Advancing Open Science

Sleep-disordered breathing (SDB) is common after stroke and may negatively influence recovery, yet it is frequently underdiagnosed. Portable respiratory monitoring devices could facilitate early SDB screening in these patients. We estimated the prevalence of sleep apnea (SA) using a smartphone-based monitoring system in post-stroke patients and examined associations between respiratory indices, stroke severity and disability (NIHSS, mRS), and rehabilitation outcomes (motor and cognitive Functional Independence Measure; FIM). Consecutive patients admitted to inpatient rehabilitation within three months after a stroke underwent an overnight assessment with a smartphone-based respiratory monitoring device, which estimated the apnea–hypopnea index (AHI), mean and minimum SpO₂, time with SpO₂ < 94% and <90%, and hourly oxygen desaturation events (≥3% and ≥4%). Of the 104 screened patients, 59 were recruited, while 56 had valid recordings. Most patients (89%) had previously undiagnosed SA: 11% mild (AHI ≥ 5 and <15), 38% moderate (AHI ≥ 15 and <30), and 41% severe (AHI ≥ 30). Greater event burden and nocturnal hypoxemia were associated with older age, worse baseline disability (mRS), lower admission motor FIMs, and poorer rehabilitation metrics. Smartphone-based portable monitoring is an accessible, easy-to-use approach that may enable earlier identification of SA, particularly in individuals with substantial hypoxemia or respiratory event burden.

Parametric tiered-seating design can be framed as a constrained multi-objective optimization problem in which a low-dimensional decision vector is evaluated by a deterministic operator with sequential feasibility rejection and visibility constraints. This study introduces an oracle-preserving, learning-assisted screening workflow, where a multi-output multilayer perceptron (MLP) is used only to prioritize candidates for evaluation. Here, multi-output denotes a single network trained to predict the full objective vector jointly. Candidates are sampled within bounded decision ranges and evaluated by an operator that propagates section-coupled geometric state and enforces hard clearance thresholds through a Vertical Sightline System (VSS), i.e., a deterministic row-wise sightline/clearance evaluator that enforces hard clearance thresholds. The oracle-evaluated set is reduced to its mixed-direction Pareto-efficient subset and filtered by feature-space proximity to a fixed validation reference using nearest-neighbor distances in standardized 11-dimensional features, yielding a robustness-oriented pool. A compact shortlist is derived via TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution; used here strictly as a post-Pareto decision-support ranking rule), and preference uncertainty is assessed by Monte Carlo weight sampling from a symmetric Dirichlet distribution. In an archived run under a fixed oracle budget, 1235 feasible designs are evaluated, producing 934 evaluated Pareto solutions; proximity filtering retains 187 robust candidates and TOPSIS reports a traceable top-30 shortlist. Stability is supported by concentrated top-k frequencies under weight perturbations and by audits under single-feature-drop ablations and tested rounding precisions. Overall, the workflow enables reproducible multi-objective screening and reporting for feasibility-dominated seating design.

This study addresses the issue of nonfragile state estimation for fractional-order memristive neural networks with time-varying delays under an adaptive event-triggered mechanism. Possible gain perturbations of the estimator are considered. A Bernoulli-distributed random variable is introduced to model the stochastic nature of gain fluctuations. The primary objective is to develop a nonfragile estimator that accurately estimates the network states. By means of Lyapunov functionals and fractional-order Lyapunov methods, two delay and order-dependent sufficient criteria are established to guarantee the mean-square stability of the augmented system. Finally, the effectiveness of the proposed estimation scheme is demonstrated through two simulation examples.

Taraxacum kok-saghyz (Tks) is a promising plant species for natural rubber (NR) production and represents a model for studying NR biosynthesis in the Asteraceae family. The generation of transgenic plants overexpressing a gene of interest is a well-established strategy to investigate gene function and potential interactions. Here, we present a comprehensive workflow—from the construction of an overexpression vector to the generation, identification, and propagation of stable transgenic Tks lines. In addition, we describe a rapid and reliable method for quantifying NR content in transformed plants, providing essential phenotypic characterisation in this species.

Deep learning models based on supervised learning rely heavily on large annotated datasets and particularly in the context of medical image segmentation, the requirement for pixel-level annotations makes the labeling process labor-intensive, time-consuming and expensive. To overcome these limitations, self-supervised learning (SSL) has emerged as a promising alternative that learns generalizable representations from unlabeled data; however, existing SSL frameworks often employ highly parameterized encoders that are computationally expensive and may lack robustness in label-scarce settings. In this work, we propose a scattering-based SSL framework that integrates Wavelet Scattering Networks (WSNs) and Parametric Scattering Networks (PSNs) into a Bootstrap Your Own Latent (BYOL) pretraining pipeline. By replacing the initial stages of the BYOL encoder with fixed or learnable scattering-based front-ends, the proposed method reduces the number of learnable parameters while embedding translation-invariant and small deformation-stable representations into the SSL pipeline. The pretrained encoders are transferred to a U-Net and fine-tuned for cardiac image segmentation on two datasets with different imaging modalities, namely, cardiac cine MRI (ACDC) and cardiac CT (CHD), under varying amounts of labeled data. Experimental results show that scattering-based SSL pretraining consistently improves segmentation performance over random initialization and ImageNet pretraining in low-label regimes, with particularly pronounced gains when only a few labeled patients are available. Notably, the PSN variant achieves improvements of 4.66% and 2.11% in average Dice score over standard BYOL with only 5 and 10 labeled patients, respectively, on the ACDC dataset. These results demonstrate that integrating mathematically grounded scattering representations into SSL pipelines provides a robust and data-efficient initialization strategy for cardiac image segmentation, particularly under limited annotation and domain shift.

