Browse Articles

Bioinformatics analyses of comorbid mechanisms between psoriasis and type 2 diabetes mellitus

Comprehensive analysis of microRNA expression provides mechanistic insights into transcriptomic alteration in primary and metastatic testicular germ cell tumors

PACT is requisite for prostate cancer cell proliferation

Abstract PACT (encoded by the PRKRA gene) is a double-stranded RNA binding protein with defined antiviral defense and cytoplasmic RNA-induced silencing actions in mammals. We previously described a further role for PACT as a modulator of nuclear receptor (NR)-regulated gene expression. Here, we investigated the role of PACT in prostate cancer (PCa) using a loss-of-function approach. Depletion of PACT in multiple PCa cell lines resulted in a reduction in cell proliferation, but viability was maintained. RNA-sequencing analysis of LNCaP PCa cells ± PACT revealed a depletion of biological processes involved in cell cycle, mitochondrial function, and NR-response pathways in the PACT knockout (KO) cells. In the PACT KO cells, downregulated genes included the androgen-regulated KLK3 (prostate specific antigen, PSA), together with H2AFJ, PSMD5, AQP3, TMEM45B , and SLC22A3, and siRNA-mediated knockdown of these genes reduced cell growth and proliferation in LNCaP cells. Further, reducing PACT or PSA induced cell cycle arrest at G0/G1. Additionally, the hormone-mediated upregulation and AR antagonist-driven downregulation of PSA gene expression were respectively attenuated and enhanced in PACT KO cells. Taken together, these data support a pro-proliferative role for PACT in PCa, and siRNA therapeutic targeting of PACT, or downregulated genes with PACT KO, could represent a new therapeutic approach.

Modelling effective diffusion for accurate NMR pore size analysis in nano- and microporous rocks

Abstract Low-field NMR (LF-NMR) is a widely applied technique for evaluating pore size distribution (PSD) in porous materials. Conventional approaches typically assume surface-controlled spin-spin relaxation and negligible diffusion contributions under the fast-diffusion regime, which introduces systematic errors when applied to nano- and microporous systems. In this work, we present the Effective Diffusion Cubic (EDC) model, a new framework for LF-NMR-based PSD estimation in tight rocks. The EDC method incorporates pore-size dependence of both the effective diffusion coefficient and the induced internal magnetic field gradient. Crucially, the effective diffusion coefficient, D(d), is parameterized by a logistic function that faithfully approximates the Padé form, enabling a precise quantification of diffusion-related effects on T2 relaxation. Applied to nine siliciclastic core samples, the EDC approach produced PSDs corrected for diffusion-induced distortions and in closer agreement with independent reference data compared to conventional models. These results demonstrate that the EDC methodology provides a physically consistent and more accurate means of quantifying pore systems, thereby enhancing NMR-based petrophysical characterization of tight rock formations.

Emergence of human associated bacterial pathogens in Anabas testudineus reared in freshwater Biofloc systems

Identification of stigmasterol derived AChE inhibitors for Alzheimer’s disease using high throughput virtual screening and molecular dynamics simulations

Engineering and logistical concerns add practical limitations to stratospheric aerosol injection strategies

Super-resolution X-ray tomography using deep learning applied to the 3D quantification of defects in lattice structures

Abstract Lattices are intrinsically multiscale materials, forming large structures composed of repeated unit cells, while also including relatively small defects such as pores or grooves. Those defects are detrimental to their mechanical properties and must be quantified. X-ray tomography (CT), a 3D non-destructive imaging technique, is an excellent candidate for this task but it is limited by a trade-off between spatial resolution and scan time. Hence, fully imaging the lattice at a resolution providing a clear depiction of the defects of interest is not compatible with its multiscale aspect, as it would require a prohibitive amount of scan time. Recently, deep learning-based super-resolution has shown remarkable advances in improving spatial resolution of low-resolution CT. However, the validation of these super-resolution workflows is often based on visual quality metrics that do not directly assess the ability to capture critical metrics or phenomena relevant to material scientist. The present study aims at redefining image quality from a material science and task-based perspective, and to quantify the measurement uncertainties associated with super-resolution applied to the 3D characterisation of defects in lattice structures. To address this issue, we have designed a comprehensive super-resolution workflow using a mixed-scale dense network, covering data acquisition, preprocessing, and tailored algorithm validation. The method was tested experimentally on a steel lattice produced by laser powder bed fusion. Super-resolution volumes were computed and their quality was assessed from global greyscale data down to the local scale, investigating both key features of interest: porosity and surface roughness. This approach enhances image quality and improves the morphometric depiction of defects, while enabling a significant reduction in scan time, reaching several orders of magnitude. Thus, we demonstrate that defects inspection in multiscale material such as lattices is now feasible within a reasonable timeframe through deep learning-based super-resolution.

