bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Quantitative Model that Incorporates Reactive Inhibition but No Micro-Consolidation Explains Motor Sequence Practice Effects
Authors: Gupta Mohan , Rickard Timothy 
Published: May 2023

Abstract
Several recent papers have advanced the hypothesis that motor sequence learning occurs exclusively during short breaks between performance trials, a process known as micro-consolidation. T. Here, we advance an alternative hypothesis in which motor learning happens concurrently with performance and that reactive inhibition – which has a progressive slowing effect on performance during each trial but dissipates during breaks – operates with equivalent magnitude within each training trial, yielding the illusion that learning occurs during breaks across the range of early training trials on which analyses were conducted in the recent studies. We advance a quantitative model of motor sequence practice effects that embodies our assumptions and provides a close fit to data at the grain-size of sequences within trials. Our modeling approach allows for a level of predictive precision and theoretical transparency that in our view should be the standard in future work on this topic.
Development of a molecular marker for the Pi1 gene based on the association of the SNAP protocol with the touch-up gradient amplification method
Authors: Scheuermann Klaus Konrad , Pereira Adriana 
Published: May 2023

Abstract

Background
Rice blast disease, caused by the fungus Magnaporthe oryzae, is one of the major constrains for rice production. Genetic resistance is the most effective and environmentally safe approach to combat the rice blast. However, the use of resistance genes depends on factors such as the availability of molecular markers allowing marker-assisted selection during the breeding process. Pi1 gene, considered a broad-spectrum resistance gene, has great potential to contribute in achieving durable resistance to rice blast, but lacks a friendly marker to be employed.
Methods and results
In the present study, we have explored a nucleotide polymorphism in the Pik locus, associating SNAP protocol with touch-up gradient amplification method to develop a SNAP marker. Through the screening of a germplasm bank and analysis of an F2 population, the Pi1 SNAP marker was validated by pathogenicity tests and compared with previously existing markers.
Conclusions
The Pi1 SNAP marker is effective in distinguishing germplasms carrying the Pi1gene from Pik alleles, employing a cost-effective methodology.
Suppression of lysosome metabolism-meditated GARP/TGF-β1 complexes specifically depletes regulatory T cells to inhibit breast cancer metastasis
Authors: Chen Yutong , Li Tao , Liu Jianing , Zhang Wei , Cao Yi , Hu Bin , Niu Hanjing , Gao Jinhua , Zhang Zhongze , Yue Kexin , Bao Guochen , Wang Jiajia , Wang Chaojie , Wang Peng , xie Song-qiang , Zou Taotao , Ma Jing 
Published: May 2023

Abstract

Regulatory T cells (Tregs) prevent autoimmunity and contribute to cancer progression. They exert contact-dependent inhibition of immune cells through the production of active transforming growth factor-β1 (TGF-β1). However, the absence of a specific surface marker makes inhibiting the production of active TGF-β1 to specifically deplete human Tregs but not other cell types a challenge. TGF-β1 in an inactive form binds to Tregs membrane protein GARP and then activates it via an unknown mechanism. Here, we demonstrate that TRAF3IP3 in the Treg lysosome is involved in this activation mechanism. Using a novel naphthalenelactam-platinum-based anticancer drug (NPt), we developed a new synergistic effect by suppressing ABCB9 and TRAF3IP3-mediated divergent lysosomal metabolic programs in tumors and human Tregs to block the production of active GARP/TGF-β1 for remodeling the tumor microenvironment. Mechanistically, NPt is stored in Treg lysosome to inhibit TRAF3IP3-meditated GARP/TGF-β1 complex activation to specifically deplete Tregs. In addition, by promoting the expression of ABCB9 in lysosome membrane, NPt inhibits SARA/p-SMAD2/3 through CHRD-induced TGF-β1 signaling pathway. In addition to expose a previously undefined divergent lysosomal metabolic program-meditated GARP/TGF-β1 complex blockade by exploring the inherent metabolic plasticity, NPt may serve as a therapeutic tool to boost unrecognized Treg-based immune responses to infection or cancer via a mechanism distinct from traditional platinum drugs and currently available immune-modulatory antibodies.
Age, sex and race distribution of accelerometer-derived sleep variability in US school-aged children and adults
Authors: Price Elexis , Li Xinyue , Xu Yanyan , Mansuri Asifhusen , McCall William V. , Su Shaoyong , Wang Xiaoling 
Published: May 2023

