Browse Articles

Correction: The efficacy of novel biomarkers for the early detection and management of acute kidney injury: A systematic review

The bacterial ESCRT-III PspA rods thin lipid tubules and increase membrane curvature through helix α0 interactions

The phage shock protein A (PspA), a bacterial member of the endosomal sorting complexes required for transport (ESCRT)-III superfamily, forms rod-shaped helical assemblies that internalize membrane tubules. The N-terminal helix α0 of PspA (and other ESCRT-III members) has been suggested to act as a membrane anchor; the detailed mechanism, however, of how it binds to membranes and eventually triggers membrane fusion and/or fission events remains unclear. By solving a total of 15 cryoelectron microscopy (cryo-EM) structures of PspA and a truncation lacking the N-terminal helix α0 in the presence of Escherichia coli polar lipid membranes, we show in molecular detail how PspA interacts with and remodels membranes: Binding of the N-terminal helix α0 in the outer tubular membrane leaflet induces membrane curvature, supporting membrane tubulation by PspA. Detailed molecular dynamics simulations and free energy computations of interactions between the helix α0 and negatively charged membranes suggest a compensating mechanism between helix-membrane interactions and the energy contributions required for membrane bending. The energetic considerations are in line with the membrane structures observed in the cryo-EM images of tubulated membrane vesicles, fragmented vesicles inside tapered PspA rods, and shedded vesicles emerging at the thinner PspA rod ends. Our results provide insights into the molecular determinants and a potential mechanism of vesicular membrane remodeling mediated by a member of the ESCRT-III superfamily.

Spatiotemporal patterns of methane fluxes across alpine permafrost region on the Tibetan Plateau

Sustained learned immunosuppression could not prevent local allergic ear swelling in a rat model of contact hypersensitivity

Abstract Taste-immune associative learning has been shown to mimic immunopharmacological responses. Conditioned pharmacological effects may therefore be considered as controlled drug dose reduction strategy to maintain treatment efficacy. Against this background, the present study applied an established taste-immune associative learning protocol to a rat model of DNFB-induced contact hypersensitivity. After repeated pairings of a saccharin taste (conditioned stimulus, CS) with injections of the immunosuppressant cyclosporine A (CsA, unconditioned stimulus, UCS), animals were sensitized with the hapten. Retrieval started by presenting the CS together with sub-effective doses of CsA. This procedure preserved a conditioned suppression of splenic cytokine production. Compared to full dose treated animals, conditioned effects were neither observed in draining lymph nodes nor did it prevent ear swelling. These findings suggest that active sensitization might have masked a potential conditioned reduction of local allergic reactions. Additionally, symptoms such as itch may be more suited as readout parameter since it better reflects patients’ disease burden. The present study reaffirms that learned immunopharmacological effects can be preserved using a memory-updating approach. It also emphasizes the need to further explore the usability of associative learning protocols in clinical contexts in order to address disease-specific symptoms more effectively.

Physical integrity and residual bio-efficacy of PBO-pyrethroid synergist-treated and pyrethroid-only LLINs after 1.5 years of field use in Western Kenya

Background Long-lasting insecticidal nets (LLINs) are vital for malaria control in sub-Saharan Africa, but their durability is challenged by fabric decay and pyrethroid resistance. This study assessed the physical integrity and bioefficacy of piperonyl butoxide-LLINs (PBO-LLINs) and pyrethroid-only LLINs (pyrethroid-LLINs) after 1.5 years of use in western Kenya, where resistance is widespread. Methods A survey on net integrity and insecticide efficacy was conducted in randomly selected households (101–107 per group per visit) from three villages per net type group in Muhoroni Sub-County, Kisumu County. Physical integrity surveys were done after every six months while residual bio-efficacy was after every three months for 18 months. Physical integrity and residual bio-efficacy studies were conducted following WHO guidelines. Results PBO-LLINs exhibited higher physical integrity than pyrethroid-LLINs over time. At 18 months, 45.2% (61/135) of pyrethroid-LLINs and 21.8% (31/142) of PBO-LLINs were torn, with pHI values of 2494.1 ± 1696.4 and 1618.6 ± 1056.7, respectively. Net type, net age and house wall structures significantly influenced net integrity (p < 0.05). Torn nets were significantly more common in pyrethroid-LLIN households with mud-unplastered [OR=5.323 (95% CI = 1.685–16.816), p = 0.004] and corrugated iron walls [OR=6.31 (95% CI = 2.10–18.93), p < 0.001] and in PBO-LLIN households with mud-unplastered walls [OR=9.823 (95% CI = 1.487–64.898), p = 0.018]. Against the Kisumu susceptible Anopheles gambiae s.s, both net types decreased in mortality at baseline (when new) from 97.6% to 18.4% and 98.6% to 18.5% for pyrethroid and PBO-LLINs respectively at 18 months. Against a Bungoma pyrethroid-resistant Anopheles gambiae s.s, mosquito mortality with pyrethroid-LLINs declined from 36.9% when new to 6.8% at 18 months, while PBO-LLINs dropped from 55.6% to 11.8%. Conclusion Both physical integrity and bioefficacy of LLINs declined significantly within 18 months. The findings demonstrate that not all nets in the field offer maximum protection by this time point, calling for net care education and further evaluation of PBO-LLINs especially in pyrethroid-resistant regions.

