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Deep-strong tunable plasmon–exciton coupling utilizing Ag–Si core–shell combined with WS2 monolayer for quantum optics applications

In close proximity to quantum emitters (QEs), plasmonic nanoparticles (NPs) facilitate energy exchange with the QEs, which is known as plasmon–exciton coupling. The strong coupling regime, associated with Rabi splitting, is crucial for advanced nanophotonic devices, including solar cells, single-photon nonlinear optics, and nanolasers. Recently, high refractive index semiconductor NPs (typically Si NPs) have emerged for designing strongly coupled systems. However, their large mode volumes of magnetic Mie resonances have limited their success in achieving strong coupling. This study investigates the plasmon–exciton coupling between an Ag–Si core–shell and a monolayer QE of WS2 (Ag–Si–WS2 system) in air and water environments. Here, we compare the coupling dynamics of the hybrid Ag–Si–WS2 system to that of the Si–WS2 system as a benchmarking system. Employing Mie’s theory of core–shell scattering, in conjunction with Maxwell–Garnett effective medium theory, we analyze the optical responses of both configurations. Then, we calculate the Rabi splitting frequency for each system to identify the coupling regime. Our results suggest that the Ag–Si–WS2 system can achieve a deep-strong coupling regime when the Ag core radius is less than 30 nm, with enhanced coupling strength in water compared to air. Conversely, the Si–WS2 system does not achieve strong coupling in either medium. The hybrid modes in Ag–Si–WS2 demonstrate remarkable symmetrical spectral characteristics compared to the asymmetric spectral line shape observed in the Si–WS2 system. The findings suggest avenues for utilizing the plasmon–exciton strong coupling in the Ag–Si–WS2 system to enhance optoelectronic and quantum electronic devices.

Entropy-driven phase behavior of all-DNA associative polymers

Associative polymers (APs) with reversible, specific interactions between “sticker” sites exhibit a phase behavior that depends on a delicate balance between distinct contributions controlling the binding. For highly bonded systems, it is entropy that mostly determines whether, upon increasing concentration, the network forms progressively or via a first-order transition. With the aim of introducing an experimentally viable system tailored to test the subtle dependence of the phase behavior on binding site topology, we numerically investigate APs made of DNA, where “sticker” sites formed by short DNA sequences are interspersed in a flexible backbone of poly-T spacers. Due to their self-complementarity, each binding sequence can associate with another identical sticky sequence. We compare two architectures: one with a single sticker type, (AA)6, and the other with two distinct alternating types, (AB)6. At low temperatures, when most of the stickers are involved in a bond, the (AA)6 system remains homogeneous, while the (AB)6 system exhibits phase separation, driven primarily by entropic factors, mirroring predictions from simpler bead–spring models. Analysis of bond distributions and polymer conformations confirms that the predominantly entropic driving force behind this separation arises from the different topological constraints associated with intra- vs inter-molecular bonding. Our results establish DNA APs as a controllable and realistic platform for studying in the laboratory how the thermodynamics of associative polymer networks depend on the bonding site architecture in a clean and controlled manner.

A simple model for the barrier properties of brushes with an arbitrary chain length distribution

Polymer brushes can form protective barriers on surfaces, reducing fouling and adsorption of foreign entities. Predicting how the properties of such surfaces depend on physical brush parameters has technological implications for the applications of these coatings. However, most theoretical models require in-depth knowledge or advanced mathematical and computational skills, which prevents their broad use. Here, we present a simple model extending the Alexander–De Gennes ansatz for arbitrary chain length distributions, allowing us to easily rationalize the effect of polydispersity, which is a feature of all realistic brushes. This model can predict the interaction of a brush with nanoparticles or an opposing wall, as demonstrated by good qualitative agreement with molecular dynamics simulations. An open-source Python implementation of our model is freely available on GitHub, and the model allows for efficient screening of brush designs to reach technological applications.

