Search Results (3,736)

Neuroendocrine and circadian dysfunctions are thought to underlie bipolar disorder (BD) and could be implemented as markers of complex symptom conditions, including the presence of Post-Traumatic Stress Disorder (PTSD) comorbidity. The primary objective of this study was to investigate neuroendocrine profiles in different BD phenotypes through the measure of nocturnal urinary levels of Cortisol (stress-response factor) and 6-sulfatoxymelatonin (aMT6s; light/dark signal) in euthymic BD patients diagnosed with PTSD (PTSD group) or with major depressive episodes without trauma symptoms (DEP group). Both groups were compared against healthy controls (CTL). Nighttime urinary levels of aMT6s and Cortisol were analyzed by competitive ELISA, with ensuing values normalized for specific gravity. The Cortisol-to-aMT6s ratio (Cort/aMT6s) was calculated as an exploratory proxy for the homeostatic balance between HPA-mediated catabolic/coping activities and Melatonin-related reparative functions. Clinical severity and psychosocial activities were assessed by using mood HAM-D, YMRS, trauma IES-R and functioning WSAS scales. Results revealed distinct biological patterns: PTSD subjects showed increased nocturnal Cortisol compared to DEP and CTL groups, whereas DEP patients exhibited lower aMT6s levels and an elevated Cort/aMT6s ratio. Cortisol and aMT6s correlated positively in PTSD, while the ratio correlated positively with Cortisol solely in controls. Clinically, Cortisol correlated positively with IES-R and functional impairment (WSAS), whereas aMT6s correlated negatively with HAM-D and YMRS scores. Present findings suggest a possible neuroendocrine divergence in BD patients with depression versus PTSD: nocturnal Melatonin deficiency may be associated with depression, while HPA-axis hyperactivity and altered crosstalk between the two systems may characterize PTSD comorbidity, with the Cort/aMT6s ratio as a potential index of impaired daily functioning. These results could be promising for the use of biomarkers linked to allostatic and circadian responses within the BD spectrum. Full article

Segmenting brain tumor subregions in multimodal MRI is difficult due to severe class imbalance and scarce annotated data, and current state-of-the-art models require 21.3–31 million parameters to reach a whole-tumor Dice of 0.906–0.921. We propose a parameter-efficient 2D encoder–decoder coupling a Fourier Neural Operator (FNO) and a Vision Transformer (ViT), trained with BraTSPipeline, which raises throughput from 257 to 16,040 slices per epoch, and two composite loss functions penalizing false negatives 2.3× more than false positives. On BraTS 2020, the 2D variant (5.6 M parameters) achieves whole-tumor (WT), tumor-core (TC), and enhancing-tumor (ET) Dice of 0.8985, 0.8263, and 0.7568 on the 56-case held-out test set, using 3.8–5.6× fewer parameters than published architectures. Encoding three consecutive axial slices as 12 channels (2.5D, 20.5 M parameters) raises WT to 0.9117, within 0.010 of the best published 2D result on BraTS 2020, Mod-R2AU-Net (WT = 0.921); a 4.6 M-parameter ablation without the ViT reaches WT of 0.9106 and TC of 0.8428, while the ViT adds 0.043 ET Dice. Full article

For image data, matrix-based clustering methods are gaining popularity because they can directly process two-dimensional (2D) data structures without vectorization. However, most existing approaches rely on squared Frobenius norms in their objective functions, making them sensitive to outliers and noise commonly encountered in real-world images. To overcome this limitation, we propose a novel robust ${\mathsf{\ell }}_p$-norm two-dimensional discriminative clustering method (R2DDC) specifically designed for image clustering. R2DDC utilizes the ${\mathsf{\ell }}_p$-norm to simultaneously minimize within-cluster distances and maximize between-cluster distances at the matrix level, thus preserving the inherent 2D spatial information of images while providing enhanced robustness against pixel-level outliers and noise. An efficient iterative optimization algorithm is designed to solve the proposed objective function. Extensive experiments on contaminated face image datasets demonstrate that R2DDC consistently surpasses conventional clustering approaches, highlighting its effectiveness for robust image analysis. Full article

