Browse Articles

Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research

Background Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. This study aims to identify and screen potential biomarkers for IPF diagnosis, thereby providing new diagnostic approaches. Methods We choosed datasets from the Gene Expression Omnibus (GEO) database, including samples from both IPF patients and healthy controls. For the training set, we combined two gene array datasets (GSE24206 and GSE10667) and utilized GSE32537 as the test set. We identified differentially expressed genes (DEGs) between IPF and normal tissues and determined IPF-related modules using Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, we employed two machine learning strategies to screen potential diagnostic biomarkers. Candidate biomarkers were quantitatively evaluated using Receiver Operating Characteristic (ROC) curves to identify key diagnostic genes, followed by the construction of a nomogram. Further validation of the expression of these genes through transcriptomic sequencing data from IPF and normal group animal models. Next, we conducted immune infiltration analysis, single-gene Gene Set Enrichment Analysis (GSEA), and targeted drug prediction. Finally, we created an artificial neural network model specifically for IPF. Results We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90. These genes were validated by RT-qPCR. Immune infiltration analysis revealed that specific immune cell types are closely related to IPF, suggesting that these immune cells may play a significant role in the pathogenesis of IPF. Conclusion ASPN, COMP, and GPX8 have been identified as potential diagnostic genes for IPF, and the most relevant immune cell types have been determined. Our research results propose potential biomarkers for diagnosing IPF and present new pathways for investigating its pathogenesis and devising novel therapeutic approaches.

Carbon-supported ZnO materials for sulfur capturing in supercritical water

Abstract Sulfur (S) capturing materials working at supercritical water (SCW) conditions need to be designed and developed to overcome issues related with catalyst poisoning during the hydrothermal gasification of wet biomass, an efficient and sustainable technology for alternative fuels production. Sorbent materials of zinc oxide (ZnO) deposited on porous carbon (C) support were prepared by an innovative continuous flow SCW impregnation method. Their S-adsorption performance was tested under the same supercritical conditions in the presence of sodium hydrosulfide (NaHS), as model inorganic sulfur compound. During sulfidation experiments, ZnS replaced ZnO indicating an efficient chemisorption of S with the formation of the sulfide particles by a pseudomorphic replacement mechanism. The S adsorption capacity of the ZnO/C composites reaches 1.55 mol S /mol Zn at relatively low temperature, which is much higher than those of other reported S capturing materials employed in SCW processes. The results reported here confirm that S sorbents can be both generated and used under the continuous flow SCW conditions relevant for technological applications towards the production of hydrogen and methane from biomass wastes and residues.

Genetic diversity and verification of plant material compliance of Cocoa (Theobroma cacao L.) in the Barombi-Kang Regional variety trial

Cocoa (Theobroma cacao L.) is a pivotal agricultural commodity in Cameroon, which ranks as one of the top five global cocoa producers. This study focused on evaluating the genetic diversity and verifying the plant material compliance of cocoa genotypes in the Barombi-Kang Regional variety trial, employing 12 highly polymorphic SSR markers. A comprehensive analysis of 318 hybrid families and 15 parental genotypes was conducted, which revealed extensive genetic variability. The study found an average polymorphic information content (PIC) of 0.72 for hybrids and 0.68 for parents, alongside observed heterozygosity rates of 0.54 and 0.42, respectively, indicating a rich genetic reservoir. Significantly, an 18.55% labeling error rate was identified, underscoring prevalent issues in germplasm management that could impact the efficacy of breeding programs. These errors highlight the critical need for enhanced genetic verification protocols to ensure the accuracy and reliability of plant materials used in breeding. The genetic analysis also demonstrated substantial allelic richness with the hybrids showing an average of 72 alleles per locus, suggesting a high capacity for selection within the breeding pool. The data from this study not only reinforce the potential for genetic improvement of cocoa in Cameroon but also provide crucial insights into the genetic structure and population dynamics within the trial. Addressing the genetic and management challenges identified could lead to the development of superior cocoa varieties, enhancing yield, disease resistance, and environmental stress tolerance, thereby contributing to the sustainable advancement of the cocoa industry in Cameroon and beyond.

