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Study on the transverse hardness distribution in strip-induced vibration of the Stand S4 cold rolling mill

Rootstock effects on floral induction in commercial Iranian almond cultivars: Insights from morphophysiological, biochemical, and molecular analyses

Orchard productivity in almond trees is strongly influenced by rootstock selection, which plays a key role in floral induction and yield optimization. Although several rootstocks are commonly used in Iran, their comparative effects on floral induction in major commercial cultivars remain poorly understood. This study evaluated five Peach × Almond hybrid rootstocks (GN15, GF677, GN15-M, Shurab2, and Shurab3) grafted with two widely grown Iranian cultivars, Mamaee and Shahrud12, to investigate rootstock–scion interactions. These combinations were chosen based on their commercial importance and regional adaptability. A four-year factorial experiment (2021–2025) was conducted in a completely randomized design. Morphological traits, including flower number, blooming density, and vegetative growth, were measured alongside photosynthetic pigments and endogenous hormone profiles. Additionally, the expression of flowering-related genes was analyzed in leaf and bud tissues. Results revealed that Shurab3 significantly enhanced floral induction in both cultivars, with the Shurab3–Shahrud12 combination producing the highest flower number and bloom density. Shurab3 also outperformed GN15 in promoting flowering in Mamaee, whereas the GN15–Mamaee combination showed the lowest performance. Rootstocks GF677, GN15-M, and Shurab2 exhibited intermediate effects. Shurab3 combinations were further associated with higher chlorophyll content, increased indole-3-acetic acid (IAA), and dynamic patterns of abscisic acid (ABA) and gibberellic acid (GA3). Molecular analyses confirmed upregulation of FLOWERING LOCUS T ( FT ), CONSTANS ( CO ), SUPPRESSOR OF OVEREXPRESSION OF CO 1 ( SOC1 ), LEAFY ( LFY ), and APETALA1 ( AP1 ) in Shurab3–Shahrud12, consistent with observed phenotypic improvements. Overall, these findings indicate that both rootstock and scion selection critically influence reproductive performance. Shurab3 emerges as a promising flower-inducing rootstock, providing practical guidance for optimizing orchard management and enhancing almond productivity under regional climatic conditions.

The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies

Abstract Han Chinese people comprise nearly 20% of the global population but remain under-represented in genetic studies 1,2 , so there is an urgent need for large-scale cohorts to advance precision medicine. Here we present the Taiwan Precision Medicine Initiative (TPMI), established by Academia Sinica in collaboration with 16 major medical centres around Taiwan, which has recruited 565,390 participants who consent to provide DNA samples for genetic profiling and grant access to their electronic medical records (EMRs) for research. EMR access is both retrospective and prospective, allowing longitudinal studies. Genetic profiling is done with population-optimized arrays of single-nucleotide polymorphisms for people of Han Chinese ancestry, which enable genome-wide association 3,4 , phenome-wide association 5,6 and polygenic risk score 7,8 studies to be performed to evaluate common disease risk and pharmacogenetic response. Participants also agreed to be re-contacted for future research and receive personalized genetic risk profiles with health management recommendations. The TPMI has established the TPMI Data Access Platform, a central database and analysis platform that both safeguards the security of the data and facilitates academic research. As a large cohort of individuals with non-European ancestry that merges genetic profiles with EMR data and enables longitudinal follow-up, TPMI provides a unique resource that could be used to validate genetic risk prediction models, perform clinical trials of risk-based health management and inform health policies. Ultimately, the TPMI cohort will contribute to global genetic research and serve as a model for population-based precision medicine.

Transthoracic echocardiography of left ventricular underfilling improves risk stratification in pulmonary arterial hypertension

