bluuebunny/crossref_metadata_2025_split huggingface dataset:
Human umbilical cord mesenchymal stem cells improve uterine incision healing after cesarean delivery in rats by modulating the TGF-β/Smad signaling pathway
Authors: Sun Qing , Zhang Dan , Ai Qiuying , Yue Yang , Wang Haijiao , Tang Le , Yi Xiling , Wang Siyuan , Zheng Yang 
Published: February 2024

Abstract
Objective
Although human umbilical cord-derived mesenchymal stem cells (HU-MSCs) have attracted increasing attention because of their pivotal functions in the process of wound healing, the underlying molecular mechanisms have been poorly understood. It has been shown that the TGF-β/Smad signaling pathway plays an important role in the process of scar formation. The present study focused on exploring whether HU-MSCs improve uterine incision healing after cesarean delivery in rats via the TGF-β/Smad signaling pathway.

Study Design
Pregnant rats were randomly assigned to three groups, including the NP group, incision-injected group (HU-MSCs1 group), and tail vein-injected group (HU-MSCs2 group), and 30 days after cesarean section, sampling was carried out to further explore the specific mechanisms from tissue and protein levels.

Results
HU-MSCs secretion could inhibit the fibrosis of scar tissue. We observed that the TGF-β induced expression of TGF-β1, Smad2, and Smad3 was attenuated upon HU-MSCs treatment in scar tissue, while the decrease in TGF-β3 expression was enhanced by HU-MSCs. Furthermore, HU-MSCs treatment accelerated wound healing and attenuated collagen deposition in a damaged uterine rat model, leading to the promoting of uterine incision scarring. In addition, the expression of alpha-smooth muscle actin (a-SMA) was enhanced by HU-MSCs treatment.

Conclusion
HU-MSCs transplantation promotes rat cesarean section uterine incision scar healing by modulating the TGF-β/Smad signaling pathway.
Prodromal multiple sclerosis: considerations and future utility
Authors: Harding Katharine E. , Kreft Karim L. , Ben-Shlomo Yoav , Robertson Neil P. 
Published: February 2024

AbstractA multiple sclerosis (MS) prodrome has recently been described and is characterised by increased rates of healthcare utilisation and an excess frequency of fatigue, bladder problems, sensory symptoms and pain, in the years leading up to clinical onset of disease. This important observation may have several potential applications including in the identification of risk factors for disease, the potential to delay or prevent disease onset and early opportunities to alter disease course. It may also offer possibilities for the use of risk stratification algorithms and effective population screening. If standardised, clearly defined and disease specific, an MS prodrome is also likely to have a profound influence on research and clinical trials directed at the earliest stages of disease. In order to achieve these goals, it is essential to consider experience already gleaned from other disorders. More specifically, in some chronic neurological disorders the understanding of disease pro-drome is now well advanced and has been successfully applied. However, understanding of the MS prodrome remains at an early stage with key questions including the length of the prodrome, symptom specificity and potential benefits of early intervention as yet unanswered. In this review we will explore the evidence available to date and suggest future research strategies to address unanswered questions. In addition, whilst current understanding of the MS prodrome is not yet sufficient to justify changes in public health policy or MS management, we will consider the practical utility and future application of the MS prodrome in a wider health care setting.
Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography
Authors: Lal Utkarsh , Chikkankod Arjun Vinayak , Longo Luca 
Published: February 2024

AbstractParkinson’s disease (PD) is an incurable neurological disorder that degenerates the cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) signal analysis can provide reliable information regarding PD conditions. However, EEG is a complex, multichannel, and nonlinear signal with noise that problematizes identifying PD symptoms. A few studies have employed fractal dimension (FD) to extract distinguishing PD features from EEG signals. However, no exploratory study exists, as per our knowledge, on the efficiency of the different FD measures. We aim to conduct a comparative analysis of the various FDs that, as feature extraction measures, can discriminate PD patients who are ON and OFF medication from healthy controls using ML architecture. This study has implemented and analyzed several techniques for segmentation, feature extraction, and ML models. The results show that k-nearest neighbors (KNN) classifier with Higuchi FD and 90% overlap for segmented window delivers the highest accuracies, yielding a mean accuracy of $$99.65\pm 0.15\%$$

99.65
±
0.15
%

for PD patients ON medication and $$99.45\pm 0.18\%$$

99.45
±
0.18
%

for PD patients OFF medication, respectively. The model accurately identifies the signs of the disease in resting-state EEG with almost equivalent accuracy in both OFF and ON medication patients. To enhance the interpretability in our study, we leveraged XGB’s feature importance to generate brain topographic plots. This integration of explainable AI (XAI) enhanced the transparency and comprehensibility of our model’s classifications. Additionally, a comparison between the performance of FD and a few entropy measures has also been drawn to validate the significance of FD as a superior feature extraction measure. This study contributes to the body of knowledge with an architectural pipeline for detecting PD in resting-state EEG while emphasizing fractal dimension as an effective way of extracting salient features from EEG signals.
A fast in silico model for preoperative risk assessment of paravalvular leakage
Authors: Spanjaards Michelle , Borowski Finja , Supp Laura , Ubachs René , Lavezzo Valentina , van der Sluis Olaf 
Published: February 2024