Surface macrotexture is of major interest for barrier-free routing, particularly for wheelchair travellers, because it relates to the functional properties of pavements, such as loss of energy through tyre-rolling resistance or vibrational discomfort. These functional properties are difficult to assess. The measurement and characterization of pavement macrotexture, therefore, is a promising approach to support safe route choice for wheelchair travellers and to provide comparative quality criteria for inclusive urban infrastructure management and planning. We use 3D point clouds from terrestrial laser scanning (TLS) and suggest a set of surface roughness parameters tailored to assess the accessibility of urban pavements at the micro-level. We explore the sensitivity of the parameters to point cloud resampling and apply them to a real-world dataset with eleven different pavements. In spite of value variation related to scanning $distance$ and $coverage$, our results indicate that combining median-based versions of Average Roughness and Simulated Texture Depth together with parameters tailored to quantify the depth and proportion of joints and the roughness of the contact area facilitates the grouping and comparison of surfaces regarding barrier-free mobility. The results of this work will be integrated into a framework for accessibility analysis and barrier-free routing. Full article

The goal of this project is to identify ways to improve the performance of the bushing–flange–circlip connection on the V-arm suspension components of heavy-duty commercial vehicles. Failures related to circlip ejection and flange loosening identified from customer relationship management (CRM) data motivated the development of this computational optimization framework. This study involved generating a parametric dataset, using Latin Hypercube Sampling (LHS) with synthetic data, for the development of the artificial neural networks (ANNs). The networks use the surrogate model to optimize solutions through a combination of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The ANN provided an overall test set of R² = 0.968 across the three objective functions. The hybrid optimization method produced 11 surrogate-predicted Pareto-optimal candidate designs, simultaneously minimizing the micro-displacement and stiffness loss while maximizing the fatigue life. The present results are based on analytically derived synthetic training data. Simcenter 3D Version 2506 Finite Element Analysis (FEA) integration and prototype validation constitute the planned next phase. The methods described here can be configured for other components of the suspension and are expected to be compatible with similar applications depending on future enhancements. Full article

Parkinson’s disease (PD) involves progressive basal ganglia dysfunction, with hyperexcitability of striatal indirect-pathway D2 medium spiny neurons (D2-MSNs) linked to motor impairment. Neuromotor rehabilitation is an important therapy, but its efficacy varies across interventions. This thematic review examines eleven rehabilitative strategies for PD, spanning forced and voluntary exercise (FE and VE respectively), non-invasive brain stimulation (rTMS, tDCS), and technology-based approaches such as exoskeletons, augmented reality, and dual-task training, within a framework distinguishing striatal recalibration from compensation via alternative motor networks. To formalize this distinction, a Bayesian ranking framework combines neurobiological plausibility with clinical evidence quality, identifying three functional clusters: a high-recalibration cluster (FE, p ≈ 0.80; LSVT BIG, p ≈ 0.62), an intermediate-uncertainty cluster (rTMS, HIIT, tDCS, treadmill, resistance training, Tai Chi/dance; 0.40–0.56), and a bypass/compensatory cluster (augmented reality, exoskeletons, dual-task training; p ≤ 0.33). This distinction between direct modulation of basal ganglia circuitry and recruitment of alternative motor networks, including the lateral premotor cortex, parieto-premotor circuits, and cerebello-thalamo-cortical pathways, supports a precision rehabilitation approach in PD. Full article

Pre-packaged fermented soybean whey-based tofu (FSW-tofu) was stored under dynamic temperature conditions (4–20 °C) that simulated typical supermarket and e-commerce cold-chain transport modes. Changes in total viable count (TVC), psychrophilic bacterial count (PBC), hardness, springiness, chewiness, and water-holding capacity were monitored over 35 d, and a hybrid prediction model integrating mechanistic kinetics with machine learning was established. Results indicated that both temperature fluctuation amplitude and frequency significantly affected microbial proliferation and textural degradation. Under the e-commerce mode, exposure to 20 °C accelerated the TVC, reaching 5 lg CFU/g at 17 d, earlier than under the supermarket mode (27 d). However, the sustained low-temperature stress in the supermarket mode caused more profound degradation of the protein gel network, leading to more severe textural deterioration at the equivalent TVC threshold. The Baranyi–Roberts–Ratkowsky non-isothermal growth model and quality response functions served as the base framework, while random forest and gradient boosting trees were used for residual correction, yielding a coupled mechanistic-data-driven model. Independent validation yielded R² > 0.89 and relatively low RMSE, confirming the model’s good generalization and predictive accuracy. This approach combines mechanistic interpretability with machine learning accuracy to provide a rapid assessment tool for the cold-chain quality management of FSW-tofu. Full article