Abstract Pulmonary arterial hypertension (PAH) is characterized by right ventricular (RV) adaptation to elevated afterload. However, the prognostic value of left ventricular (LV) underfilling in response to RV volume overload is unknown. We aimed to investigate the prognostic value of LV underfilling in PAH. Does Trans-thoracic echocardiography (TTE) assessed LV underfilling enhance prognostic accuracy in PAH beyond existing models such as REVEAL-Lite 2.0 and REVEAL-ECHO? 270 patients with PAH were prospectively enrolled. LV underfilling was defined as the LV volume-to-mass (V/M) ratio of < 0.8 ml/g. TTE and hemodynamic parameters were compared between patients with and without LV underfilling. The Cox regression model identified LV parameters significantly predictive of survival. The current LV model was developed incorporating LV parameters and was compared with established models, including REVEAL-Lite 2.0 and REVEAL-ECHO. Cox-regression and model performance metrics, including the C-index, Akaike Information Criterion (AIC), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI), were used to assess the model’s prognostic accuracy. Patients with LV underfilling exhibited more advanced disease, with higher hemodynamic indices and impaired RV function (all p < .0001). Over a median 29.8 months follow-up, LV underfilling was associated with worse survival (Log-rank p < .0001). LV underfilling (HR: 5.32, 95% CI; 3.05–9.28, p = < .0001), RV/LV-basal diameter (HR: 1.96, 95% CI; 1.066–3.624, p = 0.03), LV end-diastolic diameter (HR: 1.071, 95% CI: 1.030–1.114, p = 0.0005) served as independent predictors of adverse outcomes. The current LV model integrating LV parameters demonstrated superior discrimination (Mean C-index: 0.716 ± 0.06) vs REVEAL-Lite 2.0 (Mean C-index: 0.585 ± 0.07) and REVEAL-ECHO (Mean C-index: 0.717 ± 0.11). The model also demonstrated superior reclassification and discrimination performance compared to traditional models (NRI: 0.225, IDI: 2.28). TTE-assessed LV underfilling is a novel, valuable prognostic marker in PAH. The Current LV model, in addition to REVEAL-ECHO, offers enhanced prognostic capabilities for PAH management and may serve as a key tool in optimizing PAH patient care.

Brain tumour segmentation in fused MRI-PET images with permutate U-Net framework

Brain tumor segmentation from MRI’s and PET has always been a challenging and time-consuming phase for radiologists, due to low sensitivity boundary region pixels in this image modality. Deep learning-based image segmentation is the hot research topic in recent days. Among all other deep learning models, U-Net-based variants are the most used models to segment medical images with respect to different modalities. In this paper, a Permutate version of the U-Net architecture was designed that precisely and automatically detects the boundaries of the tumour area and segments tumour regions from the fused image. There are two stages to the proposed work. In the first stage Principal component analysis (PCA) is used to fuse the MRI-PET images to enhance the fused image’s quality and improved interpretation. Later, a Permutate U-Net architecture is employed to precisely segment tumour region from the fused image. Further designed model performance is assessed using Dice Coefficient, intersection over union score (IoU) and accuracy with brain tumour segmentation challenge BraTS datasets of 2015, 2020 and 2021. Our proposed method demonstrates promising results that are superior to existing deep learning model and comparatively higher than the existing methods.

RETRACTED ARTICLE: Towards improved fake news detection using a hybrid RoBERTa and metadata enhanced XGBoost model

Dung beetle assemblage changes along a chronosequence in a recovering tropical dry forest

Tropical dry forests are among the most threatened ecosystems globally, facing extensive degradation from land-use change. Understanding how biodiversity responds during forest regeneration is critical for conservation and sustainable land management. We assessed the community composition and functional diversity of dung beetles (Scarabaeinae) across a forest recovery chronosequence (1–100 years) in the Yucatán Peninsula. The study area is characterized by traditional Mayan agroforestry systems that shape the landscape. 90 pitfall traps were set up across six forest age classes and were collected 6,605 individuals from 23 species and 13 genera. Dung beetle species richness, biomass, and abundance were significantly associated with forest structural, and diversity metrics. Forest Shannon entropy ( H ′), inverse Simpson concentration ( IF ₀, ₂), and aboveground biomass emerged as strong predictors of community attributes. Abundance and biomass responses varied by functional group: small diurnal rollers (SRD) increased with land-use intensity, while large nocturnal rollers (LRN), large diurnal tunnellers (LTD), and small nocturnal tunnellers (STN) declined sharply from mature forests to early successional stages and agricultural areas. Species richness ( 0 D ) peaked in early to intermediate successional stages (5–20 years), whereas dominant species diversity ( 2 D ) was highest in mixed-use forests under moderate disturbance. Distance-based redundancy analysis (db-RDA) and multi-model inference revealed that forest attributes—including DBH, aboveground biomass, canopy openness, and litter depth—jointly explained 48.7% of the variation in dung beetle assemblage structure (p < 0.001). Litter volume was positively correlated with species richness (adj. R 2  = 0.76), and IF₀,₂ was a key predictor of biomass (adj. R 2  = 0.62). Our findings reveal threshold-based and trait-mediated responses of dung beetle assemblages to forest succession, highlighting the ecological importance of bioculturally managed landscapes. These results underscore the role of secondary forests in maintaining biodiversity and ecosystem functions, supporting their conservation as vital components of tropical dry forest recovery.

