - a requirement often unmet due to the prohibitively high cost of manual labeling in clinical practice. In this article, we incorporate clinical knowledge to establish a weakly supervised deep learning framework for automatically estimating the latent fine-grained apnea severity when only coarse-grained labels indicating apnea presence are available in the training data.

Assessing PFAS and Their Precursor Transformation in a Landfill Leachate-Impacted Wastewater Treatment Plant.

Despite growing concern over polyfluoroalkyl and perfluoroalkyl substances (PFAS), comprehensive evaluations of their behavior in wastewater treatment plants (WWTPs) influenced by landfill leachate remain limited. This study introduces a novel, integrated framework to investigate the fate, transformation, and persistence of PFAS across various treatment stages within a full-scale WWTP uniquely impacted by a mixture of domestic wastewater and industrial landfill leachate. By combining advanced targeted analysis using USEPA Draft Method 1633 with extractable organic fluorine (EOF) quantification and total oxidizable precursor (TOP) assays, this approach offers a comprehensive, multidimensional characterization of PFAS behavior.

How maximal whole-body and segmental angular momenta relate to fastball speeds in high school baseball pitchers.

Generation of whole-body and segmental rotations are important objectives of baseball pitching and may be quantified using angular momentum about the body's center of mass (L). However, the characteristics of L during baseball pitching are not well understood, and it is not known whether generating more whole-body or segmental L would be beneficial for the performance outcome of fastball speeds. We characterized whole-body and segmental L and hypothesized that ball speed would positively associate with maximum whole-body, pelvis, trunk, pitching upper arm, forearm, and hand L.

Integrative Genomic and Immune Profiling to Identify and Characterize High-Risk Subgroups in Acute Myeloid Leukemia: Development of a 20-Gene Predictive Signature and Its Clinical Implications.

Acute myeloid leukemia (AML) is a heterogeneous malignancy with diverse genetic mutations and oncogenic pathways influencing treatment response. Despite therapeutic advances, relapse and resistance remain persistent issues. This study integrates genomic and transcriptomic profiling to identify biomarkers of high-risk AML, informing personalized medicine strategies.

A Linear-Regression-Based Method for Determining Surface Tension from Variation in Interfacial Curvature.

We develop a new linear-regression-based method for determining surface tension, γ, from interfacial curvature. Across a static fluid-fluid interface, γ is balanced by the difference in hydrostatic pressure, Δ. The balance is described by the Young-Laplace relation: Δ = γ (1 + 2), where 1 + 2 is the sum of the principal interfacial curvatures.

Chip-integrated spectroscopy capable of temperature retrieval.

We demonstrate a chip-integrated emission spectrometer capable of retrieving the temperature of the light sources. It consists of a single photon detector with low dark counts and a sweeping on-chip filter with 2 pm spectral resolution in the visible and near-infrared regimes. With wildfire sensing applications in mind, we test our system with a hollow cathode lamp to simulate the K-line emission, and show how the models of Doppler and collision broadening in the plasma can be used for temperature retrieval.

Food-Specific Morning and Night Intake Scores Are Associated with Impaired Glucose Tolerance during Pregnancy.

Background: Impaired glucose tolerance (IGT) during pregnancy is associated with numerous short- and long-term adverse health outcomes. Diet is a key factor influencing glucose tolerance, yet there are little data on the relationship between specific foods or intake timing and IGT.

Objectives: We examined whether food category intakes and their timing are associated with IGT.

In vivo dynamic imaging reveals the oviduct as a leaky peristaltic pump in transporting a preimplantation embryo toward pregnancy.

The mammalian oviduct (also called the fallopian tube) is an essential organ for natural pregnancy. As one of its major functions, the oviduct transports preimplantation embryos to the uterus for implantation. This is a critical process, and abnormalities are responsible for a range of reproductive disorders, such as tubal ectopic pregnancy and infertility, whose etiologies are unclear.

