Browse Articles

Synergistic effects of basalt fiber and volcanic pumice powder in high-strength geopolymer concrete

Improved overall survival in an anti-PD-L1 treated cohort of newly diagnosed glioblastoma patients is associated with distinct immune, mutation, and gut microbiome features: a single arm prospective phase I/II trial

Metagenomics reveals contrasted responses of microbial communities to wheat straw amendment in cropland and grassland soils

Abstract Soil microbial communities respond quickly to natural and/or anthropic-induced changes in environmental conditions. Metagenomics allows studying taxa that are often overlooked in microbiota studies, such as protists or viruses. Here, we employed metagenomics to characterise microbial successions after wheat straw input in a 4-month in-situ field study. We compared microbial successions patterns with those obtained by high throughput amplicon sequencing on the same soil samples to validate metagenomics as a tool for the fine analysis of microbial population dynamics in situ. Taxonomic patterns were concordant between the two methodologies but metagenomics allowed studying all the microbial groups simultaneously. Notably, our results evidenced that each domain displayed a specific dynamic pattern after wheat straw amendment. For instance, viral sequences multiplied in the early phase of straw decomposition, in parallel to copiotrophic bacteria, suggesting a “kill-the-winner” pattern that, to our knowledge, had not been observed before in soil. Altogether, our results highlighted that both inter and intra-domain trophic interactions were impacted by wheat amendment and these patterns depended on the land use history. Our study highlights that top-down regulation by microbial predators or viruses might play a key role in soil microbiota dynamics and structure.

Trends in medication use during the COVID-19 pandemic in Quebec, Canada

A surprise induced by a visual-haptic illusion in virtual reality can lead to motor improvement

Social vulnerability index enhances FRAX prediction of hip fractures in fall patients

Direct brain recordings reveal implicit encoding of structure in random auditory streams

Abstract The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.

Alcohol-induced gut permeability defect through dysbiosis and enterocytic mitochondrial interference causing pro-inflammatory macrophages in a dose dependent manner

A comparison of statistical methods for deriving occupancy estimates from machine learning outputs

Abstract The combination of autonomous recording units (ARUs) and machine learning enables scalable biodiversity monitoring. These data are often analysed using occupancy models, yet methods for integrating machine learning outputs with these models are rarely compared. Using the Yucatán black howler monkey as a case study, we evaluated four approaches for integrating ARU data and machine learning outputs into occupancy models: (i) standard occupancy models with verified data, and false-positive occupancy models using (ii) presence-absence data, (iii) counts of detections, and (iv) continuous classifier scores. We assessed estimator accuracy and the effects of decision threshold, temporal subsampling, and verification strategies. We found that classifier-guided listening with a standard occupancy model provided an accurate estimate with minimal verification effort. The false-positive models yielded similarly accurate estimates under specific conditions, but were sensitive to subjective choices including decision threshold. The inability to determine stable parameter choices a priori, coupled with the increased computational complexity of several models (i.e. the detection-count and continuous-score models), limits the practical application of false-positive models. In the case of a high-performance classifier and a readily detectable species, classifier-guided listening paired with a standard occupancy model provides a practical and efficient approach for accurately estimating occupancy.

Failure of multistorey parking site in hilly region and its forensic geotechnical investigation

Disproportionality analysis of European safety reports on autoimmune and rheumatic diseases following COVID-19 vaccination

Abstract The safety profile of COVID-19 vaccines is well-established, yet the widespread immunization campaign has led to an increase in reported cases of Immune-Mediated and Rheumatic Diseases (IMDRs). This study aimed to assess the reporting of Adverse Events Following Immunization (AEFIs) related to IMDRs after COVID-19 vaccination. We analyzed all individual case safety reports (ICSRs) related to COVID-19 vaccines authorized in the European Union (i.e., tozinameran, elasomeran, ChAdOx1-S NCoV-19, and Ad26.Cov2.S) registered in the EudraVigilance (EV) database from January 1, 2021, to October 23, 2023. Our analysis identified ICSRs with events indicative of IMDRs and conducted disproportionality analysis (i.e., Reporting Odds Ratio (ROR) with 95% CI) to examine the frequency of different IMDR types linked to each vaccine. In total, 45,352 ICSRs reported at least one AEFI associated with rheumatic or autoimmune conditions, with 54% of them implicating tozinameran as the suspected vaccine. More than half of the reported AEFIs were classified as serious, with approximately 45% remaining unresolved. The most frequently reported conditions were other immune-mediated diseases, followed by arthritis, vasculitis, systemic lupus erythematosus, and tendinopathies. Our disproportionality analysis suggested that mRNA vaccines may be more frequently associated with new autoimmune rheumatic diseases. Stratified analysis revealed significant associations for ChAd, particularly in vasculitis and tendinopathies, only when compared to Ad26.Cov2.S. Real-world pharmacovigilance data suggest that autoimmune and rheumatic diseases may be under-reported following COVID-19 vaccination, highlighting the need for further research to better understand the underlying mechanisms. The findings from this disproportionality analysis suggest the need for further studies to investigate these results in greater depth.