Background: Endometriosis-associated ovarian cancers (EAOCs), including ovarian clear cell (OCCC) and endometrioid ovarian carcinoma (EnOC) subtypes, frequently exhibit transcriptomic dysregulation of the PI3K/AKT/mTOR signalling axis. While genomic aberrations are well-documented, the coordinated microRNA (miRNA)-mediated networks governing post-transcriptional remodelling of this pathway across these subtypes remain poorly defined. Methods: We conducted an integrative in silico meta-analysis of independent mRNA and small RNA sequencing datasets. The mRNA analysis included 120 EAOC samples, of which 68 were OCCC and 52 were EnOC, compared with 149 normal ovarian tissues. The miRNA analysis included 170 samples comprising 55 OCCC, 82 EnOC and 33 normal ovarian tissues. Differential expression analysis, dimensionality reduction (UMAP), functional enrichment, and topologically unweighted miRNA–mRNA interaction networks were evaluated. Results: Both subtypes showed significant transcriptomic dysregulation of the core pathway machinery, including PIK3CB and mTOR, while preserving mTORC2 components. Post-transcriptional concurrent downregulation of IRS1, GRB10, DDIT4, and PIK3CD, which were identified as candidate targets of the hub miRNAs hsa-miR-30a-5p, hsa-miR-30d-5p, and hsa-miR-7-5p, suggests further refined control of the pathway. The identification of highly connected hub genes linking mTOR, MAPK, and Wnt signalling pathways within the mTOR-regulatory network suggests that pathway modulation occurs through extensive crosstalk across multiple oncogenic signalling pathways in EAOCs. Conclusions: Transcriptomic dysregulation of the mTOR pathway in EAOCs reflects not only genomic alterations but also potential post-transcriptional regulation. Despite subtype-specific transcriptomic differences, both exhibited transcriptional upregulation of components of the canonical PI3K/AKT/mTOR signalling axis. Pathway modulation through the miRNA regulatory network exhibited potential crosstalk across oncogenic pathways and hub genes. Full article

Background/Objectives: Individuals with 46,XY disorders of sexual development (DSD) present with incomplete genital masculinization, aberrant gonadal development, and occasional retention of Müllerian duct remnants. Genetic factors play a substantial role in DSD pathogenesis, and whole-exome sequencing has expanded the catalog of candidate variants in recent years. However, the underlying molecular pathways remain incompletely characterized, and the functional relevance of most isolated genetic findings has not been systematically determined. This study aimed to identify and functionally characterize novel NR5A1 variants in DSD. Methods: A heterozygous missense variant NR5A1 c.88T>A (p.Cys30Ser) was identified in two patients with DSD, and initial functional characterization of the variant was performed via immunofluorescence analysis, Western Blotting, RNA sequencing, and quantitative real-time PCR analysis. Results: Wildtype NR5A1 protein localized predominantly to the nucleus, whereas the p.Cys30Ser mutant exhibited dual nuclear and cytoplasmic distribution. Compared with the wildtype, the p.Cys30Ser variant altered the expression of 642 genes, with differentially expressed genes primarily enriched in the neuroactive ligand–receptor interaction pathway. The variant impaired the transactivation of canonical NR5A1 downstream targets, resulting in the marked downregulation of 560 genes including key regulators such as KISS1R, CYP11A1, STAR, GABRP, and GRAMD1D. Conclusions: This study is the first to identify and functionally characterize the NR5A1 p.Cys30Ser variant in the context of DSD. Our findings broaden the mutational spectrum of NR5A1-related DSD and provide new insights into the molecular genetic basis of sexual development disorders. Full article

