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Comparison of glenohumeral joint kinematics between swimmers clinically classified with multidirectional instability and asymptomatic controls

The clinical classification of glenohumeral joint instability is characterized by presumed increased humeral translations in conjunction with symptoms of instability. Prior research reports inconsistent kinematic differences in glenohumeral kinematics between individuals clinically classified with multidirectional instability and asymptomatic controls. Differing clinical classifications and motion tracking methods likely contribute to this gap. This analysis aimed to compare three-dimensional (3D) glenohumeral joint kinematics during active arm raising between individuals clinically classified with multidirectional instability and asymptomatic matched controls. Twenty competitive swimmers (13 female; mean age: 24.85; standard deviation (SD): 12.51) clinically classified with multidirectional instability via a comprehensive clinical examination and 10 asymptomatic matched controls (6 female: mean age: 24.70; SD: 7.04) were enrolled. Active, unweighted, scapular plane abduction was recorded with dynamic biplane video radiography, and glenohumeral joint kinematics were reconstructed with 2D/3D shape-matching. The variables compared between groups included: humeral position along the anterior/posterior and superior/inferior axes of the glenoid, positional dispersion of the humeral instantaneous helical axis, and humeral contact path length on the glenoid. The average humeral position between 30°-90° of glenohumeral elevation was significantly more anterior (+0.8 mm, P  < 0.001, effect size = 0.57) in individuals classified with multidirectional instability compared to controls. No other significant differences were detected. Our findings indicate that individuals classified with multidirectional instability possess significantly greater average humeral head position in the anterior direction. However, these individuals do not possess markedly different glenohumeral joint kinematics in superior/inferior humeral position, humeral instantaneous helical axis positional dispersion, or humeral contact path length compared to asymptomatic individuals during unweighted arm elevation. Further exploration is necessary to identify novel kinematic variables that accurately quantify group differences in joint stability.

Comment on “On the Fresnel factor correction of sum-frequency generation spectra of interfacial water” [J. Chem. Phys. 158, 044701 (2023)]

Quality evaluation of Bombyx Batryticatus from different sources in China and quality analysis based on cross sectional silk gland rings

First Balkan Brief Illness Perception Questionnaire (IPQ-B) among high-risk pregnancies

Background Pregnancy is a particularly delicate period in which many health-related changes lead to changes in the perception of women’s well-being, in both physiological and especially high-risk pregnancies. In high-risk pregnancies, the relationship between illness perception and general well-being is even more complicated as sometimes there might not be any apparent signs or symptoms, but the pregnant woman or foetus still might be at risk. Aims To assess the validity and reliability of the existing Serbian version of the Brief Illness Perception Questionnaire (B-IPQ) in a specific population of pregnant women with high-risk pregnancies (HRP). Methods This was a cross-sectional study including 290 patients hospitalized at the Clinic for Gynaecology and Obstetrics, University Clinical Centre of Serbia. The research instrument was a questionnaire with six sections: 1) socio-demographic; 2) pregnancy-related; 3) COVID-19 pandemic–related data 4) B-IPQ; 5) The World Health Organization Quality of Life Brief Version (WHOQOL-BREF) and 6) The Depression, Anxiety and Stress Scale – 21 Items (DASS-21). Psychometric properties of the Serbian version of B-IPQ were analysed through factorial structure and internal consistency (reliability). Confirmatory factor analysis (CFA) was performed to confirm the original two-dimensional structure of the IP. Results Analysis of internal consistency of the Serbian version of the eight-item IPQ-B showed that Cronbach’s alpha of the entire scale was 0.7, indicating good scale reliability. IP correlated significantly with QoL related to mental health, stress, anxiety, and depression levels. The consequence domain of IP affected mental health mostly. IP was one of the main direct predictors of QoL and an indirect predictor through depression, anxiety, and stress levels. Marital status, hypertension in pregnancy, fear for health during the COVID-19 pandemic, and being informed during the COVID-19 pandemic had direct negative effects on IP, and indirectly on QoL. Conclusions The Serbian version of IPQ-B has good reliability and validity for illness perception in high-risk pregnancies.