Digital economy and entrepreneurial vitality: unveiling the impact and mechanisms through the lens of smart cities

RETRACTED: N-Beats architecture for explainable forecasting of multi-dimensional poultry data

The agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework . Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model’s robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture.

A unified probability distribution of second order difference of global streamflow

Development of a size-separation technique to isolate Caenorhabditis elegans embryos using mesh filters

The free-living nematode Caenorhabditis elegans has been routinely used to study gene functions, genetic interactions, and conserved signaling pathways. Most experiments require that the animals are synchronized to be at the same specific developmental stage. Bleach synchronization is traditionally used to obtain a population of staged embryos, but the method can have harmful effects on the embryos. The physical separation of differently sized animals is preferred but often difficult to perform because some developmental stages are the same sizes as others. Microfluidic device filters have been used as alternatives, but they are expensive and require customization to scale up the preparation of staged animals. Here, we present a protocol for the synchronization of embryos using mesh filters. Using filtration, we obtained a higher yield of embryos per plate than using the standard bleach synchronization protocol and at a scale beyond microfluidic devices. Importantly, filtration has no deleterious effects on downstream larval development assays. In conclusion, we have exploited the differences in the sizes of C. elegans developmental stages to isolate embryo cultures suitable for use in high-throughput assays.

Exploring the relationship between AI literacy, AI trust, AI dependency, and 21st century skills in preservice mathematics teachers

Imaging-based assessment of muscles and malnutrition predict prognosis in patients with primary hepatocellular carcinoma

Background The significance of imaging-based assessment of muscles and malnutrition in patients with primary hepatocellular carcinoma (HCC) remains unclear. This study aimed to elucidate the prognostic role of the combination of Low Muscle Volume and Value (LMVV) and malnutrition. Methods A total of 714 Child-Pugh grade A/ B patients with first-diagnosed HCC were enrolled, and analyzed factors associated with overall survival. LMVV was defined using psoas muscle mass index and computed tomography values of multifidus muscle at the level of the third lumbar vertebra. We used hypoalbuminemia, Child-Pugh grade B, Subjective Global Assessment (SGA) grade B/C, and Royal Free Hospital Nutrition Prioritizing Tool (RFH-NPT) score > 2 as malnutrition factors in this study. Results At baseline, 29% showed LMVV, and 59% met one or more of the malnutrition criteria. No items meeting the criteria of LMVV and malnutrition was observed in 41%, 1 of them was found in 29%, and both were found in 29%. The number of items meeting criteria was an independent factor for a shorter survival. The frequency of liver-related deaths did not differ by presence of LMVV alone, while it was associated with malnutrition. In contrast, the incidence of other types of deaths was influenced by LMVV and malnutrition. Conclusions The combination of LMVV and malnutrition is a prognostic factor in patients with primary HCC.

Cognitive and neurophysiological effects of bilateral tDCS neuromodulation in patients with minimally conscious state

First global pandemic treaty agreed — without the US

Showing ‘ability’ in ‘disability’ — how I mastered interviews while using a wheelchair

Correction: In vivo treatment with a non-aromatizable androgen rapidly alters the ovarian transcriptome of previtellogenic secondary growth coho salmon (Onchorhynchus kisutch)

Comparative efficacy and prognostic impact of continuous versus intermittent hydrocortisone administration in septic shock patients

Genomic insights into the taxonomic status and bioactive gene cluster profiling of Bacillus velezensis RVMD2 isolated from desert rock varnish in Ma’an, Jordan

