Browse Articles

Distribution and characteristics of microplastics in the seagrass ecosystem of Sri Lanka (Indian Ocean)

A brain circuit of bidirectional modulation of social and nonsocial cognition by androgens and estrogens in male mice

Androgens and estrogens rapidly influence brain function and behaviors critical to social species, including social recognition, which is essential for group living and modulating social interactions. The specific brain regions involved and their hormonal regulation remain poorly understood. This regulation is complex, as the main circulating androgen, testosterone (T), is metabolized into estrogenic (17β-estradiol, E2) and androgenic (dihydrotestosterone, DHT) compounds, meaning its actions can be mediated through both androgen (AR) and estrogen receptors (ERs). This study identifies an estrogen-regulated, social recognition-specific brain circuit, demonstrating that T and E2, but not DHT, interact with the arginine–vasopressin (AVP) system in the bed nucleus of the stria terminalis (BNST)–lateral septum (LS) pathway to promote social recognition in male mice. T, E2, and DHT all facilitated social recognition and impaired object recognition within forty minutes of infusion into the BNST, but only the effects of T and E2 were blocked by an AVP receptor 1a (V1aR) antagonist, suggesting distinct mechanisms underlie the facilitation of social recognition by estrogens and androgens. Using CRISPR/Cas9 to selectively knock down AR, ERα, ERβ, or G protein–coupled estrogen receptor (GPER) in the BNST of male mice four weeks before steroid infusion, we found that ERα and ERβ (but not GPER) are necessary for T and E2 to rapidly facilitate social recognition, whereas DHT acts through AR. Our findings highlight the distinct roles of AR and ERs in the modulation of social recognition by steroid hormones, revealing redundant and nonoverlapping mechanisms of androgens and estrogens in the functioning of the male social brain.

Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning

Discovering regularity and mechanisms of word sense acquisition in childhood

How does language use inform the emergence of word meanings in early life? Prior work in developmental psychology and the cognitive sciences typically focuses on studying word acquisition in children without specifying how different senses within a word emerge through time. To shed light on word sense acquisition, we propose a framework grounded in state-of-the-art computational methodologies of contextual word embedding to characterize how different senses of a word unfold as children acquire their lexicon. Our framework identifies word senses automatically by forming semantic clusters through natural language use, and analyzes 1,270 words from approximately 4 million utterances produced by children (19 to 144 mo) and their caretakers. The psychological validity of these senses was assessed based on a combination of dictionaries, human judgment of semantic similarity, and evaluation from a large language model. We then tested three hypotheses motivated by existing work on word sense emergence in language evolution. First, concrete senses of a word tend to emerge earlier than more abstract senses in child language. Second, word senses grow incrementally in semantic space across development. Third, algorithms of semantic chaining—how words spawn new senses by extending from existing senses—recapitulate the order of word senses in development. We find support for all three hypotheses. Our work suggests that the development of word meaning in language acquisition resembles that in language evolution and offers a converging view on the cognitive principles and mechanisms across timescales in the ontogeny and phylogeny of word sense emergence.

Longitudinal effect of glycaemic variability on retinal neurodegeneration and neuropathic characteristics in paediatric patients with type 1 diabetes mellitus

Emergent anisotropic three-phase order in critically doped superconducting diamond films

Two decades since its discovery, superconducting heavily boron-doped diamond (HBDD) still poses fundamental questions that need to be answered to unlock its full potential for quantum applications. We use electrical magnetotransport measurements of critically doped homoepitaxial single crystal HBDD films to reveal signatures of intrinsically granular superconductivity. By studying the dependence of electrical resistivity on temperature and magnetic field vector, we infer that this granularity arises from doping induced disorder. We observe an unexpected three-phase anisotropy in the magnetoresistance, accompanied by a spontaneous transverse voltage (Hall anomaly). Our findings indicate the emergence of an anisotropic order in an otherwise isotropic single crystal HBDD film, offering insights into the mechanism of superconductivity in this quantum material.

Use of machine learning and voice for multiclass classification of Parkinson’s disease, chronic obstructive pulmonary disease, and healthy controls