Modeling water using multipole response tensors fitted to the monomer geometry

The water molecule’s electronic Cartesian multipole moment and polarizability tensors have been fitted with Gaussian process regression to the internal coordinates and are used to evaluate accurate electrostatic, induction, and dispersion energy components between flexible molecules. The model yields a handful of damping and scaling parameters that were adjusted for the energy components to agree with 2-body symmetry-adapted perturbation theory decomposition and then fine-tuned in order for the total energy to agree with CCSD(T) for small clusters. We present a simple algorithm for rotating symmetric Cartesian tensors and employ a dispersion potential based on multipole polarizabilities. At short range, the 2- and 3-body potential energy was corrected to CCSD(T) accuracy using Gaussian approximation potentials. The radial distribution function and self-diffusion coefficient obtained with molecular dynamics simulations agree well with experiments.

Exposure to trauma in pregnant women and its association with previous perinatal complications, IPV and antenatal service satisfaction in rural Ethiopia: a cross-sectional facility-based study

Background We aimed to describe the prevalence of exposure to traumatic events and post-traumatic stress disorder (PTSD) in pregnant women attending antenatal care (ANC) in rural Ethiopia. We hypothesised that antenatal PTSD symptoms would be associated with previous obstetric complications and intimate partner violence (IPV) and impact negatively on women´s satisfaction with ANC. Methods The design was a facility-based cross-sectional study in primary health centres providing ANC in southern Ethiopia. Trauma events were assessed using the Life Events Checklist (LEC) and PTSD checklist for DSM-5 (PCL-5). Previous obstetric complications were extracted from clinical records. IPV was measured using the ‘Non-Graphic Language’ screening test and ANC satisfaction was measured using a locally validated adapted version of the Mental Health Service Satisfaction Scale. Generalized linear mixed-effects regression models were used to calculate prevalence ratios between PTSD, IPV and ANC satisfaction. Results Out of 2079 interviewed women, 52.3% (n = 1,087) reported one or more traumatic life events on the LEC. Physical assault was the most common traumatic event experienced (n = 485; 23.3%) and witnessed (n = 1,176; 56.6%) but only 289 (13.9%) screened positive for IPV. One hundred and six women (5.1%) met DSM-5 criteria for PTSD. Women meeting diagnostic criteria for PTSD had five times increased prevalence of IPV in their current pregnancy [prevalence ratio (PR) 4.34, 95%CI 3.01–6.30; p < 0.001]. Only twenty-six women had a record of previous obstetric complications (0.01%). Overall, women with PTSD reported less satisfaction with antenatal care. Conclusions Despite high exposure to traumatic life events, particularly physical violence, among pregnant women attending ANC in Southern Ethiopia, the prevalence of PTSD is relatively low. Previous obstetric complications and IPV were under-reported, relative to known prevalence estimates. Our study highlights the challenges of detection of psychosocial needs in the ANC setting and the need for targeted interventions to support women’s disclosure of difficulties in maternity care settings.

Genetically diverse mice exhibit divergent domain-specific, sex-dependent behavioral outcomes following exposure to early life stress

Understanding how genetic variability shapes responses to environmental and developmental factors is critical for advancing translational neuroscience. However, most preclinical studies rely on inbred mouse strains that do not capture the genetic complexity of human populations. One key area of translational research focuses on identifying the neural and behavioral consequences of early life trauma. Rodent models of childhood neglect, such as maternal separation with early weaning (MSEW), have been used in isogenic strains like C57BL/6J (B6) to identify behavioral domains and neural loci of deficits stemming from exposure to MSEW. To understand how genetic diversity may contribute to the outcomes produced by MSEW, and thus inform future studies on the topic, we utilized the Jackson Laboratory Diversity Outbred (DO) line, a population derived from eight founder strains that exhibit broad genetic and phenotypic heterogeneity. We first compared MSEW effects on social behavior in DO mice versus B6 mice, because we have previously found social behavior deficits in B6 mice with a history of MSEW. Indeed, we established that MSEW incited social motivation deficits in DO mice, in a sex-specific manner. We then expanded our investigation of DO mice to test MSEW-related changes in anxiety-like behavior, fear learning and expression, and reward-seeking. Results revealed that MSEW produces distinct, sex-specific phenotypes: female DO mice displayed reduced social motivation and elevated anxiety-like behavior, while male DO mice showed attenuated CS-evoked fear expression and diminished reward-seeking behavior. Additionally, immunohistochemical analysis revealed increased Fos expression in the paraventricular nucleus of the hypothalamus (PVN) in MSEW-exposed DO mice, both at baseline and following acute stress. These findings highlight the importance of considering genetically diverse models to better capture the nuances of early life adversity-related outcomes relevant to human populations.

