Browse Articles

Collagen gene expression profiles predict recurrence and progression of DCIS to IDC

One-part slag-mullite geopolymer: role of solid activators on connected porosity and mechanical properties

Visually challenging conditions on sign language intelligibility show behavioural analogies with spoken language

Abstract This study aimed to understand how visually degraded conditions affect the intelligibility of isolated signs in sign language, and how these conditions influence perceived difficulty. Twenty-nine fluent users of Swedish Sign Language viewed 100 isolated signs presented under two types of visual degradation: spectral degradation (pixelation) and background noise (salt-and-pepper noise), each across five levels of degradation. Participants identified each sign and rated the perceived difficulty. Generalized linear mixed models were used to evaluate the effect of level of degradation on intelligibility and perceived difficulty, separately and for each type of degradation. Psychometric functions were estimated, and generalized linear mixed models were used to analyse the relationship between intelligibility and perceived difficulty. Both types of degradation significantly reduced intelligibility and increased perceived difficulty. Psychometric curves showed that intelligibility plateaued around 70% accuracy, mirroring results from speech-in-noise research. Correct responses were consistently rated as easier to perceive, and an interaction between degradation level and response accuracy was observed only in the spectral degradation condition, suggesting distinct perceptual processing mechanisms. Visual degradations affect sign language intelligibility in ways comparable to auditory distortions in speech, with differences between degradation types suggesting distinct perceptual strategies. These findings have implications for theories about linguistic processing in perceptually challenging conditions and call for further investigation into cognitive and linguistic factors that influence sign perception in degraded conditions.

On the smart coordination of flexibility scheduling in multi-carrier integrated energy systems

Abstract Coordinating the interactions among flexibility assets in multi-carrier integrated energy systems (MIES) can lead to a cost-efficient energy transition. However, the proliferation of flexibility assets and their growing participation in active demand response increases the complexity of coordinating these interactions. This paper investigates several approaches to model the coordination of flexibility scheduling in MIES with many autonomous flexibility providers. We propose runtime model coupling as an alternative modeling paradigm to overcome the limitations of monolithic centralized co-optimization. Specifically, we introduced two model coupling approaches—a distributed price-response and a decentralized market auction approach—to address practical challenges such as preserving the autonomy and privacy of flexibility providers while ensuring scalability. We conduct a quantitative benchmark of these approaches against co-optimization across varying problem sizes, complexities, and computing infrastructures. This benchmark provides new empirical insights into the trade-offs between optimality, autonomy, and scalability that have so far remained unquantified in the energy system literature. We show that model coupling offers a method to balance optimality and realism (autonomy and privacy) while delivering substantial scalability gains. The proposed model coupling approaches are formalized as open-source software with several practical applications: modelers can experiment with different flexibility modeling approaches and choose the one that best matches their modeling objectives and constraints; flexibility providers can couple their models to simulate interactions between their systems to make informed operational decisions without disclosing any confidential information.

Visual deep learning approaches for alphabetic sign language interpretation

LaRHP: latent-aware reconstruction via hypersphere projection for industrial image anomaly detection

Whole transcriptome sequencing and ceRNA regulatory network in diabetic peripheral neuropathy

Propensity-matched comparison of early femoral complications after cementless total hip arthroplasty with different stem designs

Regional transcriptomic divergence reveals thermal adaptation mechanisms in the giant kelp Macrocystis pyrifera

Contrastive unlearning via representation editing for graph neural networks

Cassipourol and β-sitosterol from Malva parviflora L.: a mechanistic study of dual anti-inflammatory action against COX/LOX and TNF-α/BCL-2

Abstract Although inflammation protects our bodies against harmful stimuli, uncontrolled inflammation drives serious chronic disorders. Malva parviflora L. (family Malvaceae) may represent a source for anti-inflammatory metabolites based on its potent anti-inflammatory activity. Its total ethanol extract demonstrated notable inhibition of cyclooxygenase-1/2 (COX-1/COX-2) and 5-lipoxygenase (5-LOX), with IC 50 values comparable to those of the reference drugs indomethacin and zileuton, respectively. The hexane fraction was the most active fraction (lowest IC₅₀) among the solvent partitions (dichloromethane, ethyl acetate, and butanol). Subsequent column chromatography of the hexane fraction produced two compounds: Cassipourol, isolated for the first time from the Malvaceae family, and β -sitosterol. Their structures were confirmed by matching their NMR and mass spectrometry data with literature. Both were validated in vitro as dual COX/LOX inhibitors, exhibiting IC₅₀ values comparable to those of the standards. To uncover additional mechanisms, a compound–target–inflammation network was constructed using network pharmacology approaches, revealing 178 shared targets. Among these, tumor necrosis factor (TNF- α ) and the antiapoptotic protein B-cell lymphoma 2 (BCL-2) emerged as central nodes linked to inflammatory pathways. Subsequent assays in human colon carcinoma (Caco-2) and lung adenocarcinoma (A549) cell lines showed that β -sitosterol suppressed TNF- α and BCL-2 by approximately 55%, whereas cassipourol displayed only modest inhibition (~20%). Molecular docking predicted moderate ( ca. –4.5) and strong (> –5) binding affinities of both compounds to key inflammatory targets. Collectively, these results suggest that β -sitosterol from M. parviflora is a promising multitarget lead for inflammatory disorders, including cancer, whereas cassipourol requires further structural optimization and mechanistic investigation to improve its unfavorable physicochemical properties.