Starch modification through physical processing represents a promising “green” strategy to enhance food functionality and nutritional quality while meeting clean-label demands. This review offers a critical overview of the structural and nutritional impacts of physical modifications coupled with an exploratory use of artificial intelligence (AI)-assisted literature for screening and mining. Focused on literature published between 2001 and 2026, a traditional human-led systematic search identifies eligible studies that examined the effects of three major commercially relevant modification techniques—annealing (ANN), heat–moisture treatment (HMT), and autoclaving—on rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS), with emphasis on resistant starch type III (RS3). Among the evaluated techniques, autoclaving, particularly when followed by retrogradation, appears to offer the greatest potential for increasing RS relative to the original native starch. However, its effectiveness is strongly dependent on the raw material, with substantial gains arising from its amylose content, while waxy starches may show little improvement or even a reduction in RS. ANN tends to produce milder and more variable effects, often shifting starch from RDS toward SDS. HMT generally reduces RDS and increases SDS, making it a promising approach for attenuating glycemic responses. However, the extent of these changes can vary substantially depending on the starch source, amylose content, moisture level, and processing temperature. Overall, the work revisits the potential of physical processing as an avenue for starch engineering while underlining a pressing need for standardized workflows, harmonized methodologies, and unified protocols for quantification of starch digestibility, namely, of RS levels. Lastly, the review exemplifies AI can accelerate preliminary literature identification and synthesis yet highlights gaps in AI aptitudes for independent quantitative integration that still maintains a need for thorough human-driven validation. Full article

Skatole is a harmful, odorous pollutant in livestock manure. Skatole-degrading strains are mostly harmful Gram-negative bacteria, whereas safe Gram-positive strains demonstrate poor degradation performance, limiting bioremediation applications. To solve this problem, this study was conducted to enhance the skatole degradation capacity of the food-grade strain Lactococcus lactis NZ9000 via heterologous expression of the skatole-degrading Ska enzyme from Acinetobacter piscicola p38. Three gene sequences, designated Ska-Y (original sequence from A. piscicola p38), Ska-D (E. coli codon-optimized), and Ska-R (L. lactis codon-optimized), were separately expressed using constitutive pMG36e and nisin-inducible pNZ8148 plasmids. The constitutive system only transcribed mRNA but produced misfolded, nonfunctional inclusion bodies. By contrast, the codon-optimized pNZ8148-Ska-R strain exhibited prominent skatole degradation ability, even under non-inductive conditions. Under optimal conditions (30 ℃, ultra-low nisin induction), the engineered strain increased the 24 h degradation rate of 50 mg/L skatole from 20% to 88% and completely degraded 25 mg/L skatole. Field tests verified that the strain effectively reduced skatole accumulation in manure compost and lagoon manure. This study achieved efficient functional heterologous expression in Gram-positive bacteria, provides new insights into ultra-low-dose induction and codon optimization, and offers a safe and efficient microbial agent for livestock manure odor remediation. Full article

