bluuebunny/crossref_metadata_2025_split huggingface dataset:
Morphological and Functional Correlations in Acute Central Serous Chorioretinopathy
Authors: Kiraly Peter , Šuštar Habjan Maja , Smrekar Jaka , Jaki Mekjavić Polona 
Published: March 2024

Abstract
Purpose
We evaluate morphological and functional correlations in patients with acute central serous chorioretinopathy (CSC).

Methods
A prospective study was conducted on 50 patients with an acute CSC episode lasting less than 3 months. At baseline, assessments included optical coherence tomography (OCT), best-corrected visual acuity (BCVA), contrast sensitivity (CS), microperimetry (MP), and multifocal electroretinography (mfERG). A correlation analysis between OCT morphological parameters (maximal subretinal fluid height (SRF), central retinal thickness (CRT), and macular volume (MV)) and functional parameters was conducted on the affected eye for each patient.

Results
Among the morphological parameters, SRF showed the strongest correlations with functional parameters (r absolute value range = 0.10–0.70). Weak correlations were observed between BCVA and morphological parameters (r absolute value range = 0.14–0.26). Average retinal sensitivity (MP-A) was the functional parameter displaying the most robust negative correlation with morphological parameters (r absolute value range = 0.61–0.70). In contrast, average contrast sensitivity (CS-A) and mfERG average amplitude density in the first (mfERG-A1) and second (mfERG-A2) ring showed weak to moderate (r absolute value range = 0.35–0.56) yet statistically significantly nonzero correlations.

Conclusions
SRF and CRT could serve as the most representative morphological proxies for visual function deficit in acute CSC patients. Retinal sensitivity, as measured by MP, may be superior to BCVA in clinical research studies or when an in-depth visual function evaluation is needed.
Interpretability of rectangle packing solutions with Monte Carlo tree search
Authors: Galán López Yeray , González García Cristian , García Díaz Vicente , Núñez Valdez Edward Rolando , Gómez Gómez Alberto 
Published: March 2024

AbstractPacking problems have been studied for a long time and have great applications in real-world scenarios. In recent times, with problems in the industrial world increasing in size, exact algorithms are often not a viable option and faster approaches are needed. We study Monte Carlo tree search, a random sampling algorithm that has gained great importance in literature in the last few years. We propose three approaches based on MCTS and its integration with metaheuristic algorithms or deep learning models to obtain approximated solutions to packing problems that are also interpretable by means of MCTS exploration and from which knowledge can be extracted. We focus on two-dimensional rectangle packing problems in our experimentation and use several well known benchmarks from literature to compare our solutions with existing approaches and offer a view on the potential uses for knowledge extraction from our method. We manage to match the quality of state-of-the-art methods, with improvements in time with respect to some of them and greater interpretability.
A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions
Authors: Nasir Murtaza , Summerfield Nichalin S. , Carreiro Stephanie , Berlowitz Dan , Oztekin Asil 
Published: March 2024

Abstract
Machine learning (ML) has the potential to revolutionize healthcare, allowing healthcare providers to improve patient-care planning, resource planning and utilization. Furthermore, identifying key-risk-factors and interaction-effects can help service-providers and decision-makers to institute better policies and procedures. This study used COVID-19 electronic health record (EHR) data to predict five crucial outcomes: positive-test, ventilation, death, hospitalization days, and ICU days. Our models achieved high accuracy and precision, with AUC values of 91.6%, 99.1%, and 97.5% for the first three outcomes, and MAE of 0.752 and 0.257 days for the last two outcomes. We also identified interaction effects, such as high bicarbonate in arterial blood being associated with longer hospitalization in middle-aged patients. Our models are embedded in a prototype of an online decision support tool that can be used by healthcare providers to make more informed decisions.
Measurements of the Viscosity of Hydrogen and a (Hydrogen + Methane) Mixture with a Two-Capillary Viscometer
Authors: Betken Benjamin , Austegard Anders , Finotti Francesco , Caccamo Chiara , Stang Hans Georg Jacob , Khosravi Bahareh , Span Roland 
Published: March 2024

