Browse Articles

Genome-scale evolution in local populations of wild chimpanzees

Study on the evolution characteristics and influencing factors of frost heave on kaolin clay during the horizontal freezing process

Validity and reliability of the step test to estimate maximal oxygen consumption in pediatric population

Harnessing CRISPR interference to resensitize laboratory strains and clinical isolates to last resort antibiotics

AbstractThe global race against antimicrobial resistance requires novel antimicrobials that are not only effective in killing specific bacteria, but also minimize the emergence of new resistances. Recently, CRISPR/Cas-based antimicrobials were proposed to address killing specificity with encouraging results. However, the emergence of target sequence mutations triggered by Cas-cleavage was identified as an escape strategy, posing the risk of generating new antibiotic-resistance gene (ARG) variants. Here, we evaluated an antibiotic re-sensitization strategy based on CRISPR interference (CRISPRi), which inhibits gene expression without damaging target DNA. The resistance to four antibiotics, including last resort drugs, was significantly reduced by individual and multi-gene targeting of ARGs in low- to high-copy numbers in recombinant E. coli. Escaper analysis confirmed the absence of mutations in target sequence, corroborating the harmless role of CRISPRi in the selection of new resistances. E. coli clinical isolates carrying ARGs of severe clinical concern were then used to assess the robustness of CRISPRi under different growth conditions. Meropenem, colistin and cefotaxime susceptibility was successfully increased in terms of MIC (up to > 4-fold) and growth delay (up to 11 h) in a medium-dependent fashion. ARG repression also worked in a pathogenic strain grown in human urine, as a demonstration of CRISPRi-mediated re-sensitization in host-mimicking media. This study laid the foundations for further leveraging CRISPRi as antimicrobial agent or research tool to selectively repress ARGs and investigate resistance mechanisms.

Study on discrete prediction model for mechanical behavior of buried pipelines under the influence of differential frost heave

Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure

AbstractEfficient energy management and maintaining an optimal indoor climate in buildings are critical tasks in today’s world. This paper presents an innovative approach to surrogate modeling for predicting indoor air temperature (IAT) in buildings, leveraging advanced machine learning techniques. At the core of this study is the application of Long Short-Term Memory (LSTM) networks for time-series modeling, which significantly enhances the capture of temporal dependencies in temperature predictions. The proposed LSTM with RWCV (Rolling Window Cross-Validation) offers significant advantages over a usual LSTM in time-series tasks, particularly due to its ability to adapt to new data trends through the rolling window mechanism. It provides more robust and generalizable forecasts in dynamic environments, prevents overfitting through dropout and cross-validation, and improves model evaluation with temporal integrity. In contrast, traditional LSTM models are better suited for static, non-evolving datasets and may not handle dynamic time-series data effectively. To rigorously assess model performance, a comprehensive evaluation framework is developed, incorporating metrics such as mean square error (MSE) and the coefficient of determination (R²). Additionally, a novel cumulative error analysis method is introduced enabling real-time monitoring and model adjustment to maintain predictive accuracy over time. Test results demonstrate that model losses on the test dataset are only marginally higher than those on the training dataset, indicating robust generalization capabilities. Loss values range from 0.0004709 to 0.02819861, depending on building operating conditions. A comparative analysis reveals that Adaboost and Gradient Boosting models outperform linear regression, highlighting their potential for achieving energy-efficient and comfortable indoor climate management in buildings. The findings underscore the efficacy of the proposed approach for IAT prediction and point towards further research possibilities in dataset expansion and model optimization to enhance building climate management and energy conservation.

A cost-utility analysis of adding SGLT2 inhibitors for the management of type 2 diabetes with chronic kidney disease in Thailand

Investigating the relationship between built environment and urban vitality using big data

Distribution and risk assessment of microplastics in a source water reservoir, Central China

AbstractThe current researches on microplastics in different water layers of reservoirs remains limited. This study aims to investigate the microplastics in different water layers within a source water reservoir. Results revealed that the abundance of microplastics ranged from 2.07 n/L to 14.28 n/L (reservoir, water) and 3 to 7.02 n/L (river, water), while varied from 350 to 714 n/kg(dw) (reservoir, sediment) and 299 to 1360 n/kg(dw) (river, sediment). The average abundance in surface, middle, and bottom water were 6.83 n/L, 6.30 n/L, and 6.91 n/L respectively. Transparent fibrous smaller than < 0.5 mm were identified as the predominant fraction with Polypropylene and Polyethylene being the prevalent polymer types. Additionally, the pollution load index, hazard index, and pollution risk index were calculated for different layers and sediments. Results showed that surface water exhibited a moderate level of risk while the sediments posed a low level of risk. Both the middle and bottom water showed elevated levels of risk due to higher concentrations of polymers with significant toxicity indices. This study presents novel findings on the distribution of microplastics in different water layers, providing crucial data support for understanding the migration patterns of microplastics in source water reservoirs and facilitating pollution prevention efforts.