Abstract
Background
Sleep variability (e.g. intra-individual variabilities in sleep duration or sleep timing, social jetlag, and catch-up sleep) is an important factor impacting health and mortality. However, limited information is available on the distribution of these sleep parameters across the human life span. We aimed to provide distribution of sleep variability related parameters across lifespan by sex and race in a national representative sample from the U.S. population.
Methods
The study included 9,799 participants 6 years and older from the National Health and Nutrition Examination Survey (NHANES) 2011–2014, who had at least 3 days of valid sleep parameters with at least one day obtained during weekend (Friday or Saturday night). These were calculated from 7-day 24-h accelerometer recordings.
Results
Of the study participants, 43% showed ≥ 60 minutes sleep duration standard deviation (SD), 51% experienced ≥ 60 minutes catch-up sleep, 20% showed ≥ 60 minutes midpoint of sleep SD, and 43% experienced ≥ 60 minutes social jetlag. American youth and young adults averaged greater sleep variability compared to other age groups. Non-Hispanic Blacks showed greater sleep variability in all parameters compared to other racial groups. There was a main effect of sex on sleep midpoint SD and social jetlag with males averaging slightly more than females.
Conclusion
Our study provides important observations on sleep irregularity parameters of residents of the United States by using objectively measured sleep patterns and will provide unique insights for personalized advice on sleep hygiene.
Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms
Authors: Portugal Liana Catarina Lima , Ramos Taiane Coelho , Fernandes Orlando , Bastos Aline Furtado , Campos Bruna , Mendlowicz Mauro Vitor , Luz Mariana , Portella Carla , Berger William , Volchan Eliane , David Isabel Antunes , Erthal Fátima , Pereira Mirtes Garcia , Oliveira Leticia 
Published: May 2023

Abstract
Background
The present study aimed to apply multivariate pattern recognition methods to predict PTSD symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues.
Methods
Trauma-exposed participants were presented with neutral and mutilation pictures during fMRI collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious (“safe context”) or real-life scenes (“real context”). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts.
Results
The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in a real context but not a safe one. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominally intrusion, avoidance, and alteration in cognition. As expected, we obtained very similar results as those obtained in a model predicting total PTSD symptoms.
Conclusion
These results are innovative by showing that machine learning applied to fMRI can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipito-parietal regions.
Static aeroelasticity analysis of rotor blade using a Gauss-Seidel fluid-structure interaction method
Authors: Li Jiaxing , Luo Jiaqi , Liu Yaolong 
Published: May 2023

Abstract
The present study introduces a Gauss-Seidel fluid-structure interaction (FSI) method including the flow solver, structural statics solver and a fast data transfer technique, for the research of structural deformation and flow field variation of rotor blades under the combined influence of steady aerodynamic and centrifugal forces. The FSI method is illustrated and validated by the static aeroelastic analysis of a transonic compressor rotor blade, NASA Rotor 37. An improved local interpolation with data reduction (LIWDR) algorithm is introduced for fast data transfer on the fluid-structure interface of blade. The results of FSI calculation of NASA Rotor 37 show that when comparing with the radial basis function (RBF) based interpolation algorithm, LIWDR meets the interpolation accuracy requirements, while the calculation cost can be greatly improved. The data transmission time is only about 1% of that of RBF. Moreover, iteration step of steady flow computation within one single FSI has little impact on the converged aerodynamic and structural results. The aerodynamic force-caused deformation accounts for nearly 50% of the total. The effects of blade deformation to the variations of aerodynamic performance are given, demonstrating that when static aeroelasticity is taken into account, the choke mass flow rate increases and the peak adiabatic efficiency slightly decreases. The impact mechanisms on performance variations are presented in detail.
Development and validation of potential molecular subtypes and signatures of thyroid carcinoma based on aging-related gene analysis
Authors: Li Zhi , Jia Li , Zhang Lu , Zhang Meng , Zhou Huang-Ren , Bai Yu-ke , Lv Juan , Deng Zhi-Yong , Liu Chao 
Published: May 2023