In-situ thermo-mechano-chemical transformation and consolidation of Sm-Co powders via a single-step route for bulk magnet fabrication.

The demand for high-performance permanent magnets continues to grow across a wide range of advanced technologies. However, conventional powder metallurgy routes for rare-earth magnets such as Sm-Co are limited by the intrinsic brittleness of the powders, reducing manufacturability and yield. Here, we report a single-step, solid-state processing method-friction consolidation (FC)-that enables simultaneous deformation, heating, and chemical transformation of brittle Sm-Co powders.

Normative Peak Physical Activity Values for Monitor-Independent Movement Summary Units: National Health and Nutrition Examination Survey 2011-2014.

Background: The most recent physical activity (PA) monitor data from the US National Health and Nutrition Examination Survey (NHANES) were processed using a novel monitor-independent movement summary (MIMS) algorithm. To date, few studies have utilized these data, likely due to a general unfamiliarity with MIMS-related metrics. The purpose of this study was to establish normative values for peak MIMS metrics as measures of free-living PA intensity and natural ambulatory effort.

Investigating the Role of Wearable Devices in Facilitating Telehealth Adoption Among the Aging Population: Mediation Analysis of US National Data.

Background: Telehealth adoption has grown significantly, presenting valuable opportunities for the aging population to access health care remotely. Despite evidence of its benefits in managing chronic conditions and promoting independence, many older adults remain hesitant to adopt telehealth, preferring traditional in-person visits even post pandemic. Current literature largely focuses on younger or general populations, overlooking the unique barriers faced by older adults, such as technology literacy and access disparities.

Predicting Subcutaneous Antibody Bioavailability Using Ensemble Protein Language Models.

Monoclonal antibodies are pivotal in modern therapeutics, yet predicting their subcutaneous bioavailability remains challenging due to the intricacies of the SC environment and the limitations of traditional experimental models. In this study, we introduce a novel machine learning framework that leverages protein language models (PLMs) to derive high-dimensional embeddings directly from antibody sequences. Using three distinct PLMs─antiBERTy, ABlang, and ESM-2─we extracted numerical representations that were subsequently refined feature selection and dimensionality reduction.

Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery.

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure-property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback.

AI-Prediction of Resistance at the Point of Care from Genomic and Epidemiologic Data.

: Antimicrobial resistance (AMR) in is an escalating global health challenge, affecting over 82 million individuals each year. The increasing resistance to commonly used antibiotics such as azithromycin, ciprofloxacin, and cefixime hinders timely and effective treatment, primarily due to the delayed detection of resistant strains. : To overcome these limitations, a hybrid machine learning (ML) and deep learning (DL) framework was developed using a dataset comprising 3786 isolates.

Maximizing the therapeutic benefits of biopolymer-derived nanoparticles in wound healing.

Nanoparticles have emerged as a cornerstone of nanomedicine, offering transformative potential to modern healthcare through their multifunctional capabilities. Their adaptability positions them as ideal candidates for wound management, either as advanced wound dressings or as efficient drug delivery systems. With intrinsic antibacterial properties and the ability to enhance tissue repair, nanoparticles have gained significant attention in promoting effective wound healing.

Nonlocal Metasurface Lens for Long-Wavelength Infrared Radiation.

Dielectric metasurfaces are structured thin films with a thickness smaller than the operating wavelength aiming at replacing and enhancing conventional bulk optical components. At visible and near-infrared frequencies, titania or silicon is routinely used as substrates to realize these ultrathin devices by structuring local resonances across an aperture. Unfortunately, directly scaling the same design and material approaches to long-wave infrared frequencies is unpractical, due to the resulting thickness and the presence of phonon absorption lines.

Physics-guided estimation of mean first-passage times from censored nucleation trajectories.