Temporally consistent tri ledger settlement enables robust and noncontestable coordination in interprovincial power systems

microRNA-184 distribution and consequences on glial septate junctions and the blood-brain barrier

Cellular permeability barriers restrict the diffusion of solutes, pathogens, and cells across tissues. In Drosophila melanogaster , septate and tricellular junctions create permeability barriers in epithelia and the blood-brain barrier in glia. In vitro , and in vivo studies in the epithelia of the Drosophila wing imaginal discs identified microRNA-184 (miR-184) as a potential regulator of a subset of pleated septate and tricellular junction proteins. However, which tissues express miR-184, and the consequences of miR-184 expression on the blood-brain barrier has not been examined. Using a miR-184 sensor, we found that miR-184 is absent in tissues with pleated septate junctions but is present in tissues with smooth septate junctions. When expressed in the subperineurial glia that form the blood-brain barrier, miR-184 resulted in the loss of targeted septate junction proteins, a compromised blood-brain barrier, decreased locomotion, and lethality. Interestingly, qRT-PCR analysis revealed that miR-184 expression did not alter mRNA levels of targeted genes. Conversely, expression of miR-184 led to an increase in the mRNA and expression of the non-target Nervana2 protein. Thus, mRNA-184 can regulate multiple pleated septate junction proteins either directly through loss of translation or indirectly by disruption of the septate junction domain.

‘Anti-woke’ policies blamed for falling attendance at some US conferences

Photobiomodulation stimulates mitochondrial function and cell proliferation in meniscus-derived stem cells (MeSCs) via activation of TRPV1 channel

Preeclampsia, prevalence and associated factors

Background Preeclampsia (PE) is a significant obstetric complication associated with adverse maternal and fetal outcomes. Zambia, like several Sub-Saharan African nations, experiences a high prevalence of pregnancy-related hypertension, with PE being a major contributor to maternal and foetal mortality. This study aimed to identify the factors associated with the development of PE. Methods We conducted a cross-sectional study at Livingstone University Teaching Hospital in Zambia (LUTH). PE was defined as new-onset hypertension with systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg occurring after 20 weeks of gestation, accompanied by proteinuria with dipstick reading of 1 + . Data from patients’ most recent hospital visits was collected by trained research assistants using medical record abstraction. A total of 1018 participants were included. Demographic, clinical, and haematological parameters were analysed. We conducted both descriptive and inferential analyses using Stata version 17. Univariable and multivariable logistic regression were employed to investigate factors associated with PE. Results The median age of participants was 27 years (IQR: 21–33). The prevalence of PE was 12.2% (n = 124). Among the 17.7% (n = 172) of participants who were employed, 19.1% (n = 33) had PE. Factors positively associated with PE included increasing age (Adjusted Odds Ratio [AOR]: 1.07, 95% Confidence Interval [CI]: 1.02–1.12, p = 0.002), a previous history of PE (AOR: 30.8, 95% CI: 8.7–108.6, p < 0.001), and a family history of PE (AOR: 9.97, 95% CI: 2.53–39.27, p = 0.001). Conversely, a unit (week) increase in gestational age was negatively associated with PE (AOR: 0.89, 95% CI: 0.83–0.97, p = 0.007). Conclusion This study identifies maternal age, family history, and prior obstetric history as key associated factors for PE, emphasizing the need for targeted screening and early intervention. Enhanced prenatal care, including routine risk assessments, patient education, and regular monitoring through blood pressure checks, urine protein testing, and fetal growth assessments, is cardinal for early detection and effective management of high-risk individuals.

Seasonal predictability of an indicator of mass coral bleaching between the Pacific ocean and the East China sea with a large ensemble climate model

Genome-wide identification and comparative analysis of strigolactone biosynthetic genes in major solanaceous crops

Strigolactones (SLs) are a class of important plant hormones that not only regulate plant growth and development but also mediate responses to biotic and abiotic stresses. Functioning as signaling molecules derived from the rhizosphere, SLs play a pivotal role in inducing the germination and facilitating the parasitism of root holoparasites such as Orobanche spp. Notably, the parasitism success of Orobanche exhibits marked variation within Solanaceous crops, demonstrating dual dependence on SL exudation dynamics and edaphic phosphate bioavailability. We performed a genome-wide characterization of SL biosynthetic orthologs across nine Solanaceous species through integrated phylogenomic pipelines alongside qRT-PCR expression profiling. In this study, we identified 113 putative SL biosynthetic orthologs across wolfberry, nightshade, tomato, eggplant, petunia, tobacco, pepper, groundcherry and potato, revealing both deep evolutionary conservation and lineage-specific diversification patterns. Phosphate deprivation significantly induced the upregulation of SL biosynthetic genes in tomato and pepper via qRT-PCR analysis, confirming that phosphorus deficiency acts as a key stimulator of SL biosynthesis. In general, this study delineates the repertoire of potential SL biosynthesis-related genes across major Solanaceous species, revealing phylogenetic conservation and clade-specific diversification.