Abstract Parkinson’s disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and health care systems. Both disorders affect voice production through different physiological mechanisms, yet neither condition has a widely adopted objective biomarker for routine clinical use. Voice analysis has emerged as a non-invasive digital biomarker candidate, but existing studies have largely focused on binary classification within a single disorder or language. This study aimed to evaluate whether an unified multiclass machine learning (ML) framework applied to sustained vowel “a” phonation can discriminate between PD, COPD, and healthy controls (HC) across linguistically distinct cohorts. Sustained vowel recordings were analyzed from Swedish speaking individuals with COPD and HC, and English-speaking individuals with PD and HC, collected under comparable mobile recording conditions. Acoustic features included baseline voice measures and Mel Frequency Cepstral Coefficients. A soft voting ML framework integrating support vector machine, random forest, CatBoost, and light gradient boosting classifiers was trained using nested cross validation with hyperparameter optimization. Data were partitioned at the participant level into a development cohort and an independent test cohort. Model performance was evaluated using accuracy, macro averaged precision, recall, F1 score, receiver operating characteristic analysis, and confusion matrices. Model interpretability was assessed using Shapley additive explanations and vowel space analysis. The final soft voting classifier achieved robust multiclass discrimination on the participant disjoint independent test set, with an overall accuracy of 0.842 and a macro averaged F1 score of 0.839. Classification performance differed across groups, with the highest performance observed for PD, intermediate performance for HC, and lower performance for COPD. Misclassifications occurred primarily between HC and COPD, while confusion between PD and COPD was minimal. Feature attribution analysis revealed class dependent relevance patterns, and vowel space analysis demonstrated subtle but consistent group level differences. These findings demonstrate the feasibility of using an explainable soft voting machine learning framework applied to sustained vowel phonation to distinguish between neurologically and respiratory driven voice impairments across linguistic contexts. The study supports voice as a promising digital biomarker modality for multiclass clinical discrimination using mobile recordings.

2024 global temperature record is consistent with model-predicted warming

We employ a semiempirical approach combining climate model simulations and observational temperatures to assess the likelihood of recent global temperature records. Monte Carlo simulations are used to generate global temperature series consistent with combined estimates of forced (anthropogenic + natural) and internal variability derived from observations and CMIP6 multimodel simulations. We find that the El Niño-boosted 2024 global temperature record had a ~12% likelihood of occurrence (a one-in-eight-year event), similar to the prior (also El Niño-boosted) record year 2016 (~14% likelihood). Of the records set during the past three decades, only 1998 is found to have been truly anomalous, with a ~2.5% likelihood of occurrence. Each of these records is found to have been nearly impossible in the absence of human-caused warming.

Flexural behaviour of RC shear wall using enhanced finite element model

Abstract Accurate nonlinear finite element modelling (FEM) of reinforced concrete (RC) shear walls is essential for evaluating their seismic performance and ensuring structural safety. However, most existing modelling approaches often involve high computational costs or simplified representations of nonlinear behaviour such as cracking, tension stiffening, and confinement effects. This study presents a practical and verified FEM approach for RC shear walls that fail in flexure, achieving a balance between accuracy and computational efficiency. The proposed modelling strategy employs nonlinear material constitutive models for both concrete and reinforcement and utilizes a refined mesh configuration to capture the global response while maintaining manageable computation times. The analyses were performed using CSI ETABS, and the model was validated against thirteen experimental wall specimens from nine independent studies with varying geometries, reinforcement ratios, and boundary conditions. The comparison of pushover curves indicated strong agreement with experimental data. The results confirm that the proposed FEM methodology can reliably simulate the nonlinear behaviour of flexural RC shear walls.

Surrounding landscapes shape species persistence in fragmented forests

Analysis, control, and forecasting the dynamics of SIRD models with saturated treatment and nonlinear incidence

Do negative social ties accelerate aging in adults, or does aging erode social ties?

Cu-Al-LDH/porphyrin-based COF nanocomposite as a high-performance sorbent for hollow fiber solid phase microextraction of selected pesticide residues

Sperm, egg, and embryo proteins critical for genetic adaptation of herring to low salinity in the Baltic Sea

How species genetically adapt to new environments is a central question in evolutionary biology. Here whole-genome sequencing combined with functional analysis is used to dissect how Atlantic herring, a marine fish, has adapted to the brackish Baltic Sea. Genes involved in reproduction and early development emerge as primary targets of natural selection, with key changes in a sperm-specific anion channel ( LRRC8C2 ), a zona pellucida protein ( ZPBA1 ), a cluster of three genes for fish transglutaminase ( FTG1-3 ), and a copy number expansion of a fish hatching enzyme gene ( HE1C ). The large diameter of LRRC8C2 homomers facilitates transport of ions and osmolytes, likely preventing swelling of sperm when spawning in low salinity. Altered ZPBA1 sequence together with modified FTG1-3 enzyme activity produces a harder egg envelope that prevents egg swelling in brackish waters, while the enhanced activity of the adapted HE1C enzyme enables larvae to digest this reinforced egg envelope during hatching. Baltic Sea herring populations reproducing in brackish water are fixed or nearly fixed for variant alleles at these four unlinked loci, each carrying multiple amino acid substitutions compared to the alleles prevalent in the Atlantic Ocean populations. The alleles at two of these loci ( FTG1-3, and HE1C ) have been introgressed from the sister species Pacific herring. These findings reveal concrete molecular mechanisms by which a marine species has adapted to a novel, low-salinity environment.