Enhanced antimicrobial activity, mechanical and dielectric properties of styrene butadiene rubber vulcanizates designed for protection products

Abstract This study focuses on fabricating and evaluating novel, ecofriendly, green, flexible, antimicrobial Styrene Butadiene Rubber vulcanizates for producing safety products. Styrene butadiene rubber (SBR) was compounded with ginger extract, a plant rich in bioactive phenolic compounds, and the conventional drugs, berberine and chitosan, in the ordinary mixer of rubber. Ginger ( Zingiber officinale ) extract was analyzed using HPLC. The rheological properties of the SBR mixes were analyzed to determine the optimal curing time. The compounded rubber mixes were then vulcanized at 152 °C. The prepared vulcanizates were evaluated using Fourier-transform infrared spectroscopy (FTIR), specific surface area measurements, mechanical, swelling, antimicrobial properties, and cytotoxicity analysis. The results showed that the physicochemical and mechanical properties of all vulcanizates remained robust, i.e., no significant degradation, even after exposure to thermal oxidative aging at 90 °C for seven days. Therefore, the change of tensile strength percentage after aging was 25, 5.56, 13, and 10% for free SBR, SBR/30 phr chitosan, SBR/20 phr ginger, and SBR/7 phr berberine, respectively. The cytotoxicity tests confirmed the safety of the investigated vulcanizates due to the negative results against the normal human fibroblast cell line (BJ1). Furthermore, the antimicrobial activity results demonstrated that the release of the various antimicrobial agents successfully inhibited the growth of different bacteria and fungi on the SBR rubber surface. The dielectric and electric findings highlighted the ability of the presence of bioactive agents to modify dielectric relaxation and conductivity in the investigated SBR vulcanizates and confirmed the utility of SBR composites contained 30 phr chitosan, 20 phr ginger, and 7 phr berberine as a good viable choice for antistatic and flexible electronic applications.

Analysis of the ERG25 gene family based on the whole-genome sequence in Trametes versicolor and its response to light stress

Convolutional low-rank adaptation for efficient semantic segmentation in vision transformers

Abstract Vision Transformers (ViTs) have shown remarkable performance across various computer vision tasks, but their fine-tuning for dense prediction tasks such as semantic segmentation remains computationally intensive. This work proposes a novel dual-task architectural application of the LyCORIS Low-Rank Adaptation for Convolutions (LyCORIS LoCon) framework, which introduces learnable low-rank convolutional modules into pre-trained ViTs. This method is applied to Depth Anything V2 (DAV2), augmenting its decoder to support dual-task outputs; monocular depth estimation and binary human semantic segmentation, without disrupting its original capabilities. By injecting only 150K trainable parameters, this approach significantly reduces the adaptation cost while achieving segmentation performance comparable to state-of-the-art models like SAM, MaskFormer, and SegFormer. Extensive experiments on filtered COCO 1 and ImageNet subsets show that Conv-LoRA enhances task-specific learning with minimal computational overhead. The method achieves an mAP of 89.69% and an mIoU of 79.17% for human segmentation, performing competitively alongside state-of-the-art models like Mask2Former, while preserving the depth prediction accuracy of the base model.

Enhanced multi-attribute decision-making method to analyze renewable energy sources for sustainable transportation by synergizing the cubic set with picture fuzzy hypersoft sets

Taxonomic reevaluation of Eryx jaculus in Anatolia with a monotypic perspective

A reliable starfish optimization algorithm for establishing equivalent circuit of proton exchange membrane fuel cell