Abstract
Background. Thyroid carcinoma (THCA) is a cancer of the endocrine system that most commonly affects women. Aging-associated genes play a critical role in various cancers. Therefore, we aimed to gain insight into the molecular subtypes of thyroid cancer and whether senescence-related genes can predict the overall prognosis of THCA patients.
Methods.Transcriptome-related expression files were obtained from The Cancer Genome Atlas (TCGA) database. These profiles were randomly divided into training and validation subsets at a ratio of 1:1. Unsupervised clustering algorithms were used to compare differences between the two subtypes, and prognosis-related senescence genes were used to further construct our prognostic models by univariate Cox and multivariate Cox analyses and construct a nomogram to predict the 1-, 3-, and 5-year overall survival probability of THCA patients. In addition, we performed gene set enrichment analysis (GSEA) to examine different aspects of THCA-related pathways in the high- and low-risk groups and to predict the immune microenvironment and somatic mutations between the different risk groups. Finally, real-time PCR was used to verify the expression levels of key model genes.
Results. The 'ConsensusClusterPlus' R package was used to cluster thyroid cancer into two categories (Cluster1 and Cluster2) on the basis of 46 differentially expressed aging-related genes (DE-ARGs); patients in Cluster1 demonstrated a better prognosis than those in Cluster2. Cox analysis was used to screen six prognosis-related DE-ARGs. The risk score and age were identified as independent prognostic factors. GSEA revealed that most genes were implicated in metabolic signaling pathways. In addition, the two risk model groups differed significantly regarding the immune microenvironment and somatic mutations. Finally, our real-time PCR results confirmed our hypothesis.
Conclusion. Differences exist between the two subtypes of thyroid cancer that help guide treatment decisions. The six DE-ARG genes have a high predictive value for risk-stratifying THCA patients, accurately identifying individuals with a potentially poor prognosis, and improving patient prognosis.
Assessment of Spatio-Temporal Evolution Trends and Driving Factors of Green Development in Harbin-Chang urban agglomeration
Authors: Tang Yang , Yuan Yongbo , Tian Boquan 
Published: May 2023

Abstract
As China has entered a new stage of high-quality development, clarifying the mechanism and spatial characteristics of green development for urban agglomerations are critical to sustainable development. Based on the data of 11 major cities in the Harbin-Changchun urban agglomeration (HCUA) from 2010 to 2020, this study constructs an evaluation system of green development index (GDI) is composed of four dimensions, i.e., urban green construction (UGC), industrial green development (IGD), resource and environmental carrying capacity (RECC), and technological innovation support (TIS). Furthermore, using the entropy weight method to obtain the weights of evaluation indicators. And then, the comprehensive index calculation is used to evaluate the GDI. The driving factors of each level of GDI are determined by the Pearson correlation coefficient. The results infer some novel findings as follows: (1) The overall tendency of the GDI of the HCUA has gradually increased from 0.358 in 2010 to 0.379 in 2020 which is at the average level. The dimension of TIS shows the highest rate of contribution while IGD and RECC show a fluctuating trend over the time window. (2) The GDI in the HCUA exhibits a patchy clustering differentiation feature that spreads from the central area to the surrounding areas with a "high in the south and low in the north" pattern. Specifically, Changchun, Harbin, and Daqing form an "inverted triangle" structure in geographical location to drive the green development of neighboring areas. (3) The core cities of the HCUA, Changchun, and Harbin, show a much higher level than the other cities. Jilin and Daqing are at the average level, and besides, the rest of the cities of GDI are at the poor level with significant fluctuations in ranking. (4) There are different driving factors between each level of GDI. For cities with good and average levels should focus on protecting resources and the environment. Meanwhile, cities with poor level of GDI need to improve IGD to optimize the urban green structure. Thus, it is suggested to strengthen the flow of factors and implement differentiated strategies to promote coordinated development and spatial clustering.
Automatic detection of Breast Cancer by using Ensemble Learning
Authors: León Carlos de la Cruz , Agarwal Deevyankar , Torre-Díez Isabel de la , Río-Solá M Lourdes 
Published: May 2023

