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Effects of stand structure on ecosystem carbon stocks in woodlot plantation, riparian, and coastal forests in southeastern Bangladesh
Nitrogen fertilization and biostimulants in sugar beet (Beta vulgaris L.) production: a systematic review, meta-analysis, and nutrient management framework
Abstract Sugar beet ( Beta vulgaris L.) is a globally significant industrial crop, yet the interactive effects of nitrogen (N) fertilization and biostimulant application on yield and juice quality remain inadequately synthesized. Excessive N application suppresses sucrose content, elevates molassogenic impurities, and generates environmental externalities; however, no prior synthesis has coupled a systematic review with field-derived path analysis to jointly characterize the N × biostimulant system. The present study quantified the effects of N rate and biostimulant type on root yield, sucrose content, juice purity, and impurity concentrations through a PRISMA-compliant systematic review (68 eligible studies from 1,250 records; 50 with extractable data), random-effects meta-analysis, GRADE certainty grading, and a two-season split-plot field experiment ( n = 72; Sakha, Egypt) analyzed by structural equation modelling (SEM). N rate positively predicted root yield (GRADE: MODERATE) but negatively affected sucrose content above 120 kg N ha⁻¹ (GRADE: LOW). All biostimulant outcomes were rated GRADE VERY LOW certainty due to high heterogeneity and publication bias. Path analysis confirmed potassium as the principal quality-limiting impurity and its co-accumulation with α-amino nitrogen (α-AN) under excess N. An optimal range of 90–120 kg N ha⁻¹ is supported by MODERATE-certainty evidence for Nile Delta clay-loam conditions. Biostimulant recommendations remain provisional pending pre-registered multi-location trials.
Stochastic tri-level optimization for renewable power plant grid connection planning under uncertainty
Overcoming pitfalls in multi-stack diffusion MRI for tractography reconstruction of skeletal muscles
Abstract Accurate in vivo characterization of skeletal muscle structure is essential for understanding muscle function, assessing pathologies, and developing musculoskeletal models. Magnetic resonance imaging (MRI) and diffusion MRI enable visualization of muscle morphology and fibre architecture in vivo, but imaging skeletal muscles often requires an extended field of view. At large distances from the magnet isocentre, gradient non-linearities and magnetic field inhomogeneities can cause severe image distortions. Here, we present multi-stack structural and diffusion-tensor echo-planar images of the lower limb using selected stack lengths and acquisition parameters to provide realistic examples. Geometric distortions, fractional anisotropy maps, and tractography-reconstructed fibre tracts are used to demonstrate anatomical plausibility and consistency across overlapping slices. In the presented examples, pronounced distortions, compromised fat suppression, and fibre reconstruction errors are observed under off-isocentre imaging conditions. While shorter stack lengths may help reduce distortions, our examples also indicate that factors such as shimming strategy, participant positioning, and post-processing can influence image quality. Our work highlights key pitfalls in muscle diffusion MRI, providing practical guidance grounded in MRI physics to support the design of multi-stack MRI protocols. These considerations support more reliable multi-stack diffusion MRI for musculoskeletal research and biomechanical modelling.
Exploring imputation performance of phenotype-associated SNPs for forensic prediction models
Abstract Imputing single nucleotide polymorphisms (SNPs) to expand genotyped SNP panels is commonly applied to study genotype-phenotype correlations in medical and population genetics but is largely unexplored in forensics. Forensic DNA phenotyping, i.e., the SNP-based phenotype prediction, can greatly benefit from imputing missing prediction model SNPs. Unfortunately, most imputation studies investigate the performance of random SNPs with limited focus on phenotype-associated SNPs. Here, we compare the imputation accuracy of SNPs leveraged in phenotype prediction models, phenotype-associated SNPs and all SNPs and explore the performance of phenotype prediction model, HIrisPlex-S, using imputed datasets. We confirm that the number and selection of SNPs in the genotype dataset and minor allele frequencies (MAFs) are major drivers of imputation call and error rates. Phenotypic SNPs show higher imputation error rates than randomly selected SNPs due to MAF differences. Further, we provide novel insights into accurate phenotype prediction for dense SNP panels of high linkage, frequent phenotypes in the reference panel, and lenient imputation thresholds due to the detrimental effect of missing genotypes on trait prediction compared to imputation errors. Overall, we show the importance of imputation performance studies for SNPs of interest and the high applicability of SNP imputations prior to phenotype prediction.