Stabilization of clay soils exposed to freeze-thaw conditions with waste Kevlar

In recent years, the use of waste materials for soil stabilization has gained attention due to their environmental and economic advantages. Kevlar, a synthetic, high-strength fiber commonly used in telecommunications, becomes a significant source of industrial waste at the end of its service life. In this study, the potential utilization of waste Kevlar material for improving clay soils against freeze-thaw effects was investigated using computed tomography (CT) and scanning electron microscopy (SEM) imaging techniques. For this purpose, waste Kevlar was randomly mixed into two types of clay soils (CL and CH) at different dosages (0.05%, 0.25%, 0.5% and 1%) and fixed fiber length of 10 mm. The prepared samples were subjected to 2, 5, and 10 freeze-thaw cycles, after which their stress-strain behavior, peak stress values, and freeze-thaw resistance were evaluated. The experimental results indicated that the peak stresses increased in all cycles with the increasing of waste Kevlar content. Compared to the unreinforced soil, in CH clay reinforced with 1% Kevlar, peak stresses increased by approximately 23%, 26%, 59%, and 45% for 0, 2, 5, and 10 cycles, respectively. In the case of CL clay, the corresponding increases were approximately 76%, 43%, 49%, and 44%. These findings demonstrate the feasibility and sustainability of utilizing waste Kevlar as an effective reinforcement material to enhance the durability of clay soils against freeze-thaw conditions in geotechnical engineering applications.

Indicators of high-quality general practice to achieve Quality Equity and Systems Transformation in Primary Health Care (QUEST-PHC) in Australia: a Delphi consensus study

Objectives This study aimed to achieve wider consensus on the relevance and feasibility of the Quality Equity and Systems Transformation in Primary Health Care (QUEST-PHC) indicators and measures developed for Australian general practice. Methods Partnering with eight Primary Health Networks (PHNs) across four states, we conducted a Delphi consensus study consisting of three rounds of online survey with general practice experts including general practitioners, practice nurses and PHN staff members. Participants rated each measure for relevance and feasibility, and provided input into the implementation of a quality indicator tool. Each measure required ≥70% agreement in both relevance and feasibility to achieve consensus. Aggregated ratings were statistically analysed for response rates, means, standard deviations, ranges, and level of agreement. Sub-group analyses were conducted to compare the aggregated ratings between practice and PHN staff, and between clinicians and non-clinicians in the practice staff. Qualitative responses were analysed thematically using an inductive approach. Results Ninety-four participants participated in Round 1 survey; 61 completed all three rounds. All measures reached the consensus threshold for both relevance and feasibility; 19 were slightly less feasible when compared with other measures. Although in general the participants scored similarly and their agreements were statistically significant, subgroup analyses showed that PHN staff scored feasibility of some measures slightly lower than practice staff (e.g., patients screened for adverse childhood experiences), and clinicians also scored the feasibility of some measures slightly lower than non-clinicians (e.g., patient perceptions of preventative health discussion on unsafe sexual practices). Conclusions The QUEST PHC suite of indicators and measures have reached consensus in this Delphi study. Whilst the feasibility of some measures still needs considerations, the QUEST PHC suite provides a framework for defining and measuring high-quality general practice to enable reporting to inform quality improvement and alternative funding models for Australian general practice.