AbstractMeasurements of the viscosity of pure hydrogen and a binary (hydrogen + methane) mixture with a nominal composition 90 mol % hydrogen are presented. The measurements were conducted with a two-capillary viscometer relative to helium along three isotherms of (298.15, 323.15, and 348.15) K and at pressures of up to 18 MPa. Expanded relative combined uncertainties in viscosity range from (0.65 to 2.7) % (k = 2) for the hydrogen data, and from (0.91 to 3.2) % (k = 2) for the (hydrogen + methane) data. The viscosity data are compared to experimental literature data and viscosity correlations implemented in the NIST REFPROP v10.0 database. Good agreement between this work’s data, literature data, and the viscosity correlation was achieved for pure hydrogen. The (hydrogen + methane) mixture was compared to the Extended Corresponding States (ECS) model implemented in REFPROP v10.0. Relative deviations between the experimental data and the ECS model exceed the experimental uncertainty and were found to exhibit a positive trend with increasing density and a weakly pronounced negative trend with increasing temperature. No experimental literature data are available at overlapping state regions. Nonetheless, deviations to the ECS model imply reasonable consistency of this work’s data and literature data. In addition to experimental viscosities, experimental zero-density viscosity ratios of the fluids under investigation and helium are reported. Fairly good agreement within the experimental uncertainty of this work with a highly accurate literature value and a value obtained from accurate ab initio calculated data was achieved for hydrogen.
GnT-V-mediated aberrant N-glycosylation of TIMP-1 promotes diabetic retinopathy progression
Authors: Xi Xiaoting , Yang Yanni , Chen Qianbo , Ma Jia , Wang Xuewei , Deng Yachun , Wang Xi , Li Yan 
Published: March 2024

Abstract

Background
Vascular endothelial growth factor (VEGF) signaling pathway plays an important role in the progression of diabetic retinopathy (DR). The glycosylation modification process of many key functional proteins in DR patients is abnormal. However, the potential involvement of abnormal N-glycoproteins in DR progression remains unclear.

Methods
Glycoproteomic profiling of the vitreous humor was performed. The level of protein and N-glycoprotein was confirmed by Western blot and Lectin blot, respectively. The cell viability and migration efficiency were detected by CCK-8 and Transwell assay. Flow cytometry was conducted to analyze the level of cell apoptosis and reactive oxygen specie. Malondialdehyde, superoxide dismutase activity and VEGF content were detected by Enzyme linked immunosorbent assays. The interaction of metalloproteinase 1 (TIMP-1) with N-acetylglucosamine transferase V (GnT-V) was detected by GST pull-down. Hematoxylin and eosin staining and choroidal and retinal flat mount stained with fluorescein isothiocyanate-Dextran assay were used for functional research in vivo.

Results
We found that N-glycosylation was up-regulated in DR rats and high glucose (HG)-induced human retinal pigment epithelium cell line ARPE-19. HG-induced inhibited the viability of ARPE-19 cells and promoted cell apoptosis and oxidative stress (OS), but these effects were reversed with kifunensine treatment, GnT-V knockdown and TIMP-1 mutation. Additionally, GnT-V binds to TIMP-1 to promote N-glycosylation of TIMP-1. Over-expression of GnT-V inhibited the viability of ARPE-19 cells and promoted cell apoptosis, OS and VEGF release, which these effects were reversed with TIMP-1 mutation. Interestingly, over-expression of GnT-V promoted retinal microvascular endothelial cells (RMECs) angiogenesis but was revered with TIMP-1 mutation, which was terminally boosted by VEGF-A treatment. Finally, knockdown of GnT-V relieved DR progression.