The impact of temporal hydrogen regulation on hydrogen exporters and their domestic energy transition

Abstract As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. While exports can bring countries revenue, large-scale on-grid hydrogen electrolysis for export can profoundly impact domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benefits of domestic carbon dioxide mitigation and hydrogen exports, whereby exports can reduce market-based costs for domestic electricity consumers while mitigation reduces costs for hydrogen exporters. However, increasing hydrogen exports in a fossil-dominated system can substantially raise market-based costs for domestic electricity consumers, but surprisingly, temporal matching of hydrogen production can lower these costs by up to 31% with minimal impact on exporters. Here, we show that this policy instrument can steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance among actors.

Outcome prediction model for patients with unresectable hepatocellular carcinoma treated with targeted therapy

A real-time end-to-end detector for detecting surface defects on oversized rings

Oversized rings in wind turbines are regarded as crucial components because they often serve as the main load-bearing and connector structures. Surface defects on these rings can disrupt the normal operation of the entire unit. Detecting surface defects on oversized rings in wind turbine generators (WTGs) is highly challenging due to the huge ring size and small target defects, which will cause the detection process to be very time-consuming and difficult to achieve the expected accuracy. To address this challenge, we propose a new lightweight multiscale high-efficiency detector (LMHD) that balances accuracy and model size. The framework utilizes RepViT as the detection backbone and incorporates a bi-directional feature pyramid network (BiFPN) in the neck to achieve bi-directional feature transfer and aggregation. Additionally, it includes a new lightweight, efficient, multi-scale cross-stage partition module called the Diverse View Group Shuffle Cross Stage Partial Network (DVOV-GSCSPM), which employs a rational architecture and multiscale information fusion to ensure that the overall model is lightweight while maintaining a rich gradient flow. Self-Calibrated Convolutions (SCConv) and Efficient Local Attention (ELA) modules are introduced into the neck network to reduce computational complexity and the number of parameters while ensuring model accuracy. Ultimately, we incorporate the Powerful-IoUv2 loss function to enhance the rate of model convergence and generalization capabilities. The model is experimentally validated on the public dataset NEU-DET, achieving a detection accuracy of 87.0% with 70.4 frames per second (FPS).

VPS29C: Adding complexity to the Retromer complex

Improving reproducibility of differentially expressed genes in single-cell transcriptomic studies of neurodegenerative diseases through meta-analysis

Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images

Abstract In the medical field, Artificial Intelligence (AI) for diagnostic processes, particularly through deep learning techniques, has become increasingly advanced. Minor trauma, such as accidental cheek biting, sharp dental edges, or poorly fitting dentures, typically causes painful mouth ulcers and bump-like sores inside the mouth. Traditionally, diagnosing these ulcers involves a dentist or physician performing a physical examination, visually assessing the sores, and asking detailed questions about their size, location, duration, and related symptoms. Our research focuses on the advanced classification of oral ulcer stages using a convolutional neural network (CNN). To evaluate performance comprehensively, we developed and tested three custom models, comparing their effectiveness in distinguishing between different stages of oral ulcers. We also explored various optimizers and activation functions to determine the best configuration for improving model performance. Although our models show promising potential as diagnostic tools for oral ulcers, they occasionally make errors. Among the models tested, UlcerNet-2 stood out for its performance. Using the RMSprop optimizer along with Softmax and SELU activation functions, UlcerNet-2 achieved a validation accuracy of 96%. These results highlight UlcerNet-2’s exceptional effectiveness in classifying oral ulcer stages, achieving a commendable balance of high accuracy, precision, and recall. This suggests UlcerNet-2 has significant potential as an advanced diagnostic tool, possibly enhancing clinical practices in detecting and staging oral ulcers. The proposed model (UlcerNet) was implemented on FogBus, the cloud framework to empirically evaluate the model performance in cloud-fog interoperable scenarios.