Correction: Multimodal analgesia practices for knee and hip arthroplasties in the Netherlands. A prospective observational study from the PAIN OUT registry

Correction: Construction and validation of a prognostic signature based on necroptosis-related genes in hepatocellular carcinoma

ProteinWeaver: A webtool to visualize ontology-annotated protein networks

Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a protein’s position in a network in the context of a specific biological process or pathway. We developed ProteinWeaver, a web-based tool designed to visualize and analyze non-human protein interaction networks by integrating known biological functions. ProteinWeaver provides users with an intuitive interface to situate a user-specified protein in a user-provided biological context (as a Gene Ontology term) in seven model organisms. ProteinWeaver also reports the presence of physical and regulatory network motifs within the queried subnetwork and statistics about the protein’s distance to the biological process or pathway within the network. These insights can help researchers generate testable hypotheses about the protein’s potential role in the process or pathway under study. Two cell biology case studies demonstrate ProteinWeaver’s potential to generate hypotheses from the queried subnetworks. ProteinWeaver is available at https://proteinweaver.reedcompbio.org/ .

Correction: Hospital-at-home care in Singapore: A qualitative exploration of health system partners’ state of readiness, and policy and implementation strategies essential to support scale-up

Seismic random noise separation and suppression based on improved variational mode decomposition via grey wolf optimization

Seismic noise separation and suppression is an important topic in seismic signal processing to improve the quality of seismic data recorded at monitoring stations. We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements K and α in VMD. Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. It is worth to be noted that the Kurtosis comparison method can select the IMFs ensuring to preserve valid seismic signal. Finally, the denoised seismic signal is restored by the effective IMFs. The experimental results from synthetic and real field seismic data show that compared with several denoising methods, the proposed method can obtain higher signal-to-noise ratio (SNR) with increasement of 27.78% and lower root mean square error (RMSE) with improvements of 78.82% under the same level of structural similarity (SSIM) which prove the validity and effectiveness of the GWO-VMD method for both separating random noise and preserving valid seismic signal.

Frankenstein, thematic analysis and generative artificial intelligence: Quality appraisal methods and considerations for qualitative research

Objective To determine accuracy and efficiency of using generative artificial intelligence (GenAI) to undertake thematic analysis. Introduction With the increasing use of GenAI in data analysis, testing the reliability and suitability of using GenAI to conduct qualitative data analysis is needed. We propose a method for researchers to assess reliability of GenAI outputs using deidentified qualitative datasets. Methods We searched three databases (United Kingdom Data Service, Figshare, and Google Scholar) and five journals (PlosOne, Social Science and Medicine, Qualitative Inquiry, Qualitative Research, Sociology Health Review) to identify studies on health-related topics, published prior to whereby: humans undertook thematic analysis and published both their analysis in a peer-reviewed journal and the associated dataset. We prompted a closed system GenAI (Microsoft Copilot) to undertake thematic analysis of these datasets and analysed the GenAI outputs in comparison with human outputs. Measures include time (GenAI only), accuracy, overlap with human analysis, and reliability of selected data and quotes. Results Five studies were identified that met our inclusion criteria. The themes identified by human researchers and Copilot showed minimal overlap, with human researchers often using discursive thematic analyses (40%) and Copilot focusing on thematic analysis (100%). Copilot’s outputs often included fabricated quotes (58% SD = 45%) and none of the Copilot outputs provided participant spread by theme. Additionally, Copilot’s outputs primarily drew themes and quotes from the first 2-3 pages of textual data, rather than from the entire dataset. Human researchers provided broader representation and accurate quotes (79% quotes were correct, SD = 27%). Conclusions Based on these results, we cannot recommend the current version of Copilot for undertaking thematic analyses. This study raises concerns about the validity of both human-generated and GenAI-generated qualitative data analysis and reporting.

Digital health literacy is linked to attitudes regarding the ethical aspects of digital health among patients with dermatologic comorbidities