An automated framework for traffic noise level analysis using explainable artificial intelligence techniques

Molecular dynamics study of urea adsorption on nitrogen and phosphorus doped carbon nanotubes for artificial kidney devices

Experimental and thermodynamic modeling of sumatriptan solubility in supercritical carbon dioxide for green pharmaceutical applications

Abstract This study examined the solubility of sumatriptan when dissolved in supercritical carbon dioxide. The temperature of the system was varied between 308 and 338 K, and the pressure was ranged between 10 and 30 MPa. The experimental mole fraction and the solubility ranged from 4.30 × 10 −6 to 5.26 × 10 −5 and from 0.010 to 0.278 g/l respectively with a cross over point of 15 MPa. A total of four semi-empirical models (Chrastil, Bartle et al., K-J, and MST) were evaluated for their potential in facilitating both the correlation and the self-consistency of the data. The K-J method yielded the best outcome, with an AARD of 8.21% and a R adj of 0.991. These models also helped estimate thermal enthalpies ( $${\Delta H}_{t}$$ = 40.96 kJ mol −1 , $${\Delta H}_{vap}$$ = 59.83 kJ mol −1 , and $${\Delta H}_{sol}$$ = − 18.87 kJ mol −1 ). Thermodynamic modeling using the PC-SAFT, Peng-Robinson and Soave–Redlich–Kwong combined with mixing rules showed that the PC-SAFT offered better accuracy (AARD = 11.75%, R adj  = 0.988). These findings provide essential experimental data and validated predictive models that are critical for designing eco-friendly supercritical fluid processes, such as the supercritical anti-solvent.

Advanced machine learning models for predicting unconfined compressive strength from point load strength index of rock samples from Chennai and Bangalore

New rhinovirus uncoating intermediate reveals how sodium versus potassium ions influence RNA release

Abstract Electron microscopy (EM) of rhinovirus A2 (RV-A2) incubated in Na + phosphate buffer (pH 7.6) for 12 h at 25 °C revealed partial fragmentation, whereas upon incubation in K + phosphate buffer, RV-A2 appeared intact. In buffers adjusted to pH 5.8, these differences became more pronounced; acidic Na + phosphate buffer promoted disintegration of the particles, whereas in acidic K + phosphate buffer, the virus appeared like native. Incubation in the acidic buffers for one hour at 4 °C followed by neutralisation resulted in the respective formation of non-infectious A particles (in Na + ) and a non-infectious novel uncoating intermediate (in K + ), which we termed ‘E0 particle’. Negative staining EM revealed phosphotungstate penetration into A particles, but not into E0 particles. Cryo-EM image reconstruction of the E0 particle showed clear differences between A and E0 particles; like native virus, E0 contained VP4 and a pocket factor. Native RV-A2 RNA cores, obtained by gentle proteinase-K digestion in K + and Na + phosphate buffer, respectively, differed in accessibility of dsRNA regions, detected by PaSTRy. Variance in RNA compactness observed in K + versus Na + phosphate buffer was confirmed by rotary shadowing EM; in K + phosphate buffer, the RNA remained condensed while, in Na + phosphate buffer, distinct unfolding stages were apparent.

Isolation, structural characterization, and anticancer potential of a novel glycolipid biosurfactant S1B with AI-driven medium optimization

Microbial responses under sunlight-dark conditions accelerate sequestration and transformation of soil biogenic, redox and non-redox components, including As and Hg

MSDC: Aspect-level sentiment analysis model based on multi-scale dual-channel feature fusion

Aspect-level sentiment analysis is a significant task in the field of natural language processing. It can process text in a fine-grained manner to predict the sentiment polarity of a specific aspect word in a sentence. However, existing single-channel models often ignore high-dimensional local feature information in syntactic dependencies, have a single structure, and cannot fully extract text features. At the same time, there are often multiple opinion words with diverse sentiment attitudes in a sentence, so there is a certain amount of noise when processing features, which interferes with the model’s understanding of the sentiment semantics related to aspect terms. To address the problems, this paper proposes an aspect-level sentiment analysis model (MSDC) based on multi-scale dual-channel feature fusion. First, through multi-head gated self-attention channels and graph neural network channels, the model further enhances its understanding of the spatial hierarchical structure of text data and improves the expressiveness of features. Then, we design an adaptive feature fusion mechanism that dynamically adjusts the weight ratio of aspect words to context according to a given aspect. Hence, the task pays more attention to key information. Finally, the data is integrated and processed through a capsule network. The results indicate that our model exhibits superior effectiveness on multiple public datasets, especially when processing fine-grained text sentiment analysis tasks, significantly improving the accuracy and F1 value compared to existing technologies.