Towards developing an operational Indian ocean dipole warning system for Southeast Asia

Abstract Two strong positive Indian Ocean Dipole (IOD) events in 2019 and 2023 led to multiple disasters over Southeast Asia, highlighting the need for warnings of IOD events. This paper presents a stock-take of the current criteria for IOD monitoring and prediction and describes the development of an IOD warning system for Southeast Asia. We examined how subjective choices such as observational datasets, baseline periods, and time averaging affect IOD event identification. Our findings indicate that the choice of sea-surface temperature dataset and time averaging (monthly vs. 3-monthly mean) lead to marked differences in the Dipole Mode Index (DMI), the index used for the monitoring and prediction of IOD events, and hence between various centers on IOD state. The southern Maritime Continent can experience the impact of the IOD on rainfall even when the IOD has not met the current operational criterion, suggesting a need for an impact-based threshold for the IOD. We assess the skill of models in capturing the strength and phase of the IOD and report errors in IOD predictions. While most models are skillful in capturing the active phase of the IOD, many models have an overactive IOD strength. Calibration of DMI-based monitoring products is therefore recommended for the most skilful IOD predictions. Finally, we describe an objective standard operating procedure to assist climate forecasters in issuing timely alerts of IOD events.

Renewable energy forecasting using optimized quantum temporal model based on Ninja optimization algorithm

Abstract Artificial intelligence allows improvements in renewable energy systems by increasing efficiency while enhancing reliability and reducing costs. Renewable energy forecasting receives substantial improvement by applying deep learning methods as one of its promising approaches. The research utilizes QTM with NiOA optimization for achieving maximum forecasting performance. NiOA functions through critical optimization processes when enhancing deep learning models with high accuracy for large complex datasets by selecting the most appropriate features. Fundamental data preparation steps, including normalization scaling, and gap handling, play a vital role before using input data for reliable renewable energy forecasting operations. Using the Ninja binary optimization engine produces superior results than all tested binary algorithms, including SBO, bSCA, bFA, bGA, bFEP, bGSA, bDE, bTSH and bBA, resulting in enhanced classification accuracy. The superior capability of bNinja to choose optimal features establishes its usefulness for renewable energy forecasting applications. Experimental implementation revealed that incorporating the Ninja Optimization Algorithm with the QTM model delivered the best R 2 performance at 95.15% with an exceptional RMSE value of 0.00003, thus establishing its ability to optimize renewable energy forecasting accuracy.

Measurement of eco-efficiency in the horse industry, spatiotemporal evolution and convergence analysis

Scale validation and prediction of environmental health literacy in Brazil

Effects of increasing the dietary contents of metabolizable energy and protein during the peripartum period on mammary gland development in Sistani goats

Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI

Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes

Research on the impact of borehole parameters on the instability and precursor characteristics of large diameter borehole in coal seam

Assessing the contribution of wind and water erosion in the agro-pastoral ecotone of Northern China with 137Cs tracer technology

Abstract This study addresses the critical ecological challenges of soil wind and water erosion in the agro-pastoral ecotone of northern China, both of which significantly contribute to soil degradation. Understanding the relative contributions of these erosion types is essential for developing effective control measures. Using the 136 Cs tracer method, we quantified the ratio of soil wind erosion to water erosion under varying topographic and geomorphic conditions. The results revealed that cropland has experienced the most severe erosion in recent decades. Specifically, on gentle slopes (6°–8°), the rate of water erosion exceeded wind erosion by approximately eightfold. On steeper slopes (10°–15°), this trend was even more pronounced, with water erosion surpassing wind erosion by a factor of approximately 27. These findings were corroborated by measured data from a previous study area. Overall, water erosion is the dominant process in the agro-pastoral ecotone of northern China, with wind erosion playing a secondary role. Future erosion prevention strategies should prioritize hydraulic erosion control measures, particularly on sloping cropland. Furthermore, advancing research on the compound mechanisms of wind and water erosion is imperative for developing integrated mitigation strategies, ultimately supporting the sustainable development of the region’s ecological environment.