Introduction Digital health literacy (DHL), also known as eHealth literacy, refers to an individual’s ability to locate, understand, evaluate, and apply health information from electronic sources to make informed health decisions. This skill is increasingly regarded as essential for navigating the modern healthcare landscape, promoting health equity, and improving health outcomes. The study objective was to establish an association between DHL and dermatologic outpatients’ attitudes regarding ethical aspects of digital health. Additionally, we validated a questionnaire designed to assess these bioethical attitudes. Patients and methods This cross-sectional study was performed in two phases (April 2024-December 2024). Phase-1 consisted of validating the Bioethical Attitudes toward Digital Health questionnaire (BADH). Phase-2 evaluated the association between the eHEALS (it assesses a person’s ability to use digital health resources) and BADH scores. Three convenience samples of consecutive patients were used: S-1 included 46 patients who participated in a pilot testing, S-2 included 100 patients who participated in the BADH validation and S-3 included 120 patients and was used to investigate the association between DHL and bioethical attitudes. Descriptive statistics and multiple linear regression analysis were used. Results The 8-item BADH was found to be feasible, valid, and reliable. The exploratory factor analysis revealed a two-factor structure, consisting of trust and privacy dimensions, which accounted for 59.8% of the total variance. This structure was subsequently validated through confirmatory factor analysis. The BADH reliability was confirmed with a Cronbach’s alpha of 0.686 and ICC of 0.684 (95% CI: 0.581–0.770). A positive linear association was identified between the eHEALS and the BADH scores (β = 0.465, 95%CI: 0.218–0.450, p < 0.001). This relationship was evident with the trust dimension of the BADH (β = 0.526, 95%CI: 0.206–0.379, p < 0.001), but not with the privacy dimension. Conclusions DHL is associated with individual moral positions regarding digital health, particularly those concerning trust. The BADH questionnaire has adequate psychometric properties.

Terrestrial laser scanning in forestry: Accuracy and efficiency in measuring individual tree parameters

With the growing global emphasis on forest resource monitoring, evaluating the accuracy of retrieving key individual tree parameters-such as tree position, tree height, and diameter at breast height (DBH)-using Terrestrial Laser Scanning (TLS) has become an important research focus. TLS has been widely applied in forest surveys due to its significant advantages in data acquisition efficiency and measurement precision. However, studies on the accuracy of extracting forest parameters from single-station, single-scan TLS data remain limited, underscoring the need for systematic evaluation and validation. This paper analyzes the accuracy and effectiveness of TLS in extracting structural parameters (tree height and DBH) and its position using Poplar and Styphnolobium as examples by using TLS, Airborne laser Scanning (ALS), and combining with field measurements. Results show that tree height estimates from single-scan TLS is limited in accuracy: the RMSE of 11.61 m in the Populus plot and 2.13 m in the Styphnolobium plot. Within a 50 m radius, single-scan TLS achieves a tree detection rate of 55.96–64.26% and a DBH RMSE of 1.60 cm (RRMSE: 9.03%). In addition, the point root mean square error of individual tree measurements remains at 0.11 m. These findings highlight the potential of TLS as an effective tool for forest inventory and provide a basis for evaluating the reliability of TLS-based plot measurements.

Leveraging dynamic stability to infer regulation in protein-protein interaction networks: A study of infectious vulnerability in COPD

The fourth leading cause of death in the US, Chronic Obstructive Pulmonary Disease (COPD) is punctuated by frequent viral and bacterial infections causing severe acute exacerbations (AECOPD) and increased mortality. In previous work we have shown that altered immune cell signaling may confer increased and persistent susceptibility to infection. Here we continue this investigation by conducting broad-spectrum proteomic profiling of circulating white blood cells to assemble an empirical protein-protein interaction network associated with frequency of infectious exacerbation. In a novel extension of conventional cross-sectional data analyses, we translate these undirected protein-protein interactions into candidate regulatory relationships with both direction and mode of action. The latter are inferred by formulating and solving a constraint satisfaction problem (SAT) whereby predicted dynamic behaviors of any valid regulatory network must support the expected persistent nature of low and high vulnerability phenotypes. Solving this SAT problem produced a set of competing candidate protein regulatory network architectures and signalling rules that unanimously highlighted several novel candidate pathway elements involved in oxidative stress response. Analysis of the overall dynamics supported by these networks, again supported the hypothesis that progression beyond an immune tipping point may confer persistent susceptibility to infection and that this may constitute a stable phenotype or regulatory trap in COPD characterized by a reactive oxygen cascade.

