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Found 6374 publications
ESG disclosure topics and reporting frameworks: exploratory research across automotive, construction, and energy industries
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Environmental, Social, and Governance (ESG) reporting and proper measurement of greenhouse gas emissions are becoming increasingly important for industries with substantial environmental impact. This research aims to assess the current state of ESG reporting practices and highlight areas for improvement across the automotive, construction and energy industries operating in the Central Eastern European (CEE) region. To achieve this aim, a multi-industry sustainability disclosure database was created and analyzed through a Python-based text-mining methodology, using term frequency-inverse document frequency and keyword-in-context analysis. The process involved extracting and preprocessing text from 60 sustainability reports for the year 2021, followed by constructing a custom dictionary of key ESG terms aligned with the European Sustainability Reporting Standards. The findings reveal considerable variance in the focus of qualitative disclosures across industries, particularly regarding climate change and biodiversity. The investigation underscores the need for enhanced transparency, consistent metrics, and rigorous validation in ESG reporting. The study also provides new insights into the technical possibilities of automated text analysis for sustainability reporting in the CEE region, and highlights key areas where improvement appears necessary.
Open Access: Yes
Bioinformatics analysis of Rickettsia typhi autoimmune associations and screening of Streptomyces-derived inhibitors
Publication Name: Biodata Mining
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Background: Rickettsia typhi is the causative agent of epidemic murine typhus and Rocky Mountain spotted fever. The infection can affect multiple vital organs, including the heart, lungs, kidneys, and brain. Doxycycline is the recommended treatment but inflammation, mal-response, and drug resistance may arise. No natural product inhibitors have been reported against this bacterium. Aim: The objective of this study was to establish a potential connection between autoimmune disorders triggered by R. typhi, identify therapeutic targets within its core proteome, and explore novel natural product inhibitors from Streptomyces spp. that could potentially inhibit it. Methodology: Complete proteomes of four publicly available R. typhi strains were used for pan-proteomic analysis. The fni gene product (Isopentenyl pyrophosphate isomerase) was selected as the potential drug target. Molecular docking of 607 Streptomyces-derived metabolites was performed, with top hits validated using DiffDock and Vinardo scoring. Additionally, the Absorption, Distribution, Metabolism, Excretion, and Toxicity properties of the leading compounds were assessed via pkCSM, and formulation characteristics optimized using FormulationAI. Results: Out of the 803 core proteins, associations between 14 proteins were mined for autoimmune diseases (including psoriasis, rheumatoid arthritis, optic atrophy, uveitis, even-plus syndrome, Sjogren syndrome, inflammatory bowel disease, allergic rhinitis, systemic lupus erythematosus, sclerosis, Stevens-Johnson syndrome, toxic epidermal necrolysis, colitis etc.). 17 core proteins were predicted as druggable. ZINC01482946 demonstrated the strongest inhibitory potential, as confirmed by DiffDock scoring, convolutional neural network-based ranking, and Vinardo scoring. It demonstrated a stable configuration and exhibited a favorable pharmacokinetic profile, with bioavailability enhanced through cyclodextrin complexation. Conclusion: To the best of our knowledge, this is the first report identifying human autoimmune associations with R. typhi and natural product inhibitors targeting the pathogen. ZINC01482946 shows potential as an effective inhibitor of R. typhi, while SBE-β-CD appears to be a promising cyclodextrin for improving its solubility and bioavailability. Clinical trial number: Not applicable.