Abstract
Breast cancer is a significant health problem, with about 2 million new cases annually diagnosed and 600,000 deaths. Early detection and accurate diagnosis are critical to patient prognosis. Machine learning (ML) models show promising results in accurate and efficient diagnosis. In the present work, the performance of different models of ML are studied in the publicly accessible online dataset "Wisconsin Breast Cancer Dataset". Those models are formed by logistic regressions, Random Forest, Naïve Bayes, and Support Vector Machine algorithms, being the last one the best performing. An ensemble model combining the best proposed models is then implemented. An SVM model with standardized dataset is used, a logistic regression model with standardized dataset and 10-component PCA analysis. A Random Forest model with standardized dataset and 60 estimators. All models use a test dataset formed by 30% of the original dataset. The models are combined using a majority weighted voting system. The SVM model has a weight of 0.5 while the regression and Random Forest models have weights of 0.25. The ensemble voting model manages to improve the results of the individual models with an accuracy of 98%, precision of 97%, recall of 99% and F1 score of 98%.
A Case Study of Using Artificial Neural Networks to Predict Heavy Metal Pollution in Lake Iznik
Authors: Mert Berna Kırıl , Kasapoğulları Deniz 
Published: May 2023

Abstract

Since high levels of heavy metals cause serious complications for water resources, plants, animals and human health, determining their presence and concentration is very important for the sustainability of the ecosystem. In recent years, rapid advances in the field of artificial neural networks (ANNs) brought them the forefront in water quality prediction. In this paper, various experiments were conducted with a model for predicting the presence of heavy metals using IBM SPSS statistics 23 software. In order to assess the water quality of Lake Iznik –an important source of water– in terms of heavy metals, water quality parameters of samples taken in the period 2015–2021 from five different water sources flowing into the lake were analyzed. A number of psychochemical were measured in samples taken from Karasu, Kırandere, Olukdere, and Sölöz streams flowing into the lake, and were used as input data for modeling, while fifteen heavy metal concentrations in Karsak stream flowing out of the lake were used as output data of the model. The analyses showed that the R2 coefficients for heavy metals were mostly close to 1. Considering the importance of the independent variable in heavy metal pollution prediction, the most effective parameters for streams stood out to be conductivity, COD, COD, and temperature, respectively. It was seen that ANN model is a good prediction tool method that can be used effectively to determine heavy metal pollution in the lake in terms of ecological sustainability in order to conservation the water quality of Lake Iznik and to eliminate the existing pollution.
The Detection of Keratoconus using a Three-Dimensional Corneal Model Derived from Anterior Segment Optical Coherence Tomography
Authors: Tran Sang , Mohammed Isa S.K. , Tariq Zeshan , Munir Wuqaas M. 
Published: May 2023

Abstract

Purpose:
To differentiate between keratoconus and healthy corneas via three-dimensional (3D) measurements of surface area and volume. Measurements are derived from anterior segment optical coherence tomography (AS-OCT) images.
Methods:
Keratoconus patients were identified along with healthy controls patients between the ages of 20 and 79 years old. The selected patients underwent a nine-line raster scan AS-OCT. ImageJ was used to determine the central 6mm of each image and each corneal image was then divided into six 1mm segments. Free-D software was then used to render the nine different images into a 3D model to calculate corneal surface area and volume.
Results:
33 eyes with keratoconus, along with 33 eyes healthy controls were enrolled. There were statistically significant (p < 0.05) differences between the healthy and keratoconus groups in the metric of anterior corneal surface area (13.927 vs 13.991 mm2, p = 0.046), posterior corneal surface area (14.045 vs 14.173 mm2, p < 0.001), and volume (8.430 vs 7.773 mm3, p < 0.001) within the central 6 mm.
Conclusion:
3D corneal models derived from AS-OCT can be used to measure anterior corneal surface area, posterior corneal surface area, and corneal volume. All three parameters are statistically different between corneas with keratoconus and healthy corneas. Further study and application of these parameters may yield new methodologies for the detection of keratoconus.
Identification of the role of new genetic biomarkers in susceptibility and pathogenesis of spondyloarthropathies in Morocco.
Authors: Canossi Angelica , Oumhani Khadija , Beato Tiziana Del , Sebastiani Pierluigi , Colanardi Alessia , Aureli Anna 
Published: May 2023

