Search Results (1,573)

Aging is a complex, universal biological process characterized by the progressive and irreversible decline of physiological functions across multiple organ systems. This deterioration is primarily driven by cumulative cellular damage arising from both intrinsic and extrinsic stressors. The free radical theory of aging, first proposed by Denham Harman in 1956, highlights the role of reactive oxygen species (ROS), byproducts of normal metabolism, in driving oxidative stress and age-related degeneration. Emerging evidence emphasizes the importance of redox imbalance in the onset of neurodegenerative diseases and aging. Among the critical cellular defenses against oxidative stress are sulfur-containing amino acids, namely cysteine (Cys) and selenocysteine (Sec). Cysteine serves as a precursor for glutathione (GSH), a central intracellular antioxidant, while selenocysteine is incorporated into key antioxidant enzymes such as glutathione peroxidases (GPx) and thioredoxin reductases (TrxR). These molecules play pivotal roles in neutralizing ROS and maintaining redox homeostasis. This review aims to provide an updated and critical overview of the role of thiol-containing amino acids, specifically cysteine and selenocysteine, in the regulation of redox homeostasis during aging. Full article

Secukinumab (SEC) is a recombinant, fully human monoclonal antibody that is selective for interleukin-17A (IL-17A). SEC may increase the risk of developing infections such as oral herpes and oral candidiasis. The aim of this case report and literature review was to describe chronic hyperplastic candidiasis (CHC) in a patient with psoriasis (PsO) and psoriatic arthritis (PsA) treated with SEC. CHC is a rare and atypical clinical entity. A definitive diagnosis requires biopsy of the oral mucosa for histopathological diagnosis (PHD). The differential diagnosis includes hairy tongue, hairy leukoplakia, oral lichen planus (OLP), oral lichenoid reaction (OLR), leukoplakia, frictional keratosis, morsication, oral psoriasis, syphilis, and oral lesions associated with coronavirus disease (COVID-19). In addition to the usual factors (xerostomia, smoking, antibiotics, vitamin deficiency, immunosuppression, comorbidities), the new biological therapies/immunotherapies are a predisposing factor for oral candidiasis. The therapeutic approach must be multidisciplinary and in consultation with a clinical immunologist. Dentists and specialists (oral medicine, dermatologists, rheumatologists) must be familiar with the oral adverse events of the new biological therapies. Simultaneous monitoring of patients by clinical immunology and oral medicine specialists is crucial for timely diagnosis and therapeutic intervention to avoid possible adverse events and improve quality of life (QoL). Full article

In the context of the valorization of agri-food by-products, tomato (Solanum lycopersicum L.) seeds represent a protein-rich matrix containing potential bioactives. The aim of the present work is to develop a biochemical pipeline for (i) achieving high protein recovery from tomato seed, (ii) optimizing the hydrolysis with different proteases, and (iii) characterizing the resulting peptides. This approach was instrumental for obtaining and selecting the most promising peptide mixture to test for antifungal activity. To this purpose, proteins from an alkaline extraction were treated with bromelain, papain, and pancreatin, and the resulting hydrolysates were assessed for their protein/peptide profiles via SDS-PAGE, SEC-HPLC, and RP-HPLC. Bromelain hydrolysate was selected for antifungal tests due to its greater quantity of peptides, in a broader spectrum of molecular weights and polarity/hydrophobicity profiles, and higher DPPH radical scavenging activity, although all hydrolysates exhibited antioxidant properties. In vitro assays demonstrated that the bromelain-digested proteins inhibited the growth of Fusarium graminearum and F. oxysporum f.sp. lycopersici in a dose-dependent manner, with a greater effect at a concentration of 0.1 mg/mL. The findings highlight that the enzymatic hydrolysis of tomato seed protein represents a promising strategy for converting food by-products into bioactive agents with agronomic applications, supporting sustainable biotechnology and circular economy strategies. Full article