Plant growth-promoting bacteria (PGPB) are increasingly deployed as biofertilizers, yet the link between an inoculant’s genomic potential and its realized effect on the plant is rarely assessed within an integrative framework that jointly captures the rhizosphere microbiome, plant phenotype, and strain genome. Two Bacillus strains—B. halotolerans 1453 and B. pumilus 630—were applied to wheat and soybean in a factorial pot experiment (2 strains × 2 application methods × 3 frequencies + control, 3–4 replicates). Rhizosphere samples (n = 67 after filtering) were profiled by 16S rRNA sequencing with PICRUSt2 functional prediction and compositional validation (Aitchison PERMANOVA, ALDEx2, ANCOM-BC2). The PGPB gene repertoire was characterized by genome mining (481 marker genes, 14 categories). Wheat phenotype (six traits) and soybean height were analyzed with models appropriate for count data (Negative Binomial and binomial GLMs) for treatment-vs.-control comparisons, and with factorial ANOVA for decomposition into main effects and interactions. Crop identity was the dominant factor shaping both microbiome structure and function (PERMANOVA R² = 14.7% taxonomically and R² = 7.8% functionally, both p < 0.001), with biologically meaningful taxonomic differences between wheat and soybean; strain, application count and method had no significant effect on community composition (R² < 4% each), and co-occurrence networks showed no reliable differences between crops once read depth and sample size were controlled for. Despite this neutrality at the microbiome level, inoculation significantly increased wheat spike count (NB-GLM, all 12 treatments vs. control, padj 0.0002–0.031), ear weight, and stem count, with application count the strongest source of variability and a pronounced strain × application count. Strain 1453 outperformed 630 in spike count (+23.1%, p = 0.012) and ear weight (+20.4%, p = 0.023); we hypothesize that this may be related to its more complete DNRA pathway (narGHI + nirB-nirD) and biocontrol genes (bacE, srfAA). Strain 630 produced a less pronounced effect than strain 1453 but was subject to smaller fluctuations across replicates (CV ≈ 16–21% vs. ≈24–26% for 1453), which may reflect better resilience to environmental fluctuations, possibly due to its confirmed rsbV/rsbW stress-tolerance regulon. Rhizosphere microbiome composition differed clearly by crop (wheat vs. soybean) but showed no detectable response to strain, application method, or application count. Despite this lack of a microbiome signal, inoculation significantly increased wheat spike count and ear weight, with the magnitude and stability of this effect differing by strain. We hypothesize that this strain-dependent difference relates to underlying genomic differences—particularly in nitrogen metabolism (DNRA pathway) and stress-tolerance genes—though this link has not been tested directly and remains a hypothesis for future work. Full article

Generalized Lotka–Volterra (GLV) systems, with roots in ecology, constitute one of the most studied classes of positive ODEs. Recently, a reaction-network perspective for a generalization useful in mathematical epidemiology, called block GLV systems, was offered by Adenane, Avram and Halanay. These authors developed a “siphon calculus” in which the boundary stability of block GLV equations is studied through (i) invariant faces associated with minimal siphons, (ii) transversal Jacobians, (iii) invasibility expressed via R-invasion functions, (iv) relay graphs, (v) exclusion partitions and (vi) Lyapunov functions, without leaving the original state space. Another reaction-network perspective for GLV systems was offered by Rojas La Luz, Yu and Craciun, who developed a global stability theory for positive equilibria by introducing associated polyexponential systems obtained through the logarithmic change of variables $x_i=e^{{\xi }_i}$. In these logarithmic coordinates, compatibility classes become affine subspaces and simple quadratic Lyapunov functions establish global convergence of complex-balanced systems. The purpose of the present paper is to combine and compare these two perspectives. Our block GLV results here start with a general Perron–Volterra relay theorem (Theorem 7) and its explicit verification for the rank-one multi-strain class. The theorem constructs a face-adapted Lyapunov function in which resident blocks enter through Perron-weighted entropy terms and missing blocks through positive left Perron functionals, and reduces global convergence to resident and transversal closing conditions together with compactness. For the rank-one block model, the canonical Perron normalization gives the closing terms explicitly on an arbitrary resident support I: for every missing block $k\notin I$, the corresponding coefficient has the sign of $R_k\left(E_I\right)-1$. Hence, the relay-sink conditions $R_k\left(E_I\right)<1,k\notin I,$ verify the transversal closing hypothesis and yield convergence to the resident invariant set selected by the resident closing condition (Corollary 20); when that set consists of a single equilibrium $E_I$, the convergence is global to $E_I$. For nonlinear scalar GLV systems, Theorem 6 gives an exact characterization of global Volterra admissibility through the Jacobian averaged along the segment joining the positive equilibrium $x^{*}$ to each point x: $a\in \mathcal{W}\left(x^{*}\right)\iff {(x-x^{*})}^T\left[A\overline{J}(x;x^{*})+\overline{J}{(x;x^{*})}^{T}A\right](x-x^{*})\le 0\mathrm{for}\mathrm{every}x\in {\mathbb{R}}_{>0}^n.$ It also identifies the averaged-Jacobian matrix inequality as a sufficient global certificate, shows that uniform diagonal stability of the pointwise Jacobian family is a stronger sufficient condition, and shows, via a nonlinear counterexample, that even strict diagonal stability of $Df\left(x^{*}\right)$ at the equilibrium does not imply global Volterra admissibility. Then, we give a complex-balanced GLV example for which no positive Volterra weight yields a Lyapunov function on the whole positive orthant (Theorem 11). Thus, complex balance does not imply global Volterra admissibility. Interestingly, Volterra decrease is recovered on the compatibility manifold in this example, which leads to the open question whether such compatibility-restricted Volterra functions exist more generally for complex-balanced GLV systems (Problem 2). Finally, we show that all four logical combinations of complex balance and global Volterra admissibility occur among GLV systems, three of them already among affine systems (Theorem 12). Full article