Abstract
The spondyloarthropathies (SpA) are inflammatory rheumatologic diseases with common clinical features, whose pathogenesis is attributed to the interaction between genetic and environmental factors. Information on SpA biomarkers in Morocco are limited compared with Europe. This study was designed as a case-control analysis to explore the relationship of some genetic polymorphisms of the CTLA-4 CT60 and FCGR3A with SpA, also together with the role of HLA-B locus, in Moroccan population.
The sample consisted of 84 Moroccan SpA patients and 95 healthy individuals. Gene variability was evaluated by sequence-based typing (SBT) in four single-nucleotide polymorphisms (SNPs) located in 3’UTR of CTLA-4 gene and FCGR3A-158G/T SNP. The study showed a higher frequency of G/G genotype in rs11571319 in axial SpA (AxSpa) group (p=0.031, OR=4.60) and pleuro-pulmonary patients (p=0.036), and instead a significant reduction of A/G genotype (p=0.013, pcorr.=0.039 OR=0.06), compared to controls. Furthermore, a significant increase of A/A in rs3087243 was shown in males SPA pts with peripheral joints pain (p=0.035 OR=4.455). Data from FCGR3A-158G/T analysis evidenced both a reduction of T/T genotype (p=0.030, OR=0.09) and an increase of G/T genotype (p=0.06 OR=4.93) in AxSpA patients, compared to controls. With regard to HLA-B locus as risk factor, we noticed a significant increase of HLA-B*51,52 alleles (p=0.041 OR=2.714) and a reduction of HLA-B*45:01 (pcorr.=0.033 OR=0.041) correlated with these pathologies. Our data suggest the existence of new biomarkers for diagnosis and treatment of SPA in Moroccan population.
Thermo-Mechanical Characterization of Electrospun Polyurethane /Carbon- Nanotubes Nanofibers: A Comparative Study
Authors: Shaker Ahmed , Khedewy Amira , Hassan Mohamed , El-Baky Marwa Abd 
Published: May 2023

Abstract
Creating ultrathin mountable fibers from a wide range of polymeric functional materials have made electrospinning an adequate approach to produce highly flexible and elastic materials. In this paper, electrospinning was utilized to produce thermoplastic polyurethane (TPU) nanofibrous membranes for the purpose of studying their thermal and mechanical properties. Towards a study of the effects of fiber orientation and multi-walled carbon nanotubes (MWCNTs) as a filler on both mechanical and thermal characteristics of electrospun TPU mats, an experimental comparison was held between a unidirectional and randomly aligned TPU and TPU/CNT nanofibrous structures. Incorporation of MWCNTs into randomly oriented TPU nanofibers resulted in a significant increase in Young's modulus (E), from 3.66 MPa to 5.68 MPa. Conversely, for unidirectionally spun fibers, Young's modulus decreased from 16.68 MPa to 11.63 MPa upon addition of MWCNTs. However, dynamic mechanical analysis (DMA) revealed a different behavior. The randomly oriented specimens exhibited a storage modulus with a significant increase from 180 MPa to 614 MPa for TPU and TPU/CNT mats, respectively, and a slight decrease from 157 MPa to 143 MPa for unidirectional TPU and TPU/CNT mats, respectively. Meanwhile, the loss modulus increased with the addition of MWCNTs from 15.7 MPa to 58.9 MPa and from 6.4 MPa to 12 MPa for the random and aligned fibers, respectively. Thermal degradation of the membranes was not significantly affected by the addition of MWCNTs, indicating that the mixing of the two constituents did not change the TPU’s polymer structure, and the TPU/CNT nanocomposite exhibited stable thermal degradation properties.