Objectives: Although sex differences have been emphasized in stroke and congenital heart disease, there has been limited investigation into their role in patent foramen ovale (PFO) closure for secondary prevention of stroke. We aimed to explore differences by sex in baseline profiles, procedural characteristics, and short-term outcomes of patients undergoing transcatheter PFO closure. Methods: A retrospective analysis was conducted on 458 consecutive patients (265 women and 193 men) treated with PFO closure at Centro Cardiologico Monzino in Milan between 2006 and 2011. Baseline information included demographic characteristics, medical history, diagnostic and procedural information, and periprocedural complications. Post-closure outcomes were assessed at index hospitalization and during the first follow-up. Results: The indications for PFO closure were as follows: cryptogenic stroke/TIA in 78% of women vs. 88% of men (p = 0.04). Positive thrombophilic screening was observed in 16% of women vs. 19% of men (non-significant). We observed age-matched (mean age 44 ± 12 years) patients without sex-related differences in baseline and procedural characteristics, with the exception of greater arterial hypertension in women. The mean follow-up time was 13 years for both groups. Recurrent stroke was observed in 0.1% and TIA observed in 0.4% of the ‘cryptogenic stroke/TIA’ group; in the ‘other indications’ group, 1.4% experienced stroke and no TIA was reported. No significant differences were present between sexes. Conclusions: There were no differences in procedural and short-term outcomes between males and females undergoing transcatheter PFO closure, but significant baseline differences in risk factors were identified. There is a critical need for long-term, systematic studies to understand sex and gender differences in the PFO population.

Bump feeding is a nutritional management strategy in swine production that involves increasing feed allowance and/or dietary nutrient density during the final weeks of gestation, usually from day 90 to farrowing, to support rapid fetal growth and prepare sows for lactation. This strategy is widely applied to improve piglet birth weight, neonatal viability, and subsequent reproductive performance. This review synthesizes current evidence on the effects of increased maternal feed intake during late gestation on sow body condition and feeding-related behavioral responses, and farrowing outcomes. Available studies suggest that increasing feed allowance during late gestation can influence litter characteristics, piglet survival at birth, and sow energy reserves, as reflected by changes in backfat thickness (BFT) and body condition score (BCS). The nutritional composition of bump-feeding diets, including dietary energy and amino acid balance, is critically evaluated in relation to pregnancy maintenance, farrowing duration, and early lactation performance. In addition, the roles of parity and feeding behavior during late gestation are examined, with particular emphasis on their associations with sow activity patterns, restlessness around parturition, and farrowing efficiency. Despite these reported effects, findings across studies remain inconsistent, particularly regarding the balance between improved reproductive outcomes and the risk of excessive fat deposition in sows. This review highlights key knowledge gaps and underscores the need for optimized, parity-specific bump-feeding strategies that integrate nutritional management with feeding behavior to enhance farrowing performance, piglet survival, sow welfare, and economic sustainability in modern pig production.

This study aims to investigate the predictive value of pre-treatment multi-phasic contrast-enhanced computed tomography (CECT) radiomic features for treatment resistance in patients with rat sarcoma virus (RAS)-mutated colorectal liver metastases (CRLMs) receiving bevacizumab-based chemotherapy. Seventy-three samples with RAS-mutated CRLMs receiving bevacizumab-combined chemotherapy regimens were evaluated. Radiomic features were extracted from arterial phase (AP), portal venous phase (PVP), AP-PVP subtraction image, and Delta phase (DeltaP, calculated as AP-to-PVP ratio) images. Three groups of radiomics features were extracted for each phase, including peritumor, core tumor, and whole-tumor regions. For each of the four phases, a two-sided independent Mann–Whitney U test with the Bonferroni correction and K-means clustering was applied to the remnant features for each phase. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was then applied for further feature selection. Six machine learning algorithms were then used for model development and validated on the independent testing cohort. Results showed peritumoral radiomic features and features derived from Laplacian of Gaussian (LoG) filtered images were dominant in all the compared machine learning algorithms; NB models yielded the best-performing prediction (Avg. training AUC: 0.731, Avg. testing AUC: 0.717) when combining all features from different phases of CECT images. This study demonstrates that peritumoral radiomic features and LoG-filtered pre-treatment multi-phasic CECT images were more predictive of treatment response to bevacizumab-based chemotherapy in RAS-mutated CRLMs compared to core tumor features.