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Ciprofloxacin resistance rapidly declines in nfxB defective clinical strains of Pseudomonas aeruginosa

Severity of acute SARS-CoV-2 infection and risk of new-onset autoimmune disease: A RECOVER initiative study in nationwide U.S. cohorts

SARS-CoV-2 infection has been associated with increased autoimmune disease risk. Past studies have not aligned regarding the most prevalent autoimmune diseases after infection, however. Furthermore, the relationship between infection severity and new autoimmune disease risk has not been well examined. We used RECOVER’s electronic health record (EHR) networks, N3C, PCORnet, and PEDSnet, to estimate types and frequency of autoimmune diseases arising after SARS-CoV-2 infection and assessed how infection severity related to autoimmune disease risk. We identified patients of any age with SARS-CoV-2 infection between April 1, 2020 and April 1, 2021, and assigned them to a World Health Organization COVID-19 severity category for adults or the PEDSnet acute COVID-19 illness severity classification system for children (<age 21). We collected baseline covariates from the EHR in the year pre-index infection date and followed patients for 2 years for new autoimmune disease, defined as ≥ 2 new ICD-9, ICD-10, or SNOMED codes in the same concept set, starting >30 days after SARS-CoV-2 infection index date and occurring ≥1 day apart. We calculated overall and infection severity-stratified incidence ratesper 1000 person-years for all autoimmune diseases. With least severe COVID-19 severity as reference, survival analyses examined incident autoimmune disease risk. The most common new-onset autoimmune diseases in all networks were thyroid disease, psoriasis/psoriatic arthritis, and inflammatory bowel disease. Among adults, inflammatory arthritis was the most common, and Sjögren’s disease also had high incidence. Incident type 1 diabetes and hematological autoimmune diseases were specifically found in children. Across networks, after adjustment, patients with highest COVID-19 severity had highest risk for new autoimmune disease vs. those with least severe disease (N3C: adjusted Hazard Ratio, (aHR) 1.47 (95%CI 1.33–1.66); PCORnet aHR 1.14 (95%CI 1.02–1.26); PEDSnet: aHR 3.14 (95%CI 2.42–4.07)]. Overall, severe acute COVID-19 was most strongly associated with autoimmune disease risk in three EHR networks.

Wasserstein GAN for moving differential privacy protection

Identifying T cell antigen at the atomic level with graph convolutional network

The effects of learning experience on college students’ deep english learning: a study of the chain mediation effect of motivation and strategy

This study focuses on the impact of learning experience on college students’ deep learning of English and the chain-mediated effects of motivation and strategy. In the context of globalization, English is crucial for university students, but traditional teaching models often neglect the role of learning experience in deep learning. Deep learning emphasizes critical understanding, creative application and long-term memory construction, which is particularly important for English learning. Learning experience covers affective, cognitive and behavioral responses, and influences learning attitudes and effects, but there are fewer studies on its impact on college students’ deep learning of English and the related mechanisms. In this study, college students of different genders, ages, educational backgrounds and academic achievement levels were selected as samples, and learning experience, motivation, learning strategies and deep learning were comprehensively assessed by well-designed scales and statistically analyzed with the help of SPSS and AMOS software. The results of the study show that learning experience has a significant positive effect on English deep learning, and motivation and learning strategies play an important chain mediating role. Specifically, learning experience enhances motivation, which in turn promotes the use of learning strategies and ultimately improves English deep learning. This study validates for the first time the chain mediation model of “learning experience→learning motivation→learning strategies→deep learning”in the field of English language learning, which provides a new perspective for understanding the intrinsic mechanism of college students’ English language learning and enriches related research. In practice, it provides specific guidance for English teaching, and teachers can enhance students’ English deep learning by optimizing learning experience, stimulating learning motivation and guiding the use of learning strategies. However, there are some limitations in this study, such as the limited sample scope and the use of a cross-sectional design, etc. Future studies can expand the sample scope, adopt a longitudinal research design, and further explore other potential mediating variables.

Construction of metre-scale foldable space shelter based on gravity-driven self-assembling origami

Realizing mechanical frustration at the nanoscale using DNA origami

How does China’s green factory policy affect substantive green innovation?