Conclusion
The findings indicate that GnT-V can promote RMECs angiogenesis and ARPE-19 cells injury through activation VEGF signaling pathway by increasing TIMP-1 N-glycosylation level, which provides a new theoretical basis for the prevention of DR.
A Feature Selection Method Based on Feature-Label Correlation Information and Self-Adaptive MOPSO
Authors: Han Fei , Li Fanyu , Ling Qinghua , Han Henry , Lu Tianyi , Jiao Zijian , Zhang Haonan 
Published: March 2024

AbstractFeature selection can be seen as a multi-objective task, where the goal is to select a subset of features that exhibit minimal correlation among themselves while maximizing their correlation with the target label. Multi-objective particle swarm optimization algorithm (MOPSO) has been extensively utilized for feature selection and has achieved good performance. However, most MOPSO-based feature selection methods are random and lack knowledge guidance in the initialization process, ignoring certain valuable prior information in the feature data, which may lead to the generated initial population being far from the true Pareto front (PF) and influence the population’s rate of convergence. Additionally, MOPSO has a propensity to become stuck in local optima during the later iterations. In this paper, a novel feature selection method (fMOPSO-FS) is proposed. Firstly, with the aim of improving the initial solution quality and fostering the interpretability of the selected features, a novel initialization strategy that incorporates prior information during the initialization process of the particle swarm is proposed. Furthermore, an adaptive hybrid mutation strategy is proposed to avoid the particle swarm from getting stuck in local optima and to further leverage prior information. The experimental results demonstrate the superior performance of the proposed algorithm compared to the comparison algorithms. It yields a superior feature subset on nine UCI benchmark datasets and six gene expression profile datasets.
CLSTM-SNP: Convolutional Neural Network to Enhance Spiking Neural P Systems for Named Entity Recognition Based on Long Short-Term Memory Network
Authors: Deng Qin , Chen Xiaoliang , Yang Zaiyan , Li Xianyong , Du Yajun 
Published: March 2024

AbstractMembrane computing is a type of parallel computing system (generally called P system) abstracted from information exchange mechanisms in biological cells, tissues, or neurons, which can process data in a distributed and interpretable manner. LSTM-SNP, the first model of long short-term memory networks based on parameterized nonlinear Spiking neural P systems, was proposed recently. However, a systematic understanding and leveraging of the LSTM-SNP model to address named entity recognition (NER) and other natural language processing (NLP) tasks are still lacking. The bottleneck of the NER task lies in the scarcity of data and the vague definition of entity edges. Most approaches center on dataset handling, and there have been few attempts to address the issue in Spiking neural P (SNP) systems. This paper proposes a model named CLSTM-SNP based on the LSTM-SNP, aiming to tackle the NER problem in the field of SNP systems for the first time. First, this study employs a CNN layer to obtain character-level characteristics. Second, GloVe word vectors are utilized as word representations. Third, the research employs the LSTM-SNP to analyze textual features. We subsequently studied CLSTM-SNP’s effectiveness in addressing NER problems on CoNLL-2003 and OntoNotes 5.0 datasets and compared it to the results of five other baseline methods. Our model CLSTM-SNP achieved a macro F1-score of 89.2 $$\%$$
%
on CoNLL-2003 and 75.5$$\%$$
%
on OntoNotes 5.0, respectively. The performance of CLSTM-SNP and LSTM-SNP indicates a great potential for handling named entity recognition or other sequential tasks in NLP.
Soil Contamination and Biomarkers in Ucides cordatus in Mangroves from Baía de Todos os Santos, Bahia, Brazil
Authors: Ramos M. A. V. , da Rocha S. S. , Winkaler E. U. , Nobrega G. N. , da Cruz Carvalho J. , de Souza Santos A. Zaga , Otero X. L. 
Published: March 2024