Titanium is widely recognized as an interesting material for electrodes due to its excellent corrosion resistance, mechanical strength, and biocompatibility. However, further functionalization is often necessary to impart advanced interfacial properties, such as selective ion transport or stimuli responsiveness. In this context, the integration of smart polymers, such as poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA)—noted for its dual pH- and thermo-responsive behavior—has emerged as a promising approach to tailor surface properties for next-generation devices. This work compares two covalent immobilization strategies for PDMAEMA on titanium: the “graft-to” method, involving the attachment of pre-synthesized polymer chains, and the “graft-from” method, based on surface-initiated polymerization. The resulting materials were characterized with size exclusion chromatography (SEC) for molecular weight, Fourier-transform infrared spectroscopy (FTIR) for chemical structure, scanning electron microscopy (SEM) for surface morphology, and contact angle measurements for wettability. Electrochemical impedance spectroscopy and polarization studies were used to assess electrochemical performance. Both strategies yielded uniform and stable coatings, with the mode of grafting influencing both surface morphology and functional stability. These findings provide valuable insights into the development of adaptive, stimuli-responsive titanium-based interfaces in advanced electrochemical systems. Full article

Surface defects of photovoltaic (PV) cells can seriously affect power generation efficiency. Accurately detecting such defects and handling them in a timely manner can effectively improve power generation efficiency. Aiming at the high-precision and real-time requirements for surface defect detection during the use of PV cells, this paper proposes a PV cell surface defect detection algorithm based on SEC-YOLOv8. The algorithm first replaces the Spatial Pyramid Pooling Fast module with the SPPELAN pooling module to reduce channel calculations between convolutions. Second, an ECA attention mechanism is added to enable the model to pay more attention to feature extraction in defect areas and avoid target detection interference from complex environments. Finally, the upsampling operator CARAFE is introduced in the Neck part to solve the problem of scale mismatch and enhance detection performance. Experimental results show that the improved model achieves a mean average precision (mAP@0.5) of 69.2% on the PV cell dataset, which is 2.6% higher than the original network, which is designed to achieve a superior balance between the competing demands of accuracy and computational efficiency for PV defect detection. Full article

Massively parallel or next-generation sequencing (NGS) has enabled the genetic characterization of cancer patients, allowing the identification of somatic and germline variants associated with their diagnosis, tumor classification, and therapy response. Despite its benefits, NGS testing is not yet available in the Chilean public health system, rendering it both costly and time-consuming for patients and clinicians. Using a retrospective cohort of 67 formalin-fixed, paraffin-embedded (FFPE) colorectal cancer (CRC) samples, we aimed to implement the identification, annotation, and prioritization of relevant actionable tumor somatic variants in our laboratory, as part of the public health system. We compared two different library preparation methodologies (amplicon-based and capture-based) and different bioinformatics pipelines for sequencing analysis to assess advantages and disadvantages of each one. We obtained 80.5% concordance between actionable variants detected in our analysis and those obtained in the Cancer Genomics Laboratory from the Universidad de Chile (62 out of 77 variants), a validated laboratory for this methodology. Notably, 98.4% (61 out of 62) of variants detected previously by the validated laboratory were also identified in our analysis. Then, comparing the hybridization capture-based library preparation methodology with the amplicon-based strategy, we found ~94% concordance between identified actionable variants across the 15 shared genes, analyzed by the TumorSec^TM bioinformatics pipeline, developed by the Cancer Genomics Laboratory. Our results demonstrate that it is entirely viable to implement an NGS-based analysis of actionable variant identification and prioritization in cancer samples in our laboratory, being part of the Chilean public health system and paving the way to improve the access to such analyses. Considering the economic realities of most Latin American countries, using a small NGS panel, such as TumorSec^TM, focused on relevant variants of the Chilean and Latin American population is a cost-effective approach to extensive global NGS panels. Furthermore, the incorporation of automated bioinformatics analysis in this streamlined assay holds the potential of facilitating the implementation of precision medicine in this geographic region, which aims to greatly support personalized treatment of cancer patients in Chile. Full article