The origin of the Stokes–Einstein relation in simple dense liquids

We investigate the origin of the universal relation between structural relaxation and diffusion in simple dense liquids, known as the Stokes–Einstein (SE) relation. The fact that this relation, originally derived from a hydrodynamic model of a macroscopic particle in a viscous medium, can describe the microscopic-scale liquid dynamics still eludes understanding. We introduce a new universal measure of structural relaxation in a system of N identical particles based on an explicit decomposition of the configuration space into N! congruent convex polyhedra. This measure makes it possible to quantify the correlation between two distinct particle configurations in terms of their minimal Euclidean distance, optimized with respect to particle permutations. Using this measure alongside a model of independent random walkers under the single-occupancy constraint, we derive a master equation that quantifies the SE relation. It allows us to demonstrate that the universal relation between structural relaxation and diffusion in simple dense liquids is caused by two conditions: (a) the confinement of the dominant density fluctuations to the first coordination shell, manifested by de Gennes narrowing, and (b) Gaussianity of the diffusion process; the former is shown to be violated in low-density fluids, and the latter is known to be violated in supercooled liquids.

Museum genomics suggests long-term population decline in a putatively extinct bumble bee

Pollinator declines globally threaten ecosystem stability and agricultural productivity. Reconstructing pollinator historic demographies provides an evolutionary perspective to understand contemporary population declines. The Franklin bumble bee ( Bombus franklini ), once endemic to Oregon and California and last observed alive in 2006, is emblematic of this phenomenon. We collected whole-genome sequence data from museum specimens spanning four decades to elucidate the genetic and demographic history of this potentially extinct species. Heterozygosity estimates of 25 individuals were remarkably low, and runs of homozygosity (ROH) patterns identified short segments suggestive of historical inbreeding, with some individuals having almost entire chromosomes in ROH. Demographic reconstructions revealed a marked decline in effective population size beginning in the late Pleistocene, with further declines in the last 400 y, which may have been influenced by fire and drought stressors. We found little to no genomic evidence implicating pathogens in the species’ decline and used coalescent simulations to show that we would be able to detect recently reduced heterozygosity only when colony-level survival rates are 15 to 30%. We conclude that a combination of historically low effective population size and genetic diversity along with environmental stochasticity heightened this species’ extinction vulnerability prior to recent anthropogenic stressors. This study demonstrates the utility of museum collections for clarifying genetic and demographic dynamics of rare species and suggests that B. franklini may have already been on a trajectory of decline prior to human impacts.

Ovariectomy attenuates phenotypes related to Alzheimer’s disease in a preclinical mouse model and in C57BL/6 J mice

Abstract Women are at higher risk for Alzheimer’s disease (AD) than men and hormonal changes during perimenopause are considered a risk factor. The relationship between ovarian hormones and AD has been explored using AD animal models, especially through ovariectomy (OVX) in established AD models. The link between early-stage AD and ovarian hormones, however, remains unclear, largely due to the lack of suitable animal models. Appropriate models for studying early-stage AD pathology, treatment, and prevention are critically needed. The App knock-in mouse model, which carries a single amyloid precursor protein ( App ) gene mutation, effectively reproduces early amyloid AD pathology. To elucidate the relationship between ovarian hormone deficiency and the behavioral phenotypes of a preclinical AD model, we applied a comprehensive behavioral test battery to this mouse model with bilateral OVX. The App mutation reduced anxiety-like behavior and impaired performance in the fear memory task. OVX restored the anxiety-like behavior of the App mutation mice to a level comparable to that in wild-type (WT) mice. Furthermore, OVX enhanced performance in a fear memory task in both genotypes and reduced amyloid-β staining in WT mice. Together, these findings suggest that OVX attenuates AD-related phenotypes in a preclinical AD model and in C57BL/6 J WT mice.

Novel Linalool-Silver nanoparticles: Synthesis, characterization, and dual approach evaluation via computational docking and antibacterial assays

In recent years, scientists have developed new medical delivery techniques based on nanotechnology and have been actively creating nanoparticles combined with various extracts from natural plant products. This study aimed to conjugate linalool surface with silver nanoparticles, investigate its characteristics, and evaluate its effectiveness as a possible new therapeutic target against bacterial strains. A CMC-linalool solution was used to create linalool-based AgNPs (LN@AgNPs), which were then characterized by UV-Vis, FTIR, SEM, DLS, and zeta potential studies. The shape, hydrodynamic diameter, and negative zeta potential were among the advantageous properties of the resultant particles, which improved their performance and stability. Antibacterial potential was assessed using both in silico and in vitro methods. According to molecular docking, LN@AgNPs exhibit strong interactions via hydrogen bonding and potential metallic chelation with important bacterial protein residues (Cys, His, and Thr). According to the in vitro assays, LN@AgNPs exhibited inhibitory zones comparable to those of azithromycin and stronger antibacterial efficacy than free linalool against Salmonella enterica , Bacillus subtilis , and Escherichia coli . According to these results, LN@AgNPs may be a good option for creating potent antimicrobial agents. To improve its therapeutic application, further investigation of its mode of action and in vivo safety profile is necessary.