AbstractIn silico simulations can be used to evaluate and optimize the safety, quality, efficacy and applicability of medical devices. Furthermore, in silico modeling is a powerful tool in therapy planning to optimally tailor treatment for each patient. For this purpose, a workflow to perform fast preoperative risk assessment of paravalvular leakage (PVL) after transcatheter aortic valve replacement (TAVR) is presented in this paper. To this end, a novel, efficient method is introduced to calculate the regurgitant volume in a simplified, but sufficiently accurate manner. A proof of concept of the method is obtained by comparison of the calculated results with results obtained from in vitro experiments. Furthermore, computational fluid dynamics (CFD) simulations are used to validate more complex stenosis scenarios. Comparing the simplified leakage model to CFD simulations reveals its potential for procedure planning and qualitative preoperative risk assessment of PVL. Finally, a 3D device deployment model and the efficient leakage model are combined to showcase the application of the presented leakage model, by studying the effect of stent size and the degree of stenosis on the regurgitant volume. The presented leakage model is also used to visualize the leakage path. To generalize the leakage model to a wide range of clinical applications, further validation on a large cohort of patients is needed to validate the accuracy of the model’s prediction under various patient-specific conditions.
Limitations of neural network training due to numerical instability of backpropagation
Authors: Karner Clemens , Kazeev Vladimir , Petersen Philipp Christian 
Published: February 2024

AbstractWe study the training of deep neural networks by gradient descent where floating-point arithmetic is used to compute the gradients. In this framework and under realistic assumptions, we demonstrate that it is highly unlikely to find ReLU neural networks that maintain, in the course of training with gradient descent, superlinearly many affine pieces with respect to their number of layers. In virtually all approximation theoretical arguments which yield high order polynomial rates of approximation, sequences of ReLU neural networks with exponentially many affine pieces compared to their numbers of layers are used. As a consequence, we conclude that approximating sequences of ReLU neural networks resulting from gradient descent in practice differ substantially from theoretically constructed sequences. The assumptions and the theoretical results are compared to a numerical study, which yields concurring results.
Automatic fruit picking technology: a comprehensive review of research advances
Authors: Zhang Jun , Kang Ningbo , Qu Qianjin , Zhou Lianghuan , Zhang Hongbo 
Published: February 2024

AbstractIn recent years, the fruit industry has become an important part of agricultural development, and fruit harvesting is a key stage in the production process. However, picking fruits during the harvest season is always a major challenge. In order to solve the challenges of time-consuming, costly, and inefficient fruit picking, researchers have conducted a lot of studies on automatic fruit picking equipment. Existing picking technologies still require further research and development to improve efficiency and reduce fruit damage. Aiming at the efficient and non-destructive picking of fruits, this paper reviews machine vision and mechanical fruit picking technology and the current research status, including the current application status, equipment structure, working principle, picking process, and experimental results. As a promising tool, machine vision technology has been widely researched and applied due to its low hardware cost and rich visual information. With the development of science and technology, automated fruit picking technology integrates information technology, integrates automatic perception, transmission, control, and operation, etc., saves manpower costs, and continuously promotes the development of modern agriculture in the direction of refinement of equipment technology, automation, and intelligence. Finally, the challenges faced by automated fruit picking are discussed, and future development is looked forward to with a view to contributing to its sustainable development.
A Genetically Informed Study of the Association Between Perceived Stress and Loneliness
Authors: Moshtael Ryan , Lynch Morgan E. , Duncan Glen E. , Beam Christopher R. 
Published: February 2024

AbstractAlthough research shows a strong positive association between perceived stress and loneliness, the genetic and environmental etiology underlying their association remains unknown. People with a genetic predisposition to perceived stress, for example, may be more prone to feeling lonely and vice versa. Conversely, unique factors in people’s lives may explain differences in perceived stress levels that, in turn, affect feelings of loneliness. We tested whether genetic factors, environmental factors, or both account for the association between perceived stress and loneliness. Participants were 3,066 individual twins (nFemale = 2,154, 70.3%) from the Washington State Twin Registry who completed a survey during April–May, 2020. Structural equation modeling was used to analyze the item-level perceived stress and loneliness measures. The correlation between latent perceived stress and latent loneliness was .68. Genetic and nonshared environmental variance components underlying perceived stress accounted for 3.71% and 23.26% of the total variance in loneliness, respectively. The genetic correlation between loneliness and perceived stress was .45 and did not differ significantly between men and women. The nonshared environmental correlation was .54 and also did not differ between men and women. Findings suggest that holding constant the strong genetic association between perceived stress and loneliness, unique life experiences underlying people’s perceived stress account for individual differences in loneliness.
A Taxonomy of UK Reaction-to-Fire Standard Testing: The Role of Black Boxing of Expertise
Authors: Law Angus , Spinardi Graham , Bisby Luke 
Published: February 2024