The newly developed automorphism ensemble decoder (AED) leverages the rich automorphisms of Reed–Muller (RM) codes to achieve near maximum likelihood (ML) performance at short code lengths. However, the performance gain of AED comes at the cost of high complexity, as the ensemble size required for near ML decoding grows exponentially with the code length. In this work, we address this complexity issue by focusing on the factor graph permutation group (FGPG), a subgroup of the full automorphism group of RM codes, to generate permutations for AED. We propose a uniform partitioning of FGPG based on the affine bijection permutation matrices of automorphisms, where each subgroup of FGPG exhibits permutation invariance (PI) in a Plotkin construction-based information set partitioning for RM codes. Furthermore, from the perspective of polar codes, we exploit the PI property to prove a subcode estimate convergence (SEC) phenomenon in the AED that utilizes successive cancellation (SC) or SC list (SCL) constituent decoders. Observing that strong SEC correlates with low noise levels, where the full decoding capacity of AED is often unnecessary, we perform path pruning to reduce the decoding complexity without compromising the performance. Our proposed SEC-aided path pruning allows only a subset of constituent decoders to continue decoding when the intensity of SEC exceeds a preset threshold during decoding. Numerical results demonstrate that, for the FGPG-based AED of various short RM codes, the proposed SEC-aided path pruning technique incurs negligible performance degradation, while achieving a complexity reduction of up to 67.6%. Full article

In this study, we utilize a unique quarterly dataset of non-GAAP earnings to re-examine the “informational” role of non-GAAP earnings from the perspective of value relevance and earnings predictability in the post-Reg G period. We find that non-GAAP earnings are more value relevant and can better predict future operating earnings of a firm compared to equivalent GAAP earnings. Additionally, we also find empirical evidence suggesting that the difference in the value relevance and earnings predictability between non-GAAP and equivalent GAAP earnings can vary across but cannot be completely mitigated by firm-level characteristics, such as the market value of equity, accruals quality, analyst coverage, and managerial ability of a firm. Moreover, our supplementary analysis reveals that the superior value relevance and predictive power of non-GAAP earnings persist even after the SEC’s release of the Compliance and Disclosure Interpretations (C&DI) in 2010. Overall, our empirical evidence suggests a superior “informational” role of non-GAAP earnings to equivalent GAAP earnings in terms of valuation and predictability on future operating performance in the post-Reg G period. Full article

This paper introduces the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) framework, a novel approach that embeds security throughout the entire Secure Software and System Development Life Cycle (S-SDLC). AZTRM-D strategically unifies three established methodologies: DevSecOps practices, the NIST Risk Management Framework (RMF), and the Zero Trust (ZT) model. It then significantly augments their capabilities through the pervasive application of Artificial Intelligence (AI). This integration shifts traditional, often fragmented, security paradigms towards a proactive, automated, and continuously adaptive security posture. AI serves as the foundational enabler, providing real-time threat intelligence, automating critical security controls, facilitating continuous vulnerability detection, and enabling dynamic policy enforcement from initial code development through operational deployment. By automating key security functions and providing continuous oversight, AZTRM-D enhances risk mitigation, reduces vulnerabilities, streamlines compliance, and significantly strengthens the overall security posture of software systems, thereby addressing the complexities of modern cyber threats and accelerating the delivery of secure software. Full article

The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection presents interesting prospects in accuracy and responsiveness. By streamlining data complexity and allowing faster and more effective fault diagnosis, dimensionality reduction methods play vital role. Using dimensionality reduction and ML techniques, this work explores inverter fault detection in GCPV systems. Photovoltaic inverter operational data was normalized and preprocessed. In the next step, dimensionality reduction using Principal Component Analysis (PCA) and autoencoder-based feature extraction were explored. For ML training four classifiers which include Random Forest (RF), logistic regression (LR), decision tree (DT), and K-Nearest Neighbors (KNN) were used. Trained on the whole standardized dataset, the RF model routinely produced the greatest accuracy of 99.87%, so efficiently capturing complicated feature interactions but requiring large processing resources and time of 36.47sec. LR model showed reduction in accuracy, but very fast training time compared to other models. Further, PCA greatly lowered computing demands, especially improving inference speed for LR and KNN. High accuracy of 99.23% across all models was maintained by autoencoder-derived features. Full article