Does low fertility indicate better reproductive health status? Evidence from nationally representative survey in India

Background The global demographic landscape is experiencing a significant transformation of declining fertility rates, which has far-reaching implications for societal development and women’s well-being. The study investigates the association between declining fertility rates and women’s reproductive health in India, considering socioeconomic and demographic factors as well as regional variations. Methods The study uses data from the recent National Family Health Survey (NFHS-5) round conducted during 2019−21. A composite index called the Reproductive Health Index (RHI) is constructed by equally weighing indicators such as antenatal care, anemia, and body mass index. To evaluate the robustness of this index, a sensitivity analysis is performed. Descriptive statistics and Poisson regression models are employed to explore the association between fertility and RHI among currently married women. Results The findings show substantial differences in RHI scores across socio-economic, demographic groups, and geographical regions. The lowest RHI score of 4.09 is found in the Eastern region, whereas those in the Northern region exhibit the highest score of 4.42. The analysis further indicates a negative relationship between fertility and reproductive health. Women with four or more children exhibit an RHI score of 1.97 compared to 2.98 among those with one child. The Poisson regression analysis indicates that women with at least four children have lower RHI scores, even after adjusting for socio-economic and demographic factors. Women in wealthier quintiles and those with media exposure report significantly higher RHI score compared to those in the poorest wealth quintiles and no media exposure. Conclusion In conclusion, this research highlights the critical need for targeted interventions to address regional and socio-economic inequities in healthcare access and reproductive health services. By exploring the intricate relationship between low fertility and reproductive health, this study contributes to the discourse on gender equality, reproductive rights, and sustainable societal development. The findings provide evidence to guide public health policies and programs designed to promote women’s reproductive health.

A common denominator: PTSD, rapid eye movements, and fear extinction

Adaptive spatial-temporal information processing based on in-memory attention-inspired devices

Abstract Spatial-temporal information perception is widely used for motion processing in dynamic scenes, but present technology requires relatively huge hardware resource consumption. The attention mechanism helps the human brain extract required information from tremendous data at a low cost. Here, we propose an attention-inspired artificial intelligence architecture based on hetero-dimensional modulations between zero-dimensional contact and two-dimensional electrostatic interfaces. An adaptive spatial-temporal information processing primitive is successfully implemented based on in-memory analog computing. Experiments of attention adjustments responding to different situations validate the adaptation capability to environmental changes. A demonstration of 5×5-unit data stream processing is conducted, and intensities of spatial and temporal information are varied with attention distribution from 0% to 100%. The attention-inspired device is applied to autonomous driving edge intelligence scenarios, showing high adaptability to traffic scene variations. The proposed architecture exhibits a tens-fold latency reduction, hundreds-fold area improvement, and thousands-fold energy saving compared to the conventional transistor-based circuit.

Regional zenith tropospheric delay prediction using DBO-optimized CNN-LSTM with multihead attention

Abstract Zenith Total Delay (ZTD) is integral to applications such as atmospheric water vapor inversion and precise positioning in the Global Navigation Satellite System (GNSS). The development of high-precision regional ZTD models has emerged as a significant area of research within the GNSS domain. This study addresses the challenges associated with achieving high-precision tropospheric delay predictions under specific conditions and the limitations of CNN-LSTM models, particularly regarding suboptimal hyperparameter optimization and convergence to local optima. We propose a novel regional ZTD prediction model, the CNN-LSTM-Multihead-Attention (CLMA) model, optimized using the Dung Beetle Optimization (DBO), referred to as ZTD-DBO-CLMA. This model synergistically integrates the spatial feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal sequence modeling strengths of Long Short-Term Memory (LSTM) networks, enhanced through advanced hyperparameter optimization techniques. The model facilitates synchronized learning of CNN and LSTM components via the DBO optimization algorithm and the incorporation of a multihead attention mechanism.In our study, we utilized five consecutive months of ZTD data from 40 International GNSS Service (IGS) stations within the European region, sampled at one-hour intervals, to investigate regional ZTD prediction models. We employed the ZTD-DBO-CLMA model and compared it to the ZTD-CLMA model, which lacks DBO optimization. The results indicate that the ZTD-DBO-CLMA model significantly enhances prediction accuracy, reducing the mean absolute error (MAE) and root mean square error (RMSE) by 0.31 mm and 1.38 mm, respectively, while increasing the coefficient of determination (R²) by 39.43%. Furthermore, the DBO algorithm consistently demonstrates its optimization efficacy across diverse weather conditions, thereby improving the precision of ZTD predictions.