Abstract The proton exchange membrane fuel cell (PEMFC) can be effectively simulated, managed, and controlled via accurate identification of its model’s unknown parameters. The nonlinear properties of fuel cells (FCs) and the absence of complete data in the datasheet cause certain obstacles in this procedure. This study suggests a novel approach employing the starfish optimization algorithm (SFOA) for designing the equivalent circuit of PEMFC by evaluating its parameters utilizing experimental data. This algorithm is highly efficient as well as avoiding local optima due to exploitation and exploration balance. Five distinct FCs are analyzed: Horizon_H12, Horizon H-1000XP PEMFC stack, Temasek_1kw, SR-12PEM 500-W, and BCS-500W PEMFC stacks. The suggested SFOA is evaluated in comparison with previously published techniques of seagull optimization algorithm (SOA), escape optimizer (ESC), gold rush optimizer (GRO), tunicate swarm algorithm (TSA), beluga whale optimization (BWO), success history intelligent optimizer (SHIO), enhanced transient search optimizer (ESTO) and Newton–Raphson-based optimizer (NRBO). Decreasing the sum square error (SSE) between the calculated and measured output voltages is the targeted goal. The minimum values of SSE obtained through the suggested SFOA are 4.662696E−04, 3.948527E−01, 5.4322160E−01, 1.056370, and 1.169778E−02 for Horizon_H12, Horizon H-1000XP PEMFC stack, Temasek_1kw, SR-12PEM 500-W, and BCS-500W PEMFC stacks, respectively. Additionally, the PEMFC dynamic simulation model is constructed in Matlab/Simulink, while step load disruption is employed for analyzing the model’s performance. The acquired results indicate the success of the suggested SFOA in constructing dependable models of different PEMFCs.

Grating based interferometric devices on a silicon photonic platform

Abstract This work presents a class of integrated photonic interferometers based on the Michelson architecture, implemented on a silicon-on-insulator platform operating at 1.5–1.6  $$\mu$$ m. The devices combine directional couplers, subwavelength gratings (SWG), and Bragg grating reflectors. Furthermore, by cascading and branching multiple Michelson units, we demonstrate more complex interferometric structures for specialized applications. The integration of SWG sections enables fine-tuning of the effective refractive index and enhances sensitivity for sensing applications through increased evanescent field interaction with the surrounding medium. Selected designs are fabricated and experimentally characterized. Fabricated Fabry-Pérot devices achieve quality factors exceeding 6,500, while Michelson-based structures exhibit extinction ratios up to 43 dB, demonstrating good qualitative agreement with simulations. Finally, we explore potential applications optical filtering, sensing, and electro-optic modulation.

Techno-economic analysis and resilience enhancement of a hospital microgrid under grid outage scenarios

Abstract Hospitals are critical infrastructures where power continuity is paramount. This study presents a techno-economic and resilience analysis of a grid-connected hybrid microgrid for a medium-sized hospital, comprising solar photovoltaics (PV), a battery energy storage system (BESS), and a diesel generator. Using a mixed-integer linear programming (MILP) model via NREL’s REopt ® platform, we optimized the system design to minimize the Net Present Cost (NPC) while ensuring an uninterrupted power supply to critical loads during grid outages. The analysis evaluated a wide range of outage scenarios, varying in duration (7–24 h), timing, season, and critical load level (50–100%). This study shows that a design of a microgrid for enhanced resilience is both technically and economically beneficial. The microgrid design with optimization achieves a net present cost savings of 14% for the financial optimization scenario and 9% to 14.2% for all the resilience-constrained scenarios compared to the business-as-usual case of 100% grid dependence. Most importantly, compared to the financial optimum rather than the business-as-usual case, the cost of including resilience constraints is a mere 0.4% to 2.4% of net present cost, showing that increased energy resilience can be delivered at a minimal cost. A key finding is that systems designed for summer outages yield higher savings due to greater solar availability, and a strategic deep-discharge protocol for the battery during emergencies is crucial for cost-effectiveness. This work provides an actionable framework for hospital administrators to enhance energy resilience without incurring a financial penalty, and in many cases, while realizing significant long-term cost savings.

Lipid ligand binding and membrane interactions of a novel food-derived lipid transfer protein enhance basophil allergic responses

Abstract Non-specific (ns) lipid transfer proteins (LTPs) are lipid-binding allergens whose natural ligands are not fully known. To elucidate the function and allergenic relevance of nsLTP-lipid complexes, purified natural Lupinus luteus ( L. luteus ) nLTP and recombinant peach LTP, rPru p 3, were tested for membrane interaction and lipid transport activity using liposome assays and Förster-resonance-energy-transfer (FRET) in a case-level proof-of-principle investigation. Allergenic relevance of the LTP-lipid interaction was evaluated in the presence of oleic acid (OA), phosphatidylcholine (PC), phosphatidylglycerol (PG), and phosphatidylserine (PS) in a basophil activation test (BAT) with effector cells from an LTP-allergic patient. Both LTPs interacted with neutral PC and negatively charged PS liposomal membranes. A novel transport activity for anionic PG species was identified for both proteins, indicating a shared functional preference for the glycerol headgroup. LTP-dependent lipid exchange/mixing were consistent with transfer. However, fusion/mixing mechanisms cannot be excluded with the current readout. In BAT, both LTPs showed enhanced activation in combination with OA, PC, PG, and PS. As PG is a key component of bacterial membranes, the PG specificity of the lipid interaction of L. luteus nLTP and rPru p 3 is likely of relevance in allergen interaction with the gut microbiome and for enhancement of allergic symptoms. These findings highlight lipid-specific functional properties and lipid-dependent modulation of allergenic activity in plant nsLTPs.