Extreme environments like arid and semi-arid deserts harbor unique microbial diversity, offering rich sources of specialized microbial metabolites. This study explores Bacillus velezensis RVMD2, a strain isolated from rock varnish in the Ma’an Desert, Jordan. The genome was sequenced using the Illumina NextSeq 2000 platform, resulting in a 4,212,579 bp assembly with a GC content of 45.94%. The assembled genome comprises 112 contigs and encodes 4,250 proteins, 77 tRNA genes, and 4 rRNA genes. Phylogenetic analysis of the 16S rRNA gene indicated a 99.84% similarity to previously identified B. velezensis strains. Whole-genome phylogeny using EzBiome, MiGA, and TYGS confirmed its classification as B. velezensis. Functional annotation identified genes involved in carbohydrate metabolism, including 324 carbohydrate-active enzyme (CAZyme) genes, stress response, and secondary metabolite biosynthesis. The genome also contains 50 genes associated with heavy metal resistance and plant growth promotion. Analysis using AntiSMASH identified 12 biosynthetic gene clusters involved in the production of secondary metabolites, including fengycin, surfactin, polyketides, terpenes, and bacteriocins. Notably, several clusters did not match any known sequences, suggesting the presence of potentially novel antimicrobial compounds. The genomic features of RVMD2 highlight its adaptability to extreme environments and its potential for biotechnological applications, including bioremediation and the discovery of novel bioactive metabolites.

Relative DOA estimation method for UAV swarm based on phase difference information without fixed anchors

Roses are red — but their ancestors were yellow

Sustainability of evidence-based policy engagement model: A case study of Advance Family Planning initiative in India

Background Advance Family Planning (AFP) is a global engagement initiative to expand access to quality contraceptive information, services and supplies by fostering policy pledge and investment. Since it was launched in 2009 the initiate was implemented in ten countries. In India, this program was implemented in 42 districts across six states during 2012-2019. This paper describes the sustainability of its Engagement model and outlines programmatic strategies that facilitated the same. Materials and methods An explorative qualitative study was conducted to capture information from the representatives of district working groups and implementation partners from 26 districts across six states. The analysis was informed by the Beery’s framework for sustainability and was performed thematically using a mix of inductive and deductive approach. Results The integration of district working groups with existing health system partner forums or inter-departmental coordination platforms in selected districts, and adoption of SMART advocacy approach to various other programs and beyond the initiative geographies demonstrate the sustainability of the engagement model. The factors considered essential to its sustainability include strengthening the operational capacity of district working groups through regular monitoring, creating champions by training on SMART advocacy approach, fostering multi- and inter-sectoral partnerships and networks through collaborative platforms, and promoting accountability and ownership through open discussions. Conclusion The evidence emphasizes the continuity of the program as a system-led initiative for its sustainability. Health promoters and public health practitioners could proactively integrate sustainability components within their program design and achieve this through strategic planning throughout program implementation and the program lifecycle.

Anisotropic crack evolution and fractal failure mechanism of Lushan shale under compression: insights from acoustic emission

Changes of upper ocean disturbance caused by tropical cyclones in the Western North Pacific main development region (1993–2021)

During the passage of tropical cyclones (TCs) cause severe disturbances in the upper ocean, especially the TC main development region (MDR) (5°-23°N, 127°-160°E) in the Western North Pacific. The main attributes affecting the upper ocean disturbance were selected and weighted by the CRITIC weight method. An upper ocean disturbance (UOD) index with TCs intensity factors (MSW, Vh), ocean dynamic factors (SSC, RV, EPV) and ocean thermal factors (ΔSST, ΔMLD) was established. The change of UOD index in different time scales was analyzed by calculating each TC accumulation, monthly accumulation, and Interannual accumulation. The UOD index was closely related to the ENSO (El Niño–Southern Oscillation) climate anomaly, and their interannual correlation was as high as 0.83. Generally, the UOD index was higher in El Niño years and lower in La Niña years. In the past 10 years, the ocean thermal factors of UOD index have been increasing, especially in La Niña years. This is mainly due to the increasing SST, decreasing MLD and seawater salinity in recent years, which have changed upper ocean stratification. These results offer insights for the comprehensive analysis of the response and variation trend of the upper ocean to TC in the MDR of the Western North Pacific in recent years.