Salbutamol sulfate is a selective β2-receptor agonist used to treat asthma and chronic obstructive pulmonary disease. The crystals of salbutamol sulfate usually appear as needles with a relatively large aspect ratio, showing poor powder properties. In this study, spherical particles of salbutamol sulfate were obtained via antisolvent crystallization. Four different antisolvents, including ethanol, n-propanol, n-butanol, and sec-butanol, were selected, and their effects on crystal form and morphology were compared. Notably, a new solvate of salbutamol sulfate with sec-butanol has been obtained. The novel crystal form was characterized by single-crystal X-ray diffraction, revealing a 1:1 stoichiometric ratio between solvent and salbutamol sulfate in the crystal lattice. In addition, the effects of crystallization temperature, solute concentration, ratio of antisolvent to solvent, feeding rate, and stirring rate on the morphology of spherical particles were investigated in different antisolvents. We have found that crystals grown from the n-butanol–water system at optimal conditions (25 °C, antisolvent/solvent ratio of 9:1, and drug concentration of 0.2 g·mL⁻¹) could be developed into compact and uniform spherulites. The morphological evolution process was also monitored, and the results indicated a spherulitic growth pattern, in which sheaves of plate-like crystals gradually branched into a fully developed spherulite. This work paves a feasible way to develop new crystal forms and prepare spherical particles of pharmaceuticals. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

A maximum entropy (ME) methodology was used to infer the Hubble constant from the temperature anisotropies in cosmic microwave background (CMB) measurements, as measured by the Planck satellite. A simple cosmological model provided physical insight and afforded robust statistical sampling of a parameter space. The parameter space included the spectral tilt and amplitude of adiabatic density fluctuations of the early universe and the present-day ratios of dark energy, matter, and baryonic matter density. A statistical temperature was estimated by applying the equipartition theorem, which uniquely specifies a posterior probability distribution. The ME analysis inferred the mean value of the Hubble constant to be about 67 km/sec/Mpc with a conservative standard deviation of approximately 4.4 km/sec/Mpc. Unlike standard Bayesian analyses that incorporate specific noise models, the ME approach treats the model error generically, thereby producing broader, but less assumption-dependent, uncertainty bounds. The inferred ME value lies within 1σ of both early-universe estimates (Planck, Dark Energy Signal Instrument (DESI)) and late-universe measurements (e.g., the Chicago Carnegie Hubble Program (CCHP)) using redshift data collected from the James Webb Space Telescope (JWST). Thus, the ME analysis does not appear to support the existence of the Hubble tension. Full article

Lacticaseibacillus rhamnosus J3205 was isolated from traditional fermented sauces and demonstrated potential probiotic properties. The strain exhibited high tolerance to simulated saliva (93.24% survival) and gastrointestinal conditions (69.95% gastric and 50.44% intestinal survival), along with strong adhesion capacity (58.25%) to intestinal epithelial cells. Safety assessments confirmed the absence of virulence and antibiotic resistance genes. Genomic analysis revealed stress-response genes and 34 insertion sequence (IS) elements, while proteomic profiling identified Pgk as a key enzyme in lactic acid production and SecY in oxidative stress resistance. Functionally, J3205 significantly reduces pro-inflammatory cytokines (TNF-α, IL-6, IL-1β) and enhances antioxidant markers (SOD, GSH) in vitro. These results position L. rhamnosus J3205 as a promising candidate for gut-health foods, anti-inflammatory nutraceuticals, and oxidative-stress therapeutics, warranting further in vivo validation. Full article

This study describes both experimental and numerical investigations into the heat and mass transfer processes governing the vacuum freeze drying of camel milk, with a specific focus on improving the energy efficiency. A three-dimensional model was developed and solved using the finite element method to simulate temperature evolution and sublimation interface progression during drying. The numerical model was validated against experimental data, achieving strong agreement, with an $R^2$ value of 0.94. A detailed parametric analysis examined the effects of the shelf temperature, sample thickness, and chamber pressure on the drying kinetics and energy input. The results indicate that optimising these parameters can significantly reduce the energy consumption and processing time while maintaining product quality. Notably, reducing the sample thickness to 4 mm shortened the drying time by up to 40% and reduced the specific energy consumption (SEC) from 358 to 149 kWh/kg. These findings offer valuable insights for the design of more energy-efficient freeze drying systems, with implications for sustainable milk powder production and industrial-scale process optimisation. Full article