Large deviations of ionic currents in dilute electrolytes

We evaluate the exponentially rare fluctuations of the ionic current for a dilute electrolyte by means of macroscopic fluctuation theory. We consider the fluctuating hydrodynamics of a fluid electrolyte described by a stochastic Poisson–Nernst–Planck equation. We derive the Euler–Lagrange equations that dictate the optimal concentration profiles of ions conditioned on exhibiting a given current, whose form determines the likelihood of that current in the long-time limit. For a symmetric electrolyte under small applied voltages, number density fluctuations are small, and ionic current fluctuations are Gaussian with a variance determined by the Nernst–Einstein conductivity. Under large applied potentials, the ionic current distribution is generically non-Gaussian. Its structure is constrained thermodynamically by Gallavotti–Cohen symmetry and the thermodynamic uncertainty principle.

Viscoelastic structural damping enables broadband low-frequency sound absorption

Low-frequency sound absorption has traditionally relied on air-resonant structures, such as Helmholtz resonators, which are made of stiff materials that undergo negligible deformation. In these systems, energy dissipation arises primarily from air motion and thermal–viscous effects, resulting in inherently narrowband performance and bulky, complex designs for broadband absorption. Here, we presented a composite acoustic metamaterial that replaces the high-stiffness neck of a Helmholtz resonator with a soft, viscoelastic cylindrical shell. This structural modification enables material deformation and shifts the dominant energy dissipation mechanism from air resonance to intrinsic viscoelastic damping. A single unit achieves over 97% absorption across a broad low-frequency range (227 to 329 Hz) with deep-subwavelength thickness (λ/15 at 227 Hz). We developed a discretized impedance model that quantitatively links material properties and geometry to absorption behavior. Our results established a materials-centered design paradigm in which both material selection and geometry serve as coequal, tunable parameters for compact, broadband low-frequency sound control.

Evolution of AI in anatomy education study based on comparison of current large language models against historical ChatGPT performance

A novel extended inverse Weibull distribution: Statistical analysis and application

This paper proposes a new type of exponential-type Weibull distribution based on the inverse Weibull distribution --- the transformed inverse Weibull distribution. This distribution constructs a more flexible parameter structure through mathematical transformation and has a better fitting effect on actual data. We deeply analyzed the key statistical properties of this distribution, including the probability density function, survival function, quantile function, as well as Shannon entropy, Rényi entropy, Tsallis entropy, and Mathai-Haubold entropy, etc. In terms of parameter estimation, various parameter estimation methods such as maximum likelihood estimation and Bayesian estimation were adopted to estimate the parameters of the transformed inverse Weibull distribution, and the performance of various parameter estimation methods was evaluated through Monte Carlo simulation. Finally, two sets of real data were applied to verify the applicability and effectiveness of the model in practical applications. The results show that the transformed inverse Weibull distribution exhibits a superior fitting performance in the goodness-of-fit test compared to the Weibull distribution, weighted exponential distribution, exponential Pareto distribution, flexible Weibull distribution, generalized exponential distribution, and generalized inverse exponential distribution.

The Hitchhiker’s guide to differential dynamic microscopy

Over nearly two decades, differential dynamic microscopy (DDM) has become a standard technique for extracting dynamic correlation functions from time-lapse microscopy data, with applications spanning classical soft matter systems, such as colloidal suspensions, liquid crystals, polymer solutions, gels and glasses, and active fluids and biological systems. In its most common implementation, DDM analyzes image sequences acquired with a conventional microscope equipped with a digital camera, yielding time- and wavevector-resolved information analogous to that obtained in multi-angle dynamic light scattering. With a widening array of applications and a growing, heterogeneous user base, lowering the technical barrier to performing DDM has become a central objective. In this tutorial article, we provide a step-by-step guide to conducting DDM experiments—from planning and acquisition to data analysis—intended as a resource for both new and experienced practitioners. We also introduce the open-source software package, fastDDM, designed to efficiently process large image datasets using optimized, parallel algorithms that reduce analysis times by up to four orders of magnitude on typical datasets (e.g., 10 000 frames), thereby enabling high-throughput workflows, reproducibility, and broader adoption across disciplines.