Association between albumin corrected anion gap and persistent acute kidney injury in patients with sepsis-associated acute kidney injury: a retrospective study based on the MIMIC-IV database
Interference-aware optimization of three-tier RIS-enhanced hierarchical aerial computing: integrating terrestrial base stations for persistent 6G IoT coverage
Abstract The proliferation of Internet of Things (IoT) devices in emerging 6G networks demands computing architectures that simultaneously deliver high throughput, low latency, and persistent coverage across heterogeneous deployment environments. Existing two-tier unmanned aerial vehicle–high-altitude platform (UAV–HAP) frameworks offer flexible edge processing but suffer from limited battery endurance, constrained computational capacity, and susceptibility to co-channel interference (CCI) when multiple aerial platforms share the same spectrum. This paper proposes a novel three-tier RIS-enhanced hierarchical aerial computing architecture that integrates a grid-powered reconfigurable intelligent surface–equipped base station (BS-RIS) alongside four RIS-equipped UAVs and a stratospheric HAP, so as to provide persistent, interference-managed 6G IoT coverage. The proposed architecture introduces a sub-array RIS partitioning mechanism in which each RIS panel, consisting of 256 elements divided into 4 sub-arrays, dedicates one sub-array per neighboring interfering platform, achieving 85 % inter-platform interference suppression (residual fraction $$\psi ^{\textrm{sup}}=0.15$$ ). A comprehensive signal-to-interference-plus-noise ratio model is derived that captures both intra-platform CCI and inter-platform interference across all tiers. The resulting joint mixed-integer nonlinear programming problem is decomposed into three sequential stages: (i) a three-way hotspot-aware stable matching algorithm that associates IoT devices to platforms while penalising interference-heavy assignments; (ii) a sub-array-aware Riemannian conjugate gradient phase optimization that simultaneously enhances desired signal gains and suppresses inter-platform leakage; and (iii) a platform-aware hierarchical task distribution algorithm applying differentiated local-processing thresholds for battery-constrained UAVs (70 % delay margin) versus the grid-powered BS (100 % threshold). Extensive Monte Carlo simulations demonstrate that the proposed framework achieves approximately $$30\,\%$$ higher total computed data volume, $$15\,\%$$ points higher task completion rate, and $$20\,\%$$ lower average end-to-end delay compared to the two-tier UAV–HAP.
Chemical and evolutionary reasons for the rarity of natural products containing nitrogen–nitrogen bonds (NN bonds) in life’s biochemical repertoire
Abstract Understanding the chemistry of life is a central motivation of biological research. This includes understanding why life does not use particular chemical structures. Compounds containing a bond between two nitrogen atoms (NN bonds) are relatively rare in the chemicals made by life. We quantify that rarity, and explore possible reasons for it. We find that the main limitation on the use of NN compounds in biochemistry could be the toxicity of NN bond containing metabolic intermediates in their biosynthesis. The potential toxicity of the final NN compound metabolites, the energy required for their synthesis, and differences in the functional benefit from their synthesis may play additional, minor roles in explaining the NN bond rarity in biochemistry. Our study adds to a growing body of work that aims to map and explain the constraints behind life choices in chemical space.
Synergistic Upcycling of NH <sub>3</sub> and CO <sub>2</sub> Gases via an Integrated Tandem Photocatalytic System
Cross-platform software vulnerability detection using Vulnerascope-X with Word2Vec Node2Vec and RCGO optimized SVM
Abstract To increase program dependability and reduce maintenance expenses, software defect prediction is essential. For software vulnerability study across platforms, this paper presents a state-of-the-art methodology that makes use of a unique synthetic dataset called VulneraScope-X. Utilising synthetic CVE intelligence, this dataset incorporates a wealth of static, syntactic, and semantic information. This work employs an array of pre-processing methods, such as min-max normalisation, Word2Vec, Node2Vec, Synthetic Minority Oversampling Technique (SMOTE) for class balance, to deal with complexity and diversity of the features. The recently suggested Running City Game Optimiser (RCGO) outperformed state-of-the-art metaheuristics in feature selection while simultaneously lowering dimensionality and keeping predictive characteristics. With help of the features that were chosen, a Support Vector Machine (SVM) classifier was trained. The hyperparameters of this classifier were adjusted using Grid Search. Outperforming more conventional classifiers like Naive Bayes, MLP, and KNN, the model produced remarkable results with a 94.25% accuracy rate, 93.90% precision rate, 94.10% recall rate, and AUC-ROC of 0.962. In terms of accuracy and execution time, the RCGO algorithm outperformed other optimisation algorithms such as GA, GWO, CRO, and BWO. This scheme provides reproducible and scalable methodologies for software security evaluation in addition to demonstrating a high-performing pipeline for defect prediction. According to the findings, VulneraScope-X greatly improve cross-platform defect detection when combined with topological and semantic embeddings. When applied to large-scale, heterogeneous software organizations, this method demonstrates promise for vulnerability triaging and safe software development.