AbstractThe aim of the present study was to analyze the response to exposure to pollutants (trace elements and organic pollutants) using biomarkers (micronucleated cells and glutathione S-transferase and catalase activity) in uçá crab Ucides cordatus. The study was carried out at four sites: Cacha Prego (CP) and Ponta Grossa (PG), areas with low anthropic activity; and Ilha de Maré (IM) and Pitinga (PT), areas affected by industrial activity. At each site, soil and crab samples were collected to analyze the contents of potentially toxic elements (total concentration and chemical partitioning of trace elements), polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs). Both total concentrations and geochemical fractions of Cu, Zn, and Ni in soils were significantly higher in IM. Likewise, higher reactive Pb contents were observed in mangroves both in IM and in PG. Values above quality limits were observed for organic pollutants (PAHs) in soils from CP and PT, while PCB contents were below the maximum permissible levels. Metals in crabs also showed spatial variations, with higher Cu concentrations in all tissues in IM and PT and higher Ni concentrations in hepatopancreas in PT during the dry season. PAH values in crabs did not show spatial variations; however, crustaceans with contents above maximum limits in their muscle tissue were found in CP. Crabs from the Baia de Todos Santos showed different responses in biomarker expression, with higher enzymatic activity and greater numbers of micronucleated cells in crabs from IM, suggesting oxidative stress and genotoxicity in this mangrove forest.
Hybrid Molecular and Functional Micro-CT Imaging Reveals Increased Myocardial Apoptosis Preceding Cardiac Failure in Progeroid Ercc1 Mice
Authors: van Thiel Bibi S. , de Boer Martine , Ridwan Yanto , de Kleijnen Marion G. J. , van Vliet Nicole , van der Linden Janette , de Beer Isa , van Heijningen Paula M. , Vermeij Wilbert P. , Hoeijmakers Jan H. J. , Danser A. H. Jan , Kanaar Roland , Duncker Dirk J. , van der Pluijm Ingrid , Essers Jeroen 
Published: March 2024

Abstract
Purpose
In this study, we explored the role of apoptosis as a potential biomarker for cardiac failure using functional micro-CT and fluorescence molecular tomography (FMT) imaging techniques in Ercc1 mutant mice. Ercc1 is involved in multiple DNA repair pathways, and its mutations contribute to accelerated aging phenotypes in both humans and mice, due to the accumulation of DNA lesions that impair vital DNA functions. We previously found that systemic mutations and cardiomyocyte-restricted deletion of Ercc1 in mice results in left ventricular (LV) dysfunction at older age.

Procedures and Results
Here we report that combined functional micro-CT and FMT imaging allowed us to detect apoptosis in systemic Ercc1 mutant mice prior to the development of overt LV dysfunction, suggesting its potential as an early indicator and contributing factor of cardiac impairment. The detection of apoptosis in vivo was feasible as early as 12 weeks of age, even when global LV function appeared normal, underscoring the potential of apoptosis as an early predictor of LV dysfunction, which subsequently manifested at 24 weeks.

Conclusions
This study highlights the utility of combined functional micro-CT and FMT imaging in assessing cardiac function and detecting apoptosis, providing valuable insights into the potential of apoptosis as an early biomarker for cardiac failure.
Differences in Psychological Health and Weight Loss after Bariatric Metabolic Surgery between Patients with and without Pain Syndromes
Authors: Pyykkö Johanna E. , Zwartjes Max , Nieuwdorp Max , van Olst Nienke , Bruin Sjoerd C. , van de Laar Arnold W. , Sanderman Robbert , Hagedoorn Mariët , Gerdes Victor E. A. 
Published: March 2024

Abstract
Purpose
Chronic pain and obesity often co-occur, negatively affecting one another and psychological wellbeing. Pain and psychological wellbeing improve after bariatric metabolic surgery (BMS), however, it is unknown whether psychological wellbeing improves differently after weight loss between patients with and without chronic pain. We investigated whether weight loss is associated with greater psychological wellbeing and functioning change after BMS, comparing patients with and without preoperative pain syndromes.

Methods
Depression, health-related quality of life, self-esteem, self-efficacy to exercise and controlling eating behaviours, physical activity, and food cravings were measured before and 24 months after BMS among 276 patients with obesity. The presence of preoperative chronic pain syndromes was examined as a moderator for the relationship between 24-month weight loss and changes in psychological outcomes.

Results
Chronic pain syndromes were present among 46% of patients. Weight loss was associated with greater improvement in health-related quality of life, self-efficacy to exercise and controlling eating behaviours, self-esteem and greater amelioration in food cravings. Pain syndromes only moderated negatively the relationship between the postoperative weight loss and change in self-efficacy to control eating behaviours (b = -0.49, CI [-0.88,-0.12]).