A robust multi-criteria supplier selection framework based on linguistic cubic interval-valued intuitionistic fuzzy aggregation operators

Comparative insecticidal efficacy and biochemical impact of nano-encapsulated citronella and geranium essential oils against Spodoptera littoralis (Lepidoptera: Noctuidae)

Abstract This study assessed the insecticidal efficacy of bulk and nano-formulated essential oils of citronella ( Cymbopogon nardus ) and geranium ( Pelargonium graveolens ) against the cotton leafworm Spodoptera littoralis (Boisd.) under controlled laboratory conditions. Nano-formulations were prepared using polyethylene glycol (PEG) via oil-in-water emulsification, achieving high encapsulation efficiencies (EE%) of 96.16% for citronella and 94.16% for geranium oils, with corresponding loading capacities (LC%) of 77.43% and 76.35%, respectively. Transmission Electron Microscopy (TEM) confirmed spherical nanoparticles with diameters ranging from 20 to 60 nm. Bioassay results revealed that nano-citronella exhibited the highest insecticidal activity, with the lowest LC₅₀ value (0.4111 × 10 4  ppm) and a toxicity index of 100, indicating approximately tenfold greater potency than its bulk counterpart. Nano-geranium showed a moderate enhancement in toxicity (LC₅₀ = 3.95 × 10 4 ppm) compared to the bulk oil. Biochemical analyses demonstrated pronounced disruptions in enzymatic activity: chitinase activity ranged from 4.43 ± 0.94 µg NAGA/min in bulk citronella treatments to 23.70 ± 2.90 µg NAGA/min in nano-citronella. Invertase activity increased markedly, reaching 175.34 ± 3.47 µg glucose/min in nano-citronella treatments, while total protein content varied from 750.77 ± 3.85 µg/mg tissue in nano-geranium to 1315.80 ± 1.27 µg/mg tissue in bulk citronella treatments. Developmental parameters were significantly affected with nano-citronella LC₅₀ treatments, causing prolonged larval duration (19.6 ± 0.71 days) and reduced pupal weight (178.7 ± 3.18 mg) compared with control groups. Molecular docking and molecular dynamics simulations identified geraniol as a potent natural inhibitor of bacterial chitinase (PDB ID: 1CTN), forming stable π–alkyl and van der Waals interactions with Trp252, Phe373, and Ser341, and exhibiting a strong binding free energy (–20.08 kcal/mol). The geraniol–chitinase complex displayed enhanced conformational stability (RMSD = 1.15 ± 0.15 Å) and compactness (Rg = 26.9 ± 0.09 Å) relative to the unbound enzyme. Overall, these findings demonstrate that nano-encapsulation substantially enhances the insecticidal efficacy, biochemical disruption, and molecular stability of essential oils, highlighting their potential as eco-friendly, high-efficiency biopesticides within integrated pest management (IPM) programs.

Multi-level fuzzy comprehensive evaluation of the bearing deformation process and stability of the water-isolated coal pillar under mining

Urinary miR-221-3p and miR-324-5p in combination with albuminuria as a promising model for non-invasive diagnosis of pediatric celiac disease

Additional effects of air anions on eco-friendly pest control against Tetranychus urticae and Aphis gossypii

The impact of tooth brushing simulation and staining thermocycling on surface roughness and color stability of CAD/CAM laminate veneers ceramic materials: an in-vitro study

Abstract This study investigated the influence of simulated brushing and staining thermocycling on surface roughness and color stability of different types of CAD/CAM laminate veneers ceramic materials. Forty-eight square-shaped specimens were created using two types of CAD/CAM glass ceramics ( ALDS , CEREC Tessera, Dentsply Sirona, and ZLS , Vita Suprinity, Vita Zahn Fabrik), each material group was divided into two subgroups ( N  = 12) according to surface finishing protocols. Surface roughness (Ra) and color parameters were measured initially, after 6 months and 1 year of brushing simulation and Coffee/Tobacco thermocycling. Data was collected and statistically analyzed using Wald-type ANOVA, and post-hoc simple effects of estimated marginal means were compared using Wald tests with Sidak adjustment. For surface roughness, all the main effects and their interactions were not statistically significant, according to ANOVA ( P  > 0.05). Color change results (∆E) showed that after one year of tooth brushing simulation and C/T thermocycling, Tessera finished subgroup had a significantly higher mean value (16.95 ± 3.06) than Vita Suprinity finished subgroup (6.67 ± 1.85) ( p  < 0.001), and the effect size was large, 0.51. While Tessera glazed subgroup had a higher non-significant mean value (7.03 ± 0.64) than Vita Suprinity glazed subgroup (6.06 ± 2.34), ( p  = 0.188), and the effect size was small, 0.02. All tested ceramics showed acceptable surface roughness values; however, tooth brushing combined with C/T thermocycling caused discoloration especially in finished Tessera CAD/CAM materials. The color change (∆E) of the two laminate veneer materials, under different surface treatments, exceeded the clinically acceptable range.