Usability, sense of presence, and performance of a virtual reality emotion recognition task

Virtual reality (VR) has been proposed as a tool that could fulfill some of the limitations in emotion recognition (ER) assessment. Our research group created a VR task (VR-Tóol) for ER assessment. The aims of this study were to assess the usability, sense of presence, and acceptability of the VR-Tóol; to describe the performance of a VR-Tóol setup task and to examine the association between VR-Tóol performance and empathy. Forty-six healthy participants were assessed. A semi-structured interview, the Symptom Checklist-90 (SCL-90), the Cognitive and Affective Empathy Test (TECA), and the ICT Use Questionnaire (ICTUQ) were administered. Subsequently, a VR-Tóol protocol was applied. Finally, the System Usability Scale (SUS), Igroup Presence Questionnaire (IPQ), and a Motivation Questionnaire were administered. The SUS total score showed acceptable parameters; the IPQ mean scores reached marginally acceptable scores in all subscales, excepting for the Involvement subscale, which achieved non-acceptable parameters. The SSQ indicated the presence of negligible symptoms related to the VR exposure. Regarding performance, participants achieved 83.3% hits. A substantial accuracy rate was met in contempt, admiration, happiness, surprise, and compassion. Significant positive correlations were observed between VR-Tóol hits and TECA’s Perspective taking (PT) score (Spearman’s rho = 0.500, p < .001) and Total score (Spearman’s rho = 0.398, p = .007). In conclusion, VR-Tóol exhibits favorable usability and sense of presence properties, negligible cybersickness symptoms, and an overall positive experience. We found evidence of the relationship between ER and empathy. This work elucidates the potential utility of VR-Tóol for ER assessment.

Infrared spectroscopy–based zero-shot learning for identifying reaction intermediates in unseen systems

Identifying reaction intermediates is a critical component of elucidating the mechanisms of chemical reactions. Spectroscopic techniques are instrumental in this identification process. The capacity of AI to establish the correlations between spectra and chemical substances renders it a suitable tool for spectra analysis. However, the presence of limited data, or even the absence of data, is a common occurrence for intermediates with transient characteristics. This poses a significant challenge in meeting the data requirements of AI models. Herein, we propose a generalizable machine learning model that utilizes zero-shot learning to identify chemical reaction intermediates by their spectra in unseen catalytic systems. Using SHapley Additive exPlanations (SHAP) analysis and visual dimensionality reduction, it was determined that the model’s superior generalizability is attributable to its capacity of learning common patterns of spectra-intermediates, effectively mapping disparate catalytic systems to analogous digital spaces. Therefore, the model can be directly used without fine-tuning parameters for unseen systems even in the presence of noise or solvents. This work demonstrates the application of zero-shot learning in machine learned spectroscopy and illustrates the prediction mechanism, providing a perspective for interpretable and robust cross-system prediction. It lays a solid foundation for the application of spectral descriptors in real reaction systems and the comprehension of chemical reaction mechanisms.

Self-assembled metal clustersomes and chirality transfer to colloidal photonic crystals

Pyrazolopyridine pyrimidone hybrids as potential DprE1 inhibitors, design, synthesis and biological evaluation as antitubercular agents

Mining and quantitative evaluation of the laboratory biosafety policy in China

Policies play a pivotal role in guiding and overseeing laboratory biosafety management. To ensure that laboratory biosafety management is underpinned by scientifically robust and well-founded policies, an analysis and evaluation of existing policies were conducted. The object was to identify their merits and limitations, thereby offering references for future policy development. The qualitative and quantitative analysis were employed to explore 137 central-level policies issued in China as of April 30, 2024. Additionally, based on policy evaluation theory, a PMC index model was established to evaluate 11 representative laboratory biosafety policies. The results showed that: Firstly, these policies, promulgated by 24 distinct departments, spanned three regulatory tiers: laws, regulations, and administrative rules. Secondly, content analysis revealed three primary aspects: (1) management systems, (2) facility, equipment and containment barrier, and (3) operational technical standards. Thirdly, the average PMC index of the 11 policies was 5.05. Specifically, two policies were deemed excellent, eight policies were acceptable and one was inadequate. The low score was mainly attributed to three indicators: policy level, policy timeliness, and policy content. To sum up, laboratory biosafety policies in China were generally rational and comprehensive. However, insufficient collaboration among departments during policy formulation, as well as the need to improve policy continuity were identified. To enhance biosafety laboratory management, four recommendations are proposed: 1. Strengthen communication among different departments; 2.Optimize the policy formulation process; 3. Enhance supervision of biosafety level 1 and 2 (BSL-1/2) laboratories; 4. Harnessing the power of industry associations.