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News portrayals of physicians, especially in China, often depict them as vulnerable-overworked, with inadequate compensation, or as victims of violence. These portrayals may send mixed signals to the public, yet their impact remains underexplored. Understanding their impact is essential for informing media strategies and improving physician-patient relationships. This study investigated how portrayals of physicians as vulnerable influence public evaluations of their competence, warmth, morality, and overall trust and considered the moderating effects of involvement (ie, hospital visit frequency). Four studies were conducted. Study 1 (N=492) examined the effects of daily exposure to vulnerable portrayals, and study 2 (N=710) experimentally exposed participants to vulnerable portrayals to directly investigate the causal relationship between exposure and evaluations with involvement as a hypothesized moderator. Study 3 (N=565) manipulated situational involvement using an imagination task, whereas study 4 (N=436) embedded involvement-enhancing content into news articles to improve ecological validity. Study 1 revealed that among individuals with low or moderate involvement, greater exposure to vulnerable physician portrayals in everyday life predicted more favorable overall evaluations of physicians (low involvement: B=0.11 and P=.04; moderate involvement: B=0.20 and P<.001). No significant effect was found among high-involvement individuals (P>.68 in all cases), suggesting an inverted U-shaped moderating effect of involvement. Study 2 supported this pattern-vulnerable portrayals had no significant impact among individuals with low or high involvement (t <0.49 in all cases; P>.15 in all cases) but had marginally positive effects on individuals with moderate involvement (t =1.67; P=.10; d=0.26). Notably, individuals with superhigh involvement (ie, those in hospital settings) evaluated physicians more negatively following vulnerable portrayals (t =2.49; P=.01; d=0.44). Given that nearly 80% of the general population reports low to moderate hospital visits, which is the positive moderating effect range for involvement, studies 3 and 4 targeted this group and tested whether manipulated situational involvement could enhance the effects of vulnerable portrayals. In studies 3a and 3b, participants in the high-situational involvement condition evaluated physicians more positively in the vulnerable portrayal group than in the control group (3a: t =2.71, P=.007, d=0.37; 3b: t =3.48, P<.001, d=0.93), with no effects under low-involvement conditions. Study 4 confirmed that involvement-enhancing vulnerable portrayals elicited more favorable evaluations compared to the control group (t =3.14; P=.002; d=0.37). Across all 4 studies, overall evaluation significantly predicted trust in the medical profession (B≥0.38 in all cases; P<.001 in all cases), supporting the hypothesized mediation pathway. The findings reveal a complex relationship between news portrayals of vulnerable physicians and public perceptions moderated by involvement. These results have practical implications for leveraging media to increase public trust and improve physician-patient relationships.

The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures. To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States. We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death. Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 - 2.18), Renal failure (OR:1.76; CI:1.61 - 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 - 1.67), Other bacterial diseases (OR:1.45; CI:1.31 - 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 - 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 - 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 - 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 - 1.32), Other forms of heart disease (OR:1.18; CI:1.09 - 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 - 1.27), Diabetes mellitus (OR:1.14; CI:1.03 - 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 - 1.21). We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes.

This paper proposes a novel extended object tracking (EOT) approach with embedded classification. Traditionally, for extended objects, only tracking is addressed without considering classification. This has serious defects: On the one hand, some practical EOT problems require classification as an embedded subproblem; on the other hand, with the assistance of classification, the tracking performance can be improved. Therefore, we propose a systematic EOT method with embedded classification, which is desired to satisfy the practical demands and also enjoys superior tracking performance. Specifically, we first formulate the EOT problem with embedded classification by kinematic models and attribute models. Then, we explore a random-matrix-based, multiple model EOT method with embedded classification. Two strategies are creatively provided in which soft classification and hard classification are embedded, respectively. Especially for the EOT with hard classification, a sequential probability ratio-test-based classification scheme is explored due to its nice properties and adaptability to our problem. In both methods, classification assist tracking is used. The simulation results demonstrate the superiority of the proposed EOT method with embedded classification, which can not only satisfy the practical requirements for classification but can also improve the tracking performance by utilizing the assistant of classification.

This paper proposes a novel joint decision and estimation (JDE) solution for the multi-target detection and tracking (MDT) problem. MDT aims to jointly detect the number of targets and estimate their states, which is essentially a JDE problem since detection and tracking are highly coupled. Thus, a joint solution which can utilize the coupling is preferable. However, the existing JDE approach has either poor performance or excessive design parameters without considering the MDT problem realities, i.e., the losses that different decisions may lead to. Therefore, we propose a compact conditional JDE (CCJDE)-based MDT method with less design parameters but superior performance. Specifically, we propose a CCJDE-based MDT risk which unifies the detection and tracking risks in a compact way. Then, we derive the joint detection and tracking solution accounting for their couplings, where the joint probabilistic data association filter is adopted due to its advantageous performance and the adaptability to the JDE framework. Then, an efficient CCJDE-MDT algorithm is developed. Besides, some parameter designing guidelines are presented by considering the MDT realities. Simulation results verify the effectiveness of the proposed CCJDE-MDT method, which outperforms the traditional decision-then-estimation in joint performance and also beats the existing recursive joint decision and estimation(RJDE) method in many cases.