AbstractFire testing enables an individual or an organisation to make a claim about how a material, product, or system will perform in operational use. This paper describes and analyses the various reaction-to-fire tests that have used over the last 100 years in the UK. By analysing the commonalities and differences between these tests we propose a ‘taxonomy of testing’. We suggest that tests may be classified by the degree to which users may unthinkingly apply the results—without leading to negative fire safety outcomes. We propose three categories: unrepresentative tests; model tests; and technological proof tests. Unrepresentative tests are those which do not mimic building fire scenarios, but have thresholds so conservative that users need not consider whether the test was applicable to their intended application. Model tests are those based on ‘models’ of expected fire scenarios—users must therefore be confident that the model is sufficiently similar to their application. Technological proof tests are those which provide a more realistic test of a real building system—users must carefully analyse the similarities between their test and the real building before applying the results. From this we conclude that where user competence is low, policymakers should cite only unrepresentative (and conservative tests) within their guidance. Conversely where user competence is high, policy makers may more safety cite model or technological proof tests. The kinds of tests that may be safely cited in guidance are therefore indelibly linked to the expertise of the user.
Silicone rubber thermal aging performance for cables and accessories
Authors: Su Dandan 
Published: February 2024

AbstractPolymer-based semiconductor materials are mainly used for cable body and cable accessories to ensure long-term stability of power cables. The physical and chemical properties of the semi-conductive shielding layer of the cable will change due to aging. The new silicone material has excellent insulation and mechanical properties and is widely used in cable accessories. In this paper, the insulation materials—silicon rubber is studied. First, the silicone rubber sample is prepared. Then, the mechanical and dielectric properties of the samples before and after aging are tested. The results show that thermal aging has a significant effect on the characteristic parameters of silicone rubber. With the increase of aging time, the hardness and elastic modulus of the silicone rubber sample gradually increased, while the tensile strength and elongation at break significantly decreased with the increase of aging time. With the increase of aging time, the thermal stability of the silicone rubber sample decreases, the cross-linking degree of the system increases, and the organic property decreases. The measurement and analysis of thermal aging characteristics of silicone rubber can help to reduce the failure frequency of cable accessories and improve the safety.
Cannabidiol-loaded microparticles embedded in a porous hydrogel matrix for biomedical applications
Authors: David Carla , de Souza Jaqueline F. , Silva Adriana F. , Grazioli Guillermo , Barboza Andressa S. , Lund Rafael G. , Fajardo André R. , Moraes Rafael R. 
Published: February 2024

AbstractIn this study, poly (lactic-co-glycolic acid) (PLGA) microparticles loaded with cannabidiol (CBD) were synthesized (PLGA@CBD microparticles) and embedded up to 10 wt% in a chondroitin sulfate/polyvinyl alcohol hydrogel matrix. In vitro chemical, physical, and biological assays were carried out to validate the potential use of the modified hydrogels as biomaterials. The microparticles had spherical morphology and a narrow range of size distribution. CBD encapsulation efficiency was around 52%, loading was approximately 50%. Microparticle addition to the hydrogels caused minor changes in their morphology, FTIR and thermal analyses confirmed these changes. Swelling degree and total porosity were reduced in the presence of microparticles, but similar hydrophilic and degradation in phosphate buffer solution behaviors were observed by all hydrogels. Rupture force and maximum strain at rupture were higher in the modified hydrogels, whereas modulus of elasticity was similar across all materials. Viability of primary human dental pulp cells up to 21 days was generally not influenced by the addition of PLGA@CBD microparticles. The control hydrogel showed no antimicrobial activity against Staphylococcus aureus, whereas hydrogels with 5% and 10% PLGA@CBD microparticles showed inhibition zones. In conclusion, the PLGA@CBD microparticles were fabricated and successfully embedded in a hydrogel matrix. Despite the hydrophobic nature of CBD, the physicochemical and morphological properties were generally similar for the hydrogels with and without the CBD-loaded microparticles. The data reported in this study suggested that this original biomaterial loaded with CBD oil has characteristics that could enable it to be used as a scaffold for tissue/cellular regeneration.
Graphical Abstract
Aristotelian Flourishing and Contemporary Philosophical Theories of Wellbeing
Authors: Symons Xavier , VanderWeele Tyler 
Published: February 2024