Bioinformatics analysis of immune-related differentially expressed genes in Kawasaki disease

Hydration product phase evolution and mortar strength development in alkali-activated slag and fly ash systems

Alkali-activated geopolymer materials, derived predominantly from industrial byproducts such as fly ash and slag, represent a sustainable alternative to Portland cement for applications including anti-seepage grouting, road construction, and high-strength concrete. This study systematically investigates the hydration behavior of slag and fly ash activated by NaOH and Ca(OH)₂ at dosages of 4%, 6%, and 8%, with the constraint that the initial setting time is ≥ 45 min and the final setting time is ≤ 600 min. The mechanical properties of the resultant mortar systems were evaluated using standardized strength testing (ISO method) at curing ages of 3, 7, and 28 days. The phase composition and microstructural evolution of hydration products were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), backscattered electron image analysis (BSE-IA), and isothermal calorimetry. These analytical techniques provided comprehensive insights into the morphology, phase distribution, porosity, and hydration kinetics of the reaction products. The results revealed distinct activator-dependent reactivity trends: NaOH demonstrated higher efficiency in activating slag, whereas Ca(OH)₂ was more effective in promoting the hydration of fly ash. Optimal hydration was achieved with 8% NaOH for slag and 6% Ca(OH)₂ for fly ash, leading to enhanced reaction completeness, increased hydration product formation, denser pore structures, and significantly improved mechanical properties. Alkali-activated slag exhibited substantially greater strength enhancement than fly ash. The 28-day compressive strengths reached 35.94 MPa and 6.65 MPa for slag- and fly ash-based mortars, respectively, with corresponding flexural strengths of 10.23 MPa and 1.92 MPa. These findings demonstrate that the properties of alkali-activated geopolymer materials can be effectively tailored through the strategic selection of alkaline activator type and dosage. This study provides both theoretical insights and technical guidance for the development of sustainable alkali-activated geopolymer materials in construction applications.

Spatial and environmental drivers of Varroa destructor detection in New South Wales, Australia

Abstract Varroa destructor  is a major global threat to apiculture. Its recent detection in New South Wales (NSW), Australia, triggered an eradicative response followed by a transition to ongoing management. To identify factors influencing the probability of Varroa presence across apiaries in NSW, we developed a Bayesian hierarchical logistic regression model, incorporating surveillance data alongside climatic and environmental covariates. Our analysis revealed that detection probability was higher in the eradication emergency zone compared to the general emergency zone and more likely during the summer than winter. Maximum summer temperature was positively associated with Varroa presence, while minimum winter humidity was negatively associated with Varroa presence. Surveillance methods also influenced detection probabilities, with sticky traps showing higher probabilities than sugar shake methods. Public reports were associated with higher detection probabilities compared to inspections by authorised officers. The probability of detection was lower in areas with registered beekeepers within a 50-km radius compared to areas without registered beekeepers. We observed residual spatial cluster in Varroa distribution across the Sydney Basin and extending into the Central Tablelands, suggesting the influence of unmeasured risk factors. These findings highlight the need for targeted surveillance during high-risk seasons and in identified hotspot regions, supported by the wider use of sensitive detection methods and stronger community engagement, to improve early detection and sustainable management of Varroa in Australia.

Correction: Effect of a loss of the mda5/ifih1 gene on the antiviral resistance in a Chinook salmon Oncorhynchus tshawytscha cell line

We are all mosaics: vast genetic diversity found between cells in a single person

Wave masking enhances electrocardiogram reconstruction with linear regression

Abstract Electrocardiogram (ECG) reconstruction involves synthesizing leads from a reduced or alternative lead set. While ECG leads are generally considered linearly related, recording distortions and individual differences make perfect replication difficult, leading researchers to explore deep learning (DL) methods. This paper challenges DL methods by introducing wave masking, a novel preprocessing technique adapted from image recognition, where sections of the input are masked to highlight segments most relevant to improving reconstruction. Applied to ECG, it emphasizes key parts of the time-series signal. The study compares the performance of wave masking combined with linear regression against traditional preprocessing for both linear and DL models, using 10,000 normal ECG records from the CODE-15% database (trimmed to 10 s, resampled to 500 Hz, and denoised). Results show mean correlation values of 0.869 ± 0.201 for the linear pipeline, 0.880 ± 0.190 for the wave masking pipeline, and 0.894 ± 0.168 for the DL pipeline. Wave masking significantly improves linear regression performance by over 0.01 and produces results comparable to DL models, though not superior. These findings highlight wave masking as a promising, low computation preprocessing step for ECG reconstruction. Further research is needed to explore its potential benefits when integrated with deep learning models and diverse demographic records.