Food access and associated socioeconomic factors in the Durham-Chapel Hill Metropolitan Statistical Area (MSA), North Carolina

The adversity of diet-related diseases is increased because of food insecurity . North Carolina is higher than the national average (11.7%) in food insecurity at 13.9%. The availability of healthy foods in households depends on the spatial access within the food environment where people reside or work. This study characterized the food environment, food access and associated socioeconomic factors in the Durham-Chapel Hill metropolitan statistical area (MSA) at a census block group level. Using GIS and statistical techniques, the average weighted median (AWM) was devised as measure of access for food outlets; associations between the AWM and socioeconomic variables were then investigated using multivariate regressions. For everyone in the MSA, the analysis showed lowest accessibility for fruit and vegetable markets (AWM = 2), and the highest accessibility for restaurants (AWM = 136). Relative to the White population, percentage point increase in the African American population in a block group led to a statistically significant increase in access to all categories of food outlets, with the highest increase in access of fruits and vegetable markets at 4% (p < 0.001). For every person increase in household size a decrease there was a decrease in the AWM of fruit and vegetable markets and food banks by 40%. The approach used in this study can be used in across localities measure access at a higher geographic granularity (block group level) and the associated sociodemographic factors. The results highlight disparities in food access which may require public health interventions.

An anchor-based YOLO fruit detector developed on YOLOv5

Fruit detection using the YOLO framework has fostered fruit yield prediction, fruit harvesting automation, fruit quality control, fruit supply chain efficiency, smart fruit farming, labor cost reduction, and consumer convenience. Nevertheless, the factors that affect fruit detectors, such as occlusion, illumination, target dense status, etc., including performance attributes like low accuracy, low speed, and high computation costs, still remain a significant challenge. To solve these problems, a collection of fruit images, termed the CFruit image dataset, was constructed, and the YOLOcF fruit detector was designed. The YOLOcF detector, which is an improved anchor-based YOLOv5, was compared to YOLOv5n, YOLOv7t, YOLOv8n, YOLOv9, YOLOv10n, and YOLOv11n of YOLO variants. The study findings indicate that the computation costs in terms of params and GFLOPs of YOLOcF are lower than those of other YOLO variants, except for YOLOv10n and YOLOv11n. The mAP of YOLOcF is 0.8%, 1.1%, 1.3%, 0.7%, and 0.8% more accurate than YOLOv5n, YOLOv7t, YOLOv8n, YOLOv10n, and YOLOv11n, respectively, but 1.4% less than YOLOv9t. The detection speed of YOLOcF, measured at 323 fps, exceeds that of other YOLO variants. YOLOcF is very robust and reliable compared to other YOLO variants for having the highest R2 of 0.422 value from count analysis. Thus, YOLOcF fruit detector is lightweight for easy mobile device deployment, faster for training, and robust for generalization.

DeepGAM: An interpretable deep neural network using generalized additive model for depression diagnosis: Data from the heart and soul study

Deep neural networks have achieved significant performance breakthroughs across a range of tasks. For diagnosing depression, there has been increasing attention on estimating depression status from personal medical data. However, the neural networks often act as black boxes, making it difficult to discern the individual effects of each input component. To alleviate this problem, we proposed a deep-learning-based generalized additive model called DeepGAM to improve the interpretability of depression diagnosis. We utilized the baseline cross-sectional data from the Heart and Soul Study to achieve our study’s aim. DeepGAM incorporates additive functions based on a neural network that learns to discern the positive and negative impacts of the values of individual components. The network architecture and the objective function are designed to constrain and regularize the output values for interpretability. Moreover, we used a direct-through estimator (STE) to select important features using gradient descent. The STE enables machine learning models to maintain their performance using a few features and interpretable function visualizations. DeepGAM achieved the highest AUC (0.600) and F1-score (0.387), outperforming neural networks and IGANN. The five features selected via STE performed comparably to 99 features and surpassed traditional methods such as Lasso and Boruta. Additionally, analyses highlighted DeepGAM’s interpretability and performance on public datasets. In conclusion, DeepGAM with STE demonstrated accurate and interpretable performance in predicting depression compared to existing machine learning methods.