Green factory evaluation (GFE) is a green industry policy in China with the goal of accelerating green manufacturing. How does GFE influence corporate substantive green innovation (SUGI)? Existing research has yet to provide definitive results. Based on GFE policy, this paper utilizes the data of China’s A-share listed manufacturing firms spanning 2012–2021 by the time-varying difference-in-differences model to investigate the impact of GFE on SUGI. The results indicate that GFE can significantly improve corporate SUGI. The mechanism results suggest that GFE promotes corporate SUGI by limiting greenwashing and enhancing industry-university-research cooperation. Additionally, we find the impact of GFE has greatly boosted corporate SUGI in industries characterized by high energy consumption and pollution levels, suggesting a potential disruption of technological path dependencies within these sectors. Moreover, firms with extensive external information disclosure exhibit stronger enhancements in SUGI under GFE. The conclusions confirm the effect and internal logic of GFE’s influence on corporate SUGI and provide a valuable advice for relevant policies aimed at improving green development.

Keratin 80 as a diagnostic biomarker with limited prognostic value in non-small cell lung cancer

Prenatal exposure to trace elements impacts mother-infant gut microbiome, metabolome and resistome during the first year of life

Influence of vehicle expertise on acceleration profile preferences in electric vehicles

This study examines how professional expertise influences acceleration profile preferences by comparing evaluations from development experts in internal combustion engine vehicles (ICEVs) and electric vehicles (EVs). Subjective evaluations were conducted under light and middle tip-in acceleration conditions, where participants assessed five distinct acceleration profiles defined by maximum jerk, acceleration gradient, and jerk kurtosis. Results indicated that ICEV experts preferred profiles emphasizing stability and smooth transitions, while EV experts showed more balanced preferences for responsiveness and smoothness. Under light tip-in acceleration, ICEV experts demonstrated strong negative correlations between subjective preference and maximum jerk (r = −0.85) and jerk kurtosis (r = −0.75), indicating aversion to sharp transient dynamics. EV experts showed weaker correlations (r = −0.26 and r = −0.63, respectively), suggesting more flexible perception of these characteristics. Under middle tip-in acceleration, EV experts displayed strong negative correlations with maximum jerk (r = −0.82) and jerk kurtosis (r = −0.89), while ICEV experts exhibited negligible or weak associations with these parameters. These findings demonstrate that professional background significantly influences acceleration profile preferences, with ICEV experts valuing traditional driving dynamics and EV experts accepting more responsive characteristics typical of electric drivetrains. The results offer practical guidelines to improve user satisfaction and facilitate a smoother transition from ICEVs to EVs by aligning vehicle drivability characteristics with different user expectations based on their professional expertise and driving experience.

Obesity and central accumulation of fat in school-age children with attention-deficit/hyperactivity disorder

Quantitative single-shot Supercontinuum-Enhanced Terahertz Spectroscopy (SETS)

Large, regionally variable shifts in diatom and dinoflagellate biomass in the North Atlantic over six decades

The North Atlantic Ocean has large seasonal blooms rich in diatoms and dinoflagellates which can contribute disproportionately relative to other primary producers to export production and transfer of resources up the food web. Here we analyze data from the Continuous Plankton Recorder to reconstruct variation in the surface ocean diatom and dinoflagellate community biomass over 6 decades across the North Atlantic. We find: 1) diatom and dinoflagellate biomass has decreased up to 2% per year throughout the North Atlantic except in the eastern and western shelf regions, and 2) there has been a 1–2% per year increase in diatom biomass relative to total diatom and dinoflagellate biomass throughout the North Atlantic, except the Arctic province, from 1960–2017. Our results confirm the widely reported relationship where diatoms are displaced by dinoflagellates as waters warm on monthly to annual time scales. The common assumption that gradual ocean warming will result in a decadal-scale shift from diatoms to dinoflagellates was not supported by our analysis. Predicting the effects of climate change likely requires consideration of the consequences for the whole community, the simultaneous change of multiple environmental variables, and the evolutionary potential of plankton populations.