A yellow warbler is for the climate as a canary is for the coal mine

In vitro, in silico, and DFT evaluation of antimicrobial imidazole derivatives with insights into mechanism of action

Correction: Blindness and visual impairment in Central Europe

Accelerating phase diagram construction through activity coefficient prediction

Obtaining phase diagrams from molecular simulations remains computationally demanding due to the need for extensive sampling of coexistence conditions and large system sizes. This study demonstrates a novel methodology for efficiently predicting phase behavior in Lennard-Jones mixtures by leveraging machine learning. Here, we train a Gaussian process (GP) model on Kirkwood–Buff Integrals (KBIs) to establish a predictive link between KBIs and activity coefficients—a key thermodynamic quantity encoding deviations from ideality. Through the incorporation of KBI trends, the GP model leads to the prediction of the activity coefficients of two new systems without prior knowledge of their phase behavior, eliminating the need for direct coexistence simulations and significantly reducing computational cost. This framework has broad applicability in computational thermodynamics, offering a scalable strategy for studying complex mixtures with tunable interactions.

A national randomized controlled trial of the impact of public Montessori preschool at the end of kindergarten

Although seminal studies from the early 1960s suggested quality preschool can have lasting positive effects, agreement is lacking on the efficacy of different preschool models. The Montessori model is longstanding but lacks rigorous impact studies; prior random lottery studies included just one or two schools, among other compromises. Here, we report on end-of-kindergarten (age 5 to 6) impacts from a national study of public Montessori preschool. We compared children offered a Montessori seat via competitive lottery admission processes at one of 24 public Montessori schools at age 3 ( n = 242 ) to children not offered a seat ( n = 346 ), estimating Montessori impacts with intention-to-treat and complier average causal effect models. Roughly half of the treatment sample still attended Montessori for kindergarten. Although there were no notable impacts at the end of PK3 or PK4, at the end of kindergarten, controlling for baseline scores and demographics, Montessori children had significantly higher reading, short-term memory, theory of mind, and executive function scores. Intention-to-treat effect sizes exceeded a fifth of a SD, considered large in field-based school research [M. A. Kraft, Educ. Res. 49 , 241–253 (2020)]. This contrasts sharply with the more typical finding, where impacts of preschool are observed immediately following the program but disappear by the end of kindergarten. Further, a cost analysis suggested three years of public Montessori preschool costs less per child than traditional programs, largely due to Montessori having higher child:teacher ratios in PK3 and PK4. Although sensitivity and robustness analyses yielded similar results, important limitations of the study should be noted.

Correction: Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches

Navigating interprofessional collaboration in diabetes care: A qualitative study of early-career health professionals in malaysian primary care clinics

Introduction Interprofessional collaborative care (IPC) is essential for effective healthcare delivery, particularly in managing chronic conditions such as diabetes in primary care settings. However, early-career health professionals (ECHPs) often encounter significant challenges when establishing effective IPC, given its inherent complexity. This study explores how ECHPs in primary care clinics navigate and engage in IPC for diabetes management. Methods A qualitative study was conducted from 1 st December 2021–1 st October 2022 at two Malaysian primary care clinics (urban and suburban). Seven ECHPs meeting predefined criteria (6 months to 5 years’ experience, no postgraduate degree) were purposively sampled and interviewed until data saturation. In-depth semi-structured interviews (face-to-face or virtually via Zoom), conducted in either English or Malay, were audio- or video-recorded and transcribed verbatim. Data were analyzed using Braun and Clarke’s reflexive thematic analysis with constant comparison to ensure rigor. Results Seven main themes emerged regarding how ECHPs in primary care clinics navigate and engage in interprofessional collaborative practices: (1) Initiating and continuing dialogue, (2) Creating cohesiveness, (3) Effective ways of communication, (4) Having own personal values, (5) Willing to work synergistically, (6) Learning from each other, and (7) Embracing diversities and resolving conflict. These themes represent interrelated components that ECHPs had adopted to effectively engage in interprofessional collaborative practices. Conclusion IPC in diabetes management is a complex system requiring ECHPs to employ interrelated components for effective engagement. ECHPs overcame hierarchical barriers through proactive dialogue, reflecting a shift toward egalitarian teamwork. Digital platforms aided coordination, though face-to-face interactions were preferred for complex cases and direct communication. Team cohesion was strengthened through shared leadership, conflict resolution, and interprofessional learning, enabling ECHPs to adapt and contribute confidently. Educational institutions should integrate emotional intelligence, negotiation skills and digital ethics into IPC curricula. Healthcare organizations must reinforce collaborative practices through policies, mentorship and structured training to bridge theory-practice gaps. Future research should explore informal socialization, peer coaching and long-term digital communication impacts to strengthen IPC support for ECHPs.