How does the digital economy enhance carbon emission efficiency in the logistics industry? Empirical evidence from 30 Chinese provinces

Abstract The rapid advancement of digital infrastructure has accelerated the rise of the digital economy, now a key driver of industrial transformation. This study investigates how the digital economy influences carbon emission efficiency in the logistics industry, drawing on panel data from 30 Chinese provinces between 2012 and 2021. It further examines the mediating roles of industrial structural upgrading and energy structure, as well as heterogeneity and spatial spillover effects. The digital economy was measured using the entropy method, while carbon emission efficiency was assessed with the super-SBM model, followed by benchmark regression to test inter-factor relationships. The findings reveal a nonlinear relationship between the digital economy and carbon emission efficiency. Specifically, the digital economy enhances efficiency both directly and indirectly by fostering industrial structure upgrading and optimizing energy structure. Significant regional disparities were also identified, with nonlinear spatial spillover effects on neighboring provinces. Unlike prior research, this study employs a more rigorous methodological framework with a focused analysis of the logistics industry. The results enrich academic understanding of the digital economy–carbon emission efficiency nexus and offer practical insights into leveraging digital transformation to promote sustainable and green development in the logistics industry.

Multilingual voice-enabled informatics tools: Catalyst for equitable AI in HIV and HIV-comorbidity healthcare management

Human Immunodeficiency Virus (henceforth HIV) is a global health problem, presently with no known cure. Africa has one of the highest incidences of HIV. Nigeria, within the West African (WA) region, is one of the largest economies on the continent. However, the country continues to struggle with HIV, with approximately 2 million individuals currently infected and experiencing ongoing transmissions. Management of the disease has been difficult due to communication barriers between English-speaking medical practitioners and indigenous patients in rural and suburban regions of the country and bordering countries. In this paper, we used fuzzy logic and voice-enabled technology to create WAHMIDS (West African HIV and HIV-comorbidity Multilingual Indigenous Diagnostic Software) and WAHMIMA (West African HIV Multilingual Informatics Mobile Application), which are health apps designed to help diagnose HIV and manage related health issues in both rural and urban areas for people who speak different indigenous languages in West Africa. Additionally, illustrations of the application of this tool to HIV diagnosis, using existing HIV data, are demonstrated. We expect that these tools will assist English-speaking medical workers and inhabitants of West African communities in their efforts to control HIV transmissions. These informatics tools have the potential to help prescribe medications for HIV and HIV-comorbidity patients. We anticipate that these informatics tools will help address healthcare disparities and promote diversity, equality, and inclusion by reducing the gaps in healthcare delivery between different regions and facilitating the collection of diverse patient data, which is essential for developing and planning more inclusive and accurate healthcare strategies in the West African sub-region.

Frequency of complementary medicine use and attitudes among Iranian patients with chronic diseases

Effect of vermicompost and lime on faba bean (Vicia faba L.) grain yield and soil properties on non-responsive acidic soils of Western Amhara, Ethiopia

Soil acidity is a global problem that limits crop production worldwide. It is the major crop yield-limiting factor in Ethiopia. The experiment was conducted in the Guagusa Shikudad district in western Amhara during the 2021 and 2022 cropping seasons to improve the productivity of faba bean through integrated vermicompost and lime applications. The spacing between rows and plants was 40 and 10 cm, respectively and the gross plot size was 8.4 m². The treatments were zero, half and full lime factorially combined with 0, 5, 10, and 15 t ha ⁻ ¹ vermicompost. Vermicompost and lime were applied separately in rows at planting. The experiment was laid out in a randomized complete block design with three replications. Before planting, a composite surface soil sample at 0–20 cm depth and after harvest from each plot was collected for the determination of soil chemical properties. The soil analysis result indicated that vermicompost and lime significantly increased soil pH and decreased exchangeable acidity. The result also revealed vermicompost and lime significantly (p < 0.001) increased faba bean grain and biomass yield. The maximum faba bean grain yield (2.41 t ha ⁻ ¹) was recorded from the applied 10 t ha ⁻ ¹ vermicompost and full dose of lime (5.6 t ha ⁻ ¹), while the maximum faba bean biomass (5.90 t ha ⁻ ¹) was recorded from the treatment of 15 t ha ⁻ ¹ vermicompost and full dose of lime applied. The minimum grain and biomass yield of faba bean was recorded from the control (vermicompost and lime not applied). Application of 5 t ha ⁻ ¹ vermicompost and a full dose of lime gave an optimum and economical faba bean grain yield. Application of integrated organic and inorganic fertilizers with lime is suggested for the improvement of faba bean grain yield by restoring non-responsive, strongly acidic agricultural soils in the study area and similar agroecology.