Open Access: Yes
Biological and therapeutic implications of sex hormone-related gene clustering in testicular cancer
Publication Name: Basic and Clinical Andrology
Publication Date: 2025-12-01
Volume: 35
Issue: 1
Page Range: Unknown
Description:
Background: Gonadotropin dysregulation seems to play a potential role in the carcinogenesis of testicular germ cell tumor (TGCT). The aim of this study was to explore the expression of specific genes related to sex hormone regulation, synthesis, and metabolism in TGCT and to define specific hormonal clusters. Two publicly available databases were used for this analysis (TCGA and GSE99420). By means of hard-threshold regularized KMEANS clustering, we assigned TGCT samples into four clusters defined in respect to different expression of the sex hormone-related genes. We analysed clinical data, protein and gene expression, signaling regarding hormonal clusters. Based on whole-transcriptome gene expression, prediction of anti-cancer drug response was made by RIDGE models. Results: Cluster #1 (12–16%) consisted primarily of non-seminomatous germ cell tumor (NSGCT), characterized by high expression of PRL, GNRH1, HSD17B2 and SRD5A1. Cluster #2 (42–50%) included predominantly seminomas with high expression of SRD5A3, being highly infiltrated by T and B cells. Cluster #3 (8.3–18%) comprised of NSGCT with high expression of CGA, CYP19A1, HSD17B12, HSD17B1, SHBG. Cluster #4 (23–30%), which consisted primarily of NSGCT with a small fraction of seminomas, was outlined by increased expression of STAR, POMC, CYP11A1, CYP17A1, HSD3B2 and HSD17B3. Elevated fibroblast levels and increased extracellular matrix- and growth factor signaling-related gene signature scores were described in cluster #1 and #3. In the combined model of progression-free survival, S2/S3 tumor marker status, hormonal cluster #1 or #3 and teratoma histology, were independently associated with 25–30% increase of progression risk. Based on the increased receptor tyrosine kinase and growth factor signaling, cluster #1, #3 and #4 were predicted to be sensitive to tyrosine kinase inhibitors, FGFR inhibitors or EGFR/ERBB inhibitors. Cluster #2 and #4 were responsive to compounds interfering with DNA synthesis, cytoskeleton, cell cycle and epigenetics. Response to apoptosis modulators was predicted only for cluster #2. Conclusions: Hormonal cluster #1 or #3 is an independent prognostic factor regarding poor progression-free survival. Hormonal cluster assignment also affects the predicted drug response with cluster-dependent susceptibility to specific novel therapeutic compounds.
Open Access: Yes
Exercise Addiction: A Systematic Review of Neuroimaging Evidence
Publication Name: Current Addiction Reports
Publication Date: 2025-12-01
Volume: 12
Issue: 1
Page Range: Unknown
Description:
Purpose of Review: Exercise addiction (EA) is a maladaptive pattern of compulsive and excessive exercise that mirrors key features of behavioral and substance addictions. While psychological and behavioral characteristics of EA have been extensively studied, its neurobiological underpinnings remain underexplored. This systematic review aims to synthesize current neuroimaging evidence to identify brain structures and mechanisms implicated in EA. Recent Findings: Eight eligible studies using neuroimaging techniques were identified through a comprehensive search of five databases (PubMed, ProQuest, Web of Science, Scopus, and Google Scholar), following PRISMA guidelines. The findings indicate structural and functional differences in brain regions associated with reward processing, executive control, and emotional regulation, particularly the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), inferior frontal gyrus, and amygdala. Lower gray matter volume in the OFC was consistently linked to EA symptoms. Differences in functional connectivity within the default mode network and abnormalities in white matter tracts in frontal-subcortical circuits were also noted, resembling patterns seen in other behavioral addictions. Summary: Current neuroimaging evidence supports the view that EA shares neurobiological characteristics with other recognized addictive disorders. These findings reinforce the conceptualization of EA as a behavioral addiction. However, further longitudinal and experimental research is needed to clarify causal mechanisms and inform clinical recognition. OSF Registration: https://doi.org/10.17605/OSF.IO/9USBP.
Open Access: Yes
Nonlocal complex short pulse equation in -symmetry like symmetry breaking, breather–grammian interactions and soliton solutions
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Research on -symmetry and spontaneous symmetry breaking captivates contemporary scholars due to its extensive applicability in several fields, including microwave propagation and nonlinear optics. This article studies the nonlocal complex short pulse (NL-CSP) equation in which we discuss how under certain symmetry reduction general complex short pulse equation turns into NL-CSP equation. We construct the binary Darboux transformation for the reverse space-time NL-CSP equation and derive its quasi-grammian solutions. Further, we obtain explicit expressions for spontaneous symmetry-breaking and symmetry-preserving breather, interaction of breather with grammian and also the soliton solutions. It is concluded that the existence of both symmetry-breaking and symmetry-preserving solutions for NL-CSP equation. Finally, to verify the theoretical results, we illustrate the dynamics of these solutions using surface and contour plots.
Open Access: Yes
Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer
Dóra Mathiász
Christophe Le Tourneau
Dóra Tihanyi
Ákos Boldizsár
Júlia Déri
Maud Kamal
Dóra Lakatos
István Vályi-Nagy
László Urbán
Barbara Vodicska
Dóra Kormos
Anna Dirner
Róbert Dóczi
Viktor Kardos
Réka Szalkai-Dénes
Ákos Takács
Mária Kocsis-Steinbach
Gábor György Kalmár
Márton Bolyácz
Viktor Kardos
Christian Rolfo
Gábor Pajkos
István Peták
Richárd Schwáb
Edit Várkondi
Arkadiusz Z. Dudek
Publication Name: Npj Precision Oncology
Publication Date: 2025-12-01
Volume: 9
Issue: 1
Page Range: Unknown
Description:
Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings.