Conclusion
Patients with and without chronic pain showed similar improvements in weight and psychological wellbeing and behaviours after BMS. The relationship between weight loss and the improvement of self-efficacy to control eating behaviours was weaker among patients with chronic pain syndrome. Further work, measuring pain severity over time, is needed to shed light on the mechanism underlying pain and postoperative change in psychological wellbeing and weight loss.

Graphical Abstract
CIT Training Among Law Enforcement: Does It Work?
Authors: Boddy Kierstyn , Athaide-Victor Elizabeth , Law Fang-Mei , Sharp Johnathon 
Published: March 2024

AbstractThe relationship between law enforcement and those with mental illness is severely strained and possibly even broken. But who caused this rift in the relationship? Is law enforcement to blame? Are those with a mental illness to blame? Or, does the issue stem from a bigger problem of deinstitutionalization and negative stigmas that society has against both mentioned populations? The purpose of this study was to dive deeper into the relationship between law enforcement officers and those with mental illness to seek the root cause of the disconnect, as well as what can be done to bridge the gap. Previous research has focused on the law enforcement perspective, focusing on what training (if any) they have for dealing with citizens with mental illnesses as well as how those in law enforcement perceive people with mental illnesses while on the job. These perceptions are yet another possible explanation of the disconnect and could potentially be part of the solution to help mend the break between law enforcement and those suffering from a mental illness. The present study sought to examine the effects that CIT training has on the response outcomes when law enforcement officers respond to those with mental illness. These response outcomes, when ranked by severity, can help to explain the importance of CIT training and its effectiveness, especially when compared to the previous studies data pool of officer perspectives. The results of this study are compelling and create an effortless opening for conversation on a topic that is difficult for many to talk about.
Pilot Evaluation of the POWER Program: Positive Outcomes with Emotion Regulation
Authors: Zakszeski Brittany , Cain Michelle , Eklund Katie , Heurich Lissy , Friedman Reagan , Ward Ashleigh , Zhou Jingwen 
Published: March 2024

AbstractThe Positive Outcomes With Emotion Regulation (POWER) Program is a transdiagnostic intervention for adolescents at risk of developing emotional disorders. The POWER Program was designed to be implemented in secondary schools, by school personnel with or without specialized mental health training, as a Tier 2 intervention. In this pilot study, the POWER Program was implemented by school psychologists and school psychologists-in-training and evaluated across four focal student participants using a multiple-baseline-across-participants single-case design. Program efficacy was assessed using systematic direct classroom observations of student negative affect and social engagement as well as student and caregiver ratings of emotional and behavioral symptoms. Program usability was assessed through rating scales completed by intervention facilitators and student participants. Overall, results provide evidence of the POWER Program’s small- to large-sized effects on students’ emotional and behavioral functioning as observed in the classroom and self-reported by students. In addition, results suggest implementation facilitators’ and students’ positive impressions of the program, evident in ratings of high understanding, feasibility, and acceptability across groups. Study limitations are highlighted with attention to opportunities to further refine and evaluate the POWER Program.
Gap Junctions May Have A Computational Function In The Cerebellum: A Hypothesis
Authors: Gilbert Mike , Rasmussen Anders 
Published: March 2024

AbstractIn the cerebellum, granule cells make parallel fibre contact on (and excite) Golgi cells and Golgi cells inhibit granule cells, forming an open feedback loop. Parallel fibres excite Golgi cells synaptically, each making a single contact. Golgi cells inhibit granule cells in a structure called a glomerulus almost exclusively by GABA spillover acting through extrasynaptic GABAA receptors. Golgi cells are connected dendritically by gap junctions. It has long been suspected that feedback contributes to homeostatic regulation of parallel fibre signals activity, causing the fraction of the population that are active to be maintained at a low level. We present a detailed neurophysiological and computationally-rendered model of functionally grouped Golgi cells which can infer the density of parallel fibre signals activity and convert it into proportional modulation of inhibition of granule cells. The conversion is unlearned and not actively computed; rather, output is simply the computational effect of cell morphology and network architecture. Unexpectedly, the conversion becomes more precise at low density, suggesting that self-regulation is attracted to sparse code, because it is stable. A computational function of gap junctions may not be confined to the cerebellum.