Developing molecular surveillance of SARS-CoV-2 in the Czech Republic (2021–2022)

Abstract Molecular surveillance was widely used during the COVID-19 pandemic to detect rapidly emerging variants and monitor the transmission of SARS-CoV-2 within communities. In 2021, the Czech COVID-19 Genomics Consortium (COG-CZ) was set up to coordinate a new SARS-CoV-2 molecular surveillance network. In the Czech Republic, molecular surveillance employed whole genome sequencing (WGS) and variant discrimination polymerase chain reaction (VD-PCR) on samples collected through passive, active and sentinel surveillance. All WGS data was uploaded to GISAID and the PANGO lineages used by GISAID were compared to the main variants determined by VD-PCR. To assess the effectiveness and reliability of the gathered data in adapting pandemic responses, the capabilities and turnaround times of the molecular surveillance methods are evaluated. VD-PCR results were available within 48 h of sample collection for 81.5% of cases during the Delta/Omicron transition. WGS enabled the detection of low-frequency novel variants in infection clusters. WGS surveillance showed there was community spread of AY.20.1, a variant that gained novel mutations within the Czech Republic. Molecular surveillance informed the implementation of public health measures; temporal comparisons of restrictions and outcomes are described. Further areas for improvement have been identified for monitoring and managing future pandemics.

OsPRMT6b balances plant growth and high temperature stress by feedback inhibition of abscisic acid signaling

New insights into archaeological textiles (1000–1450AD) from the coastal region of the Atacama Desert: Preliminary evidence of a cochineal and shellfish purple dye combination

A multi-instrumental and non-destructive approach was used to integrally analyze four archaeological textiles from the Pre-Columbian Playa Miller-3 funerary site (1100–1450 AD) located on the coast of the Atacama Desert. The protocol included a fiber washing process with a dilute Triton X100 surfactant to remove the excess metallic components and impurities adhered to the surface of the dyed fibers. The use of animal fibers was confirmed via optical microscopy, Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). Using X-ray fluorescence (XRF) and comparing the elemental profiles of washed and unwashed textile fibers with those of soils from the archaeological site, we obtained elemental information related to potential mordants used in the dyeing process and detected the presence of bromine in some textile samples. Surface-Enhanced Raman Spectroscopy (SERS) using gold nanostructures identified the use of carminic acid and suggested a dye mixture composed of cochineal (carminic acid) and shellfish purple (dibromoindigo), which has not been previously reported for the Atacama Desert. This work provides new insights into pre-Columbian ancestral knowledge involved in the textile technology of the coastal population from this southern Andean region.

GRU controller-based UPQC compensator design for improving power quality in grid-integrated non-linear load system

Single cell profiling of human airway identifies tuft-ionocyte progenitor cells displaying cytokine-dependent differentiation bias in vitro

Pedestrian tracking method based on S-YOFEO framework in complex scene

A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. The complexity of the real-world context poses a great challenge to multi-target tracking systems. Changes due to weather or lighting conditions, as well as the presence of numerous visually similar objects, can lead to target ID switching and tracking loss, thus affecting the system’s reliability. In addition, the unpredictability of pedestrian movement increases the difficulty of maintaining consistent and accurate tracking. For the purpose of further enhancing the processing capability of small-scale features, a small target detection head is first introduced to the detection layer of YOLOv8 in this paper with the aim of collecting more detailed information by increasing the detection resolution of YOLOv8 to ensure precise and fast detection. Secondly, the Omni-Scale Network (OSNet) feature extraction network is implemented to enable accurate and efficient fusion of the extracted complex and comparable feature information, taking into account the restricted computational power of DeepSORT’s original feature extraction network. Again, addressing the limitations of traditional Kalman filtering in nonlinear motion trajectory prediction, a novel adaptive forgetting Kalman filter algorithm (FSA) is devised to enhance the precision of model prediction and the effectiveness of parameter updates to adjust to the uncertain movement speed and trajectory of pedestrians in real scenarios. Following that, an accurate and stable association matching process is obtained by substituting Efficient-Intersection over Union (EIOU) for Complete-Intersection over Union (CIOU) in DeepSORT to boost the convergence speed and matching effect during association matching. Last but not least, One-Shot Aggregation (OSA) is presented as the trajectory feature extractor to deal with the various noise interferences in complex scenes. OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. According to the trial results, S-YOFEO has made some developments as its precision can reach 78.2% and its speed can reach 56.0 frames per second (FPS), which fully meets the demand for efficient and accurate tracking in actual complex traffic environments. Through this significant increase in performance, S-YOFEO can contribute to the development of more reliable and efficient tracking systems, which will have a profound impact on a wide range of industries and promote intelligent transformation and upgrading.