The brain prioritizes the basic level of object category abstraction

Comparison of oxidative stress status in the kidney tissue of male rats treated with paraquat and nanoparaquat

Wavelength-tunable infrared metasurfaces with chiral bound states in the continuum

Prognosis of invasive encapsulated follicular variant and classical papillary thyroid carcinoma: a propensity score-matched study using the SEER database

Effect of cobalt ions doping on morphology and electrochemical properties of hydroxyapatite coatings for biomedical applications

Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions

Structural dimensions of physical function and their associations with working memory in adults aged 60–74 years

Response of carbon storage to land use change and multi-scenario predictions in Zunyi, China

Optimizing concrete crack detection an echo state network approach with improved fish migration optimization

Impaired cardiac pumping function and increased afterload as determinants of early hemodynamic alterations in Cushing disease

Unveiling chemical industry secrets: Insights gleaned from scientific literatures that examine internal chemical corporate documents—A scoping review

Objective Examine peer-reviewed scientific articles that used internal industry documents in the chemical sector to reveal corporate influence. Summarize sources of internal documents used in prior scientific papers to identify ongoing corporate strategies within the chemical field. Compare the corporate strategies identified in the chemical sector with the ones identified already identified in the pharmaceutical sector. Propose a theoretical framework for categorizing and examining the different form of corporate capture at play. Design Performed a scoping review to pinpoint scientific papers employing internal industry/corporate documents within the chemical sector. Methods We conducted a systematic search using broad and case study-derived keywords, detailed in the S1 Appendix. This resulted in 351 sources from 28 databases, encompassing peer-reviewed articles analyzing internal documents of chemical corporations. We complemented our efforts with a snowball sampling method to uncover additional case studies and journal articles not initially captured by our search. Results were categorized and analyzed using Marc-Andre Gagnon and Sergio Sismondo’s ghost management framework. Results The final results included and analyzed 18 scientific papers. Legal proceedings served as the primary source of internal document data for all examined articles. We uncovered and categorized dynamic strategies employed by chemical corporations to protect and advance their interests, including scientific capture (n = 16), regulatory capture (n = 15), professional capture (n = 7), civil society capture (n = 6), media capture (n = 4), legal capture (n = 4), technological capture (n = 3), and market capture (n = 2). Comparative analysis The limited scientific literature meeting our criteria confirms early findings by Wieland et al, highlighting a research gap in the chemical industry. Our analysis, building on the ghost-management framework, shows a different emphasis in the way internal documents were used in scientific literature to understand corporate strategies at play in the chemical sector as compared to the pharmaceutical sector. In contrast to Gagnon and Dong’s pharmaceutical corporate capture review, which identified 37 papers before 2022, our chemical industry findings reveal a lower count, with only 18 papers identified. Notably, the vast majority of the papers in both sectors shows an emphasis on analyzing strategies used for scientific capture. However, the area of regulatory capture reveals a significant distinction: only 6 of the 37 articles related to the pharmaceutical industry analyzed this dimension, as compared to 15 of the 18 articles related to the chemical industry. This body of work suggests that existing research on the chemical industry is particularly concerned with analyzing how the sector navigates and circumvents regulatory oversight. Both industries employ strategies involving conflicts of interest and the legitimization of their actions to shield themselves from public policy scrutiny and protect their interests. However, their goals seem to be significantly different. The scientific literature analyzing the pharmaceutical industry’s internal document tends to identify strategies maximizing profits through the biased promotion of health products, whereas the scientific literature analyzing the chemical industry’s internal documents is more inclined in identifying strategies institutionalizing ignorance about existing risks, evading accountability, and preventing regulatory actions. Strengths Our scoping review shows how internal documents can reveal how the chemical industry strategically institutionalizes ignorance to manage business risks. It exposes intentional efforts by chemical corporations to promote ignorance and foster conflicts of interest, thereby legitimizing their business models and safeguarding corporate interests. We shared our research findings on the Dataverse/ Borealis platform (https://doi.org/10.5683/SP3/EOIOAU), making them accessible for future studies to apply the same analytical framework seamlessly. Limitations We excluded papers that did not meet our research criteria, prioritizing those that analyzed internal corporate documents for uncovering covert ghost management captures. Beyond scientific literature, various grey literature sources have conducted quality investigations on ghost management strategies in the chemical industry, and many leaked internal documents in the chemical industry, often available through toxicdocs.org, were not analyzed in the scientific literature. Also, market concentration and other corporate captures can be investigated using publicly available resources. Despite searching scientific papers in various languages, no relevant publications were found outside of English. This presents an opportunity for future research to conduct a separate scoping review.