Mean first-passage time (MFPT) analysis is a powerful tool for extracting thermodynamic and kinetic parameters of nucleation, including the critical nucleus size, nucleation barrier, and nucleation rate, from molecular dynamics (MD) simulations. However, accurate MFPT estimation typically requires extensive sampling and long simulation durations, making it computationally expensive, especially for rare nucleation events. Here, we present an efficient method to estimate MFPT from fixed-length MD simulations by leveraging the single-exponential tail (SET) behavior of first-passage time distributions in monomer-transition-based nucleation processes.

Orlistat-dopamine conjugate micelles improve targeted delivery and therapeutic efficiency of camptothecin in combination chemotherapy.

Conventional chemotherapy as a monotherapy often suffers from drug resistance, severe systemic toxicity, and limited delivery efficiency. To address these limitations, we developed orlistat-dopamine conjugate micelles (ODCMs) as a dual-functional nanocarrier for camptothecin (CPT), enabling synergistic combination therapy against multidrug-resistant cancers. Owing to the bio-adhesive properties of the catechol moieties present in ODCMs, CPT-ODCMs rapidly accumulated at the cancer cell membrane and facilitated efficient cellular uptake.

Computationally Modeling the Physiologic Impact of the Ratio of Fats to Carbohydrates in the Diet on Intake Among Metabolically Healthy Adults.

Background: When it comes to how effectively a diet can help reduce or maintain body weight, a key question is how that diet affects a person's hunger, satiety, and subsequent eating.

Objectives: This study aimed to analyze modeling, from a physiologic perspective, how varying the ratio of fats to carbohydrates in a diet impacts hunger, satiety, and subsequent eating among metabolically healthy adults.

Methods: We developed a model representing an adult, their dietary intake, gastrointestinal tract, hunger/satiety levels, and meal consumption.

Computational Mechanistic Investigation of Biocatalytic C(sp)-H Insertions with Monosubstituted Carbenes via Engineered Heme Proteins.

A recent experimental study by Arnold's research group ( , , 67-72) showed the engineered cytochrome P450 enzyme (cytochrome P411), with acceptor-only carbenes, offers a new sustainable alternative to metal catalysts that have traditionally dominated C-H functionalization. However, there is still no detailed mechanistic understanding thus far. In this study, a series of DFT calculations were performed to uncover the basic reaction mechanism as well as insights into the observed carbene substituent effect behind this novel reaction.

Toward automatic and reliable evaluation of human gastric motility using magnetically controlled capsule endoscope and deep learning.

In this paper, we develop a combination of algorithms, including camera motion detector (CMD), deep learning models, class activation mapping (CAM), and periodical feature detector for the purpose of evaluating human gastric motility by detecting the presence of gastric peristalsis and measuring the period of gastric peristalsis. Moreover, we use visual interpretations provided by CAM to improve the sensitivity of the detection results. We evaluate the performance of detecting peristalsis and measuring period by calculating accuracy, F1, and area under curve (AUC) scores.

Impact of soil particle size on lead distribution, geochemical speciation, and bioaccessibility in lead paint-contaminated residential soils.

Deteriorating lead (Pb)-based paint is a major source of Pb contamination in urban areas. Lead contamination in homes poses significant health risks, especially to children. Children ingest Pb-contaminated soil through their hand-to-mouth activity.

Propensity and repercussion of microplastics in the soil-water-urban continuum.

Despite consistently increasing efforts to reduce legacy and emerging pollutants like microplastic (MP), agro-ecosystems, particularly soil carbon is alarmingly threated to be replaced by plastic carbon, endangering the one health. While MPs have witnessed unprecedented research focus and frequency spike, the hydro-biological aspects in connection to urbanization, agricultural practices and ecosystem services are not explicitly discussed. Hence, we provide a comprehensive examination of MP sources, transport, and factors influencing their migration in soil and groundwater, with specific emphasis on urbanization, surface-groundwater interactions, and flooding, its interactions with organic and inorganic pollutants, antibiotic resistance genes, and potential routes exposure to humans, and toxicity augmentation.