bluuebunny/crossref_metadata_2025_split huggingface dataset:
Value shift in response to aging: A longitudinal study based on healthy aging
Authors: Shen Yiran , Cheng Xinjiao , Sang Xue , Cao Ruoyun , Yan Chaoyang 
Published: March 2024

Abstract
Objective
Healthy aging is considered an effective way to deal with the challenges of aging. Intrinsic capacity(IC) and functional ability (FA) are the key elements of this concept. However, the relationship between the two has not been explored in detail.
Methods
A theoretical model was constructed by analyzing the concept of “health aging quantification.” A national database (China Health and Retirement Longitudinal Study, CHARLS) was used as the data source for the study. A range of physical and mental measures were employed to construct intrinsic abilities using factor analysis, and a measure of disability was used as a proxy for FA. Descriptive and regression analyses were also conducted to preliminarily assess the distribution of the variables on the IC). This study used cross-lagged models to examine structural and effect differences between IC and FA in the longitudinal manner, along with mediation analysis to analyze the mechanism of the two.
Results
IC showed normal distribution and an increasing trend in education and income. In the structural analysis, IC and FA indicate significant cross-lagged effects in the longitudinal direction. The effect of IC on FA is also greater than that of FA on IC; such an effect also tends to intensify over time. Furthermore, multimorbidity mediates the effect of IC on FA, but the mediating effect is not very large. Finally, social participation did not significantly mediate the effect of FA on IC.
Conclusion
The study provides novel insights into the evolution of disability, in which the discovery of the disease is only a process of the outcome of disability. This suggests that policymakers should not only focus on identifying a disease, but also on assessing, maintaining, and providing intervention before the disease. The authorities