Open Access: Yes
Pre-Experimental Wet Heat Sterilization Alters the Ecotoxicity of Pristine Graphene Oxide Toward Daphnia magna
Publication Name: Nanomaterials
Publication Date: 2025-12-01
Volume: 15
Issue: 23
Page Range: Unknown
Description:
As the exposure of the aquatic ecosystem to graphene oxide (GO) increases with its growing production and use, understanding the structure–property–toxicity relationships becomes increasingly critical in the development of effective safe design guidelines. An appropriate testing methodology is crucial in ecotoxicity assessments to accurately characterize the environmentally relevant toxicity of nanoparticles, particularly for GO, where the physicochemical properties fundamentally determine their interactions and toxicity toward aquatic organisms. Many ecotoxicological methods require the heat sterilization of samples as a preliminary treatment prior to analysis. To investigate changes in toxicity profiles induced by wet heat sterilization pretreatments (autoclaving and Tyndall treatment) of a well-characterized GO product, a comprehensive ecotoxicological evaluation was performed with Daphnia magna. This included conventional lethality and immobilization tests, along with sublethal endpoints such as heart rate and feeding activity, supplemented with the analysis of oxidative stress biomarkers. Physicochemical alterations in GO due to sterilization were examined with dynamic light scattering, ultraviolet-visible, and thermogravimetry/mass spectrometry. Sublethal endpoints were shown to be more sensitive indicators of toxicity than conventional methods, with feeding activity and heart rate inhibition demonstrating time and concentration-dependent effects. Heat-sterilized GOs exhibited greater ecotoxicity compared to pristine GO, as evidenced by elevated ROS levels and increased oxidative stress biomarkers (GPx and GST activities), implicating oxidative stress as a central mechanism of toxicity. Despite the subtle differences observed in the physicochemical properties, the impact of heat sterilization on toxicity is clear. Our research underscores the critical importance of adopting appropriate testing and evaluation methodologies for comparing GO ecotoxicity results under axenic and non-axenic conditions as well as a multimarker approach to accurately evaluate the risks posed by GO.
Open Access: Yes
DOI: 10.3390/nano15231800
Predicting maize growth and biomass: Integrating gradient boosted trees with sentinel images and IoT
Publication Name: Progress in Agricultural Engineering Sciences
Publication Date: 2025-12-01
Volume: 21
Issue: 1
Page Range: 155-167
Description:
Agricultural big data and high-performance computing have significantly improved crop yield modeling. Maize growth dynamics and yield prediction are crucial for sustainable agriculture. This study introduces an advanced modeling approach utilizing Gradient Boosted Decision Trees (GBDT) combined with a feature selection strategy to predict maize biomass production. A dataset of 200 unique maize plants was observed throughout the vegetation season. Our approach integrates manual measurements, meteorological data, and vegetation indices along with Internet of Things (IoT) field sensors to perform spatio-temporal analysis. Results indicate that maize stalk thickness and height are the most reliable predictors of biomass yield, while environmental variables show minimal impact. The most effective model, period-dependent GBDT, demonstrated superior predictive performance, achieving an average error of 4.39 mm in plant growth predictions. Notably, stalk thickness and height can be estimated six weeks before harvest, while biomass yield two weeks before harvest. This research underscores the potential of machine learning and remote sensing to enhance precision agriculture decision-making.
Open Access: Yes
Phase Response Error Analysis in Dynamic Testing of Electric Drivetrains: Effects of Measurement Parameters
Publication Name: Future Transportation
Publication Date: 2025-12-01
Volume: 5
Issue: 4
Page Range: Unknown
Description:
The development of NVH (Noise, Vibration, and Harshness) characteristics in vehicles is facing new challenges with the widespread utilization of electric drivetrains. This shift introduces new requirements in several areas, such as reduced noise and vibration levels, the need for advanced nonlinear characterization methods, and tuning/masking the typically more prominent tonal noise components. More accurate simulation and measurement techniques are essential to meet these demands. This study focuses on the experimental frequency response function (FRF) testing of electric drivetrain components, specifically on potential phase errors caused by inappropriate measurement settings. The influencing parameters and their quantitative effects are analyzed theoretically and demonstrated using real measurement data. A novel numerical approach, termed Maximum Phase Error Analysis (MPEA), is introduced to systematically quantify the largest potential phase errors due to arbitrary alignment between resonance frequencies and discrete spectral lines. MPEA enhances the robustness of phase accuracy assessment, especially critical for lightly damped systems and closely spaced resonance peaks. Based on the findings, optimal testing parameters are proposed to ensure phase errors remain within a predefined limit. The results can be applied in various dynamic testing scenarios, including durability testing and rattling analysis.
Open Access: Yes