Current techniques for visualizing and quantifying cellular forces have limitations in live cell imaging, throughput, and multi-scale analysis, which impede progress in cell force research and its practical applications. We developed a photonic crystal cellular force microscopy (PCCFM) to image vertical cell forces over a wide field of view (1.3 mm ⨯ 1.0 mm, a 10 ⨯ objective image) at high speed (about 20 frames per second) without references. The photonic crystal hydrogel substrate (PCS) converts micro-nano deformations into perceivable color changes, enabling in situ visualization and quantification of tiny vertical cell forces with high throughput. It enabled long-term, cross-scale monitoring from subcellular focal adhesions to tissue-level cell sheets and aggregates. Current techniques for visualizing cell generated forces suffer from throughput limitations. Here, Gu et al. introduced photonic crystal cellular force microscopy, inspired by chameleons, enabling visualization and quantification of vertically directed cell forces, well-suited for drug screening.

Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

This study explores the impacts of four common nonnegative media portrayals of doctors (i.e., science experts, angels in white, white-coated warriors, and vulnerable groups) on public evaluations (i.e.,stereotype content, emotional responses, and trust) and professional attractiveness (i.e.,willingness to marry or encourage child to become a doctor). Study 1 ( N  = 216) featured a between-participants design, revealing that the warrior and angel portrayals both led to more favourable ratings for warmth, competence, morality, admiration, reduced contempt, and trust than were observed in the control group. The warrior portrayal consistently received the highest scores in most dimensions, including professional attractiveness. The expert portrayal notably enhanced competence evaluations and reduced contempt, whereas the vulnerable portrayal elicited higher levels of sympathy but was associated with the lowest scores in most other dimensions. Study 2 ( N  = 320) featured a 3 (portrayal type; within-participants) × 6 (sequence; between-participants) mixed design, revealing that presentation order moderated the effects of such portrayals. The most effective sequence, angel–expert–vulnerable, elicited the highest overall evaluations, whereas the angel–vulnerable–expert sequence was least effective. These findings suggest that not only content but also sequence of portrayals can shape public attitudes towards doctors, thus highlighting relevant implications for health communication and media strategies.

Background Reverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. Although various statistical algorithms have been proposed, development of computational tools to integrate results from different methods and user-friendly online tools is still lagging. Results We developed a web server that efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance. Conclusions GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck .

This study aims to develop an artificial intelligence (AI)-based model to assist radiologists in pneumoconiosis screening and staging using chest radiographs. The model, based on chest radiographs, was developed using a training cohort and validated using an independent test cohort. Every image in the training and test datasets were labeled by experienced radiologists in a double-blinded fashion. The computational model started by segmenting the lung field into six subregions. Then, convolutional neural network classification model was used to predict the opacity level for each subregion respectively. Finally, the diagnosis for each subject (normal, stage I, II, or III pneumoconiosis) was determined by summarizing the subregion-based prediction results. For the independent test cohort, pneumoconiosis screening accuracy was 0.973, with both sensitivity and specificity greater than 0.97. The accuracy for pneumoconiosis staging was 0.927, better than that achieved by two groups of radiologists (0.87 and 0.84, respectively). This study develops a deep learning-based model for screening and staging of pneumoconiosis using man-annotated chest radiographs. The model outperformed two groups of radiologists in the accuracy of pneumoconiosis staging. This pioneer work demonstrates the feasibility and efficiency of AI-assisted radiography screening and diagnosis in occupational lung diseases.

Intrinsically disordered regions (IDRs) are common in chromatin regulators, yet how their sequence encodes regulatory logic remains unclear. Here, we show that the long linker IDR of ASXL1 (Additional Sex Combs Like 1) functions as an embedded autoregulatory module. A basic condensation‐prone segment is suppressed by a downstream acidic “charge block,” forming an electrostatic switch that gates condensation. Disease‐associated truncations remove this inhibition, unleashing phase separation and recruiting BRD2 (Bromodomain‐containing protein 2) to ectopic chromatin loci. Distinct truncation sites yield graded effects on condensate formation, chromatin accessibility, and neutrophil differentiation. Charge‐reversing mutations restore liquid‐liquid phase separation (LLPS) in a sequence‐dependent manner. Proteomic and imaging analyses identify BRD2 as a key condensate‐integrated factor whose mislocalization alters chromatin state. A compound screen reveals that Tosedostat reduces C‐terminally truncated ASXL1 (ASXL1‐TR) condensation and partially restores nuclear segmentation. Together, these findings define a tunable electrostatic switch within a long IDR and establish a broader model in which autoregulatory IDRs orchestrate condensation, chromatin engagement, and lineage fidelity. ASXL1's long IDR encodes an electrostatic “basic platform + acidic brake” that autoregulates condensation. Truncation at a clinical hotspot lifts this brake, forming condensates that retarget BRD2, remodel local chromatin accessibility, and impair neutrophil maturation. Conversely, a long truncation containing AA718–918 retains the inhibitory segment and suppresses condensation. These results illustrate how charge‐patterned IDRs govern nuclear organization and lineage fidelity.