Retinal vessel segmentation using Kolmogorov-Arnold networks and test-time adaptation
Neural adaptation in Dyslexia: differential patterns for orthography and phonology
Abstract Dysfunction in neural adaptation to words might be at the core of reading impairment. Children with dyslexia can present difficulties recognizing similarities between repeated linguistic stimuli, as evidenced by a lack of reduced activations (adaptation) in brain regions associated with reading. fMRI Rapid Adaptation (fMRI-RA) provides a sensitive index of the quality of orthographic and phonological representations to some degree independent of the access to those representations. We used fMRI-RA to compare neural adaptation in children (10.01–14.16 y.o.) with dyslexia (DYS; n = 32) and typically reading controls (CON; n = 40). Phonological adaptation was measured in the left temporoparietal cortex (TPC) and orthographic adaptation in the left anterior ventral occipitotemporal cortex (VOT). In TPC, the Group × Condition interaction reached trend level. Planned contrasts, conducted based on a priori hypotheses, were nevertheless consistent with greater phonological selectivity in CON than DYS. Both groups showed comparable orthographic adaptation in VOT. These findings suggest a preserved neural adaptation effect for short, easy, high-frequency words in the VOT in dyslexia group. In sum, reading difficulties may reflect not only single modality deficit but also variations in how orthographic and phonological representations are integrated, highlighting the importance of approaches that examine the interaction between these processes.
A method for lung cancer detection by means of explainable convolutional neural networks
Study on the mechanical properties and consolidation mechanism of dispersive soil modified with nano-clay
Genotype-by-environment interaction analysis for flowering, maturity time and yield in fonio across traditional and prospective production areas in Northern Benin
Solanum incanum L. ethanolic extract: phytochemical profile, multiple target bioactivities and in silico properties
AKR1B15 promotes hepatocarcinogenesis by activating the p53-PI3K-AKT-mTOR-E2F1 signaling pathway
Abstract Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide, with limited biomarkers available for early diagnosis and targeted therapy. AKR1B15, a lesser-studied member of the aldo-keto reductase family, shares high sequence similarity with AKR1B10, but its role in HCC remains unclear. Therefore, this study aimed to investigate the biological function of AKR1B15 in HCC and its involvement in oncogenic signaling pathways. Bioinformatic analysis of gene expression datasets and patient tissue samples was used to evaluate AKR1B15 expression and prognostic relevance. Functional assays were conducted following AKR1B15 knockdown, including CCK-8, colony formation, transwell, wound healing, flow cytometry, and xenograft models. Western blotting and immunofluorescence were employed to assess phosphorylation of key signaling molecules. AKR1B15 expression was significantly elevated in HCC tissues and associated with higher pathologic T stage and worse disease-specific survival ( P < 0.05). AKR1B15 knockdown inhibited HCC cell proliferation, invasion, and migration ( P < 0.01), and promoted apoptosis ( P < 0.001). In vivo, AKR1B15 depletion suppressed tumor growth and reduced Ki-67 expression. Mechanistically, silencing AKR1B15 decreased phosphorylation of p53 (Ser15), PI3K (Tyr458/199), AKT (Ser473), mTOR (Ser2448), and E2F1 (S364), indicating inhibition of the p53-PI3K-AKT-mTOR-E2F1 axis. AKR1B15 promotes HCC progression and is associated with activation of the p53-PI3K-AKT-mTOR-E2F1 signaling pathway. It may serve as a novel diagnostic marker and therapeutic target in hepatocellular carcinoma.
Temperature influence on the mechanical properties of CFRP laminates evaluated by DIC
Abstract Carbon fiber reinforced polymer (CFRP) composites are widely used in aerospace, marine and automotive structures, where components may experience variable thermal conditions that affect stiffness, strength and load-bearing capacity. This study evaluates the influence of temperature on the mechanical properties of CFRP laminates manufactured from Gurit EP137 prepreg and tested using a universal testing machine supported by full-field Digital Image Correlation (ARAMIS/DIC). The complete set of tensile, compressive and shear properties was determined for 20–80 °C, whereas at − 20 °C only the in-plane shear modulus G₁₂ and shear strength FSU₄₅° were obtained because frosting/icing of the chamber window and specimen surface prevented reliable DIC image registration during selected tests. Between 20 °C and 80 °C, E₁ showed an apparent increase of approximately 8.2%, while E₂ and G₁₂ decreased by approximately 34.6% and 58.5%, respectively. The tensile strength FTU₀° decreased by 28.1%, the shear strength FSU₄₅° by 41.3%, and the compressive strengths FCU₀° and FCU₉₀° by 49.4% and 43.5%, respectively. The results confirm that matrix-dominated properties are particularly sensitive to elevated temperature and provide temperature-dependent material input for subsequent numerical modelling and stability analyses of thin-walled CFRP structures.