Physiological signal emulators support the calibration, validation, and stress testing of cardiovascular monitoring devices. However, many existing systems generate electrocardiography (ECG) or photoplethysmography (PPG) independently and offer limited control over arrhythmia detection and ECG–PPG coupling. This study presents a programmable physiological signal emulator that integrates a unified event-driven ECG–PPG model with synchronized multichannel hardware. The model represents atrial pacing, atrioventricular conduction, and ventricular activation as separate functional blocks, enabling normal sinus rhythm, first-degree atrioventricular block, second-degree atrioventricular block Mobitz I, complete atrioventricular block, atrial tachycardia, and ventricular tachycardia. A Gaussian-based ECG is generated from the atrial and ventricular event sequences, while a multi-Gaussian PPG waveform is derived from ventricular activation using a beat-class-dependent electromechanical delay. The same processing architecture supports playback of recorded 12-lead clinical ECG data through an inverse lead transformation. The hardware uses an STM32F407VET6 microcontroller and MCP4921 digital-to-analog converters (DACs) to generate 10 synchronized analog outputs, comprising 09 ECG electrodes and 01 PPG channel. Validation covered physiological timing, analog-chain performance, and end-to-end signal reproduction. PR interval errors relative to a commercial electrocardiograph were 1.23 ms for normal sinus rhythm and 1.66 ms for first-degree atrioventricular block. The measured beat-to-beat PR increment during Mobitz I conduction was $40.02\pm 0.04$ ms for a programmed value of 40 ms. At commanded amplitudes of at least 800 mV, both output channels achieved absolute amplitude errors below 0.60%, total harmonic distortion below 1%, and signal-to-noise ratios (SNRs) above 30 dB. Inter-channel R-peak skew remained below the 2 ms sampling interval, and all monitored metrics varied by less than 1.5% during 60 min of continuous operation. Reproduction of a clinical 12-lead recording yielded per-lead $R^2$ values of 0.967–0.986 and a cycle-to-cycle correlation of 0.996. The emulator also reproduced amplitude-dependent bias in automated interval measurements and interpretation labels. These results demonstrate a low-cost, open-source platform for reproducible device calibration, algorithm stress testing, medical training, and physiological signal processing research. Full article

Both fibrosis and hypertrophy contribute to abnormal myocardial mechanics in hypertrophic cardiomyopathy (HCM). We sought to assess the relationships between regional structural and functional parameters in HCM by cardiac magnetic resonance (CMR). This was a retrospective single-center study of HCM patients and age-matched controls with either hypertensive heart disease (HHD) or normal CMRs. CMR feature tracking was performed to assess global and segmental 2D-radial, circumferential, and longitudinal LV strain, while native and post-contrast T1 parametric mapping analysis was performed to assess the global and regional T1 values and ECV fraction. Of 100 patients (age 56 ± 15 years; 66% male), 62 were in the HCM group and 38 in the control group (19 healthy individuals and 19 with HHD). Compared to the control group, global circumferential strain (−16.1 ± 5.9% vs. −20.8 ± 3.6%, p < 0.001) and radial strain (45.2 ± 14.1% vs. 32.1 ± 14.0%, p < 0.001) were worse in the HCM group. Among the HCM patients, there were significant correlations between segmental native T1 values at the most hypertrophied segment and global longitudinal strain (r = 0.27, p = 0.031), global circumferential strain (r = 0.34, p = 0.006), global radial strain (r = −0.29, p = 0.024), and left ventricular ejection fraction (LVEF) (r = −0.31, p = 0.014). In HCM, higher myocardial T1 at the most hypertrophied segment correlated with worse global LV myocardial strain and LVEF by CMR. Full article