AbstractSeveral philosophical theories of wellbeing claim Aristotelian ancestry and employ an Aristotelian construct of flourishing. Yet it is not clear how we should interpret Aristotle’s notion of flourishing or eudaimonia (εὐδαιμονία). In this article, we offer an analysis of Aristotle’s notion of eudaimonia and consider to what extent it can be categorized within the framework of contemporary philosophical theories of wellbeing. We stress the active character of Aristotelian flourishing and its focus on the development of living things over time, which contrasts with the static and passive character of contemporary philosophical accounts of wellbeing. We suggest that Aristotle is closest to a perfectionist theory of wellbeing, though even this requires some qualification. While rational activity in accord with virtue is central to Aristotle’s account of living well, Aristotle also states that a range of practical human activities and propitious life circumstances are necessary (and, on some interpretations, constitutive) features of the flourishing life. As such, Aristotelian flourishing includes elements of moral and cognitive perfectionism as well as an objective list of external goods and presupposes one’s embeddedness within communities. We close with a consideration of the implications of our argument for the philosophical and social scientific study of flourishing. Contemporary well-being philosophers should be mindful of ways in which Aristotelian eudaimonia extends beyond the dominant philosophical categories of wellbeing that have a static focus and concern themselves with subjective experience or the possession of objective goods. Social scientists should be transparent about the limits of invoking an Aristotelian pedigree to motivate their own theories of wellbeing and should understand the implications of more limited conceptions.
Did the COVID-19 pandemic influence access to green spaces? Results of a literature review during the first year of pandemic
Authors: González-Marín Alicia , Garrido-Cumbrera Marco 
Published: February 2024

Abstract
Context
The restrictions imposed in response to the COVID-19 pandemic disrupted the relationship between humans and their environment. The studies analyzed focus mainly on urban spaces during the first year of the COVID-19 pandemic.

Objectives
The aim of the present study was to assess the changes that the COVID-19 pandemic has caused in the relation between humans and nature, including the view, use, and perception of green spaces.

Methods
This review was performed by keyword searches in the main bibliographic repositories. We included all studies that examined the relationship between green spaces and nature during the COVID-19 pandemic. The search was restricted to studies published in English from the end of 2019 until the end of March 2021. Following the inclusion criteria, a total of 31 articles were analyzed.

Results
The COVID-19 pandemic has modified the relationship between population and green spaces. In countries with fewer restrictions, some travel was permitted, and green spaces could be visited. Countries with more severe restrictions, such as Spain and Italy, registered a significant reduction in the use of green spaces, especially during home confinement. The issues addressed by most studies analyzed include variations in access and use to green space and its economic inequalities, the influence of views of green space on well-being and the associations between COVID-19 rates and availability of green spaces from the beginning of the pandemic until March 2021.

Conclusion
The different government responses to COVID-19 have influenced the access to and use of green spaces, showing an increase in the exposure and appreciation of green spaces. Green spaces have increased the resilience of both cities and their residents, especially during the pandemic lockdown.
Refining intra-patch connectivity measures in landscape fragmentation and connectivity indices
Authors: Justeau-Allaire Dimitri , Ibanez Thomas , Vieilledent Ghislain , Lorca Xavier , Birnbaum Philippe 
Published: February 2024

Abstract

Context

Measuring intra-patch connectivity, i.e. the connectivity within a habitat patch, is important to evaluate landscape fragmentation and connectivity. However, intra-patch connectivity is mainly measured with patch size, which can conceal diverse intra-patch connectivity patterns for similar patch size distributions.

Objectives

We suggest a method to refine the intra-patch connectivity component of fragmentation and connectivity indices. This method allows for distinguishing different intra-patch connectivity patterns for similar patch size distributions.

Methods

We used normalized patch complexity indices to weight patch size in common fragmentation and connectivity indices. Patch complexity indices included two existing geometrical indices (SHAPE and FRAC), and a new index derived from spatial network analysis, the mean detour index (MDI). We analyzed the behaviours of adjusted fragmentation and connectivity indices theoretically and empirically on both artificial and real landscapes.

Results

While maintaining the mathematical properties of fragmentation and connectivity indices, our method could distinguish landscapes with identical patch size distributions but different spatial configurations. The mean detour index had a different response than geometrical indices. This result indicates that, at the patch level, topological complexity can exhibit different patterns from geometrical complexity.

Conclusions

Measuring intra-patch connectivity with patch size in fragmentation and connectivity indices cannot distinguish landscapes having similar patch sizes distribution but different spatial configurations. This paper introduces a method to distinguish such patterns relying on geometrical and topological indices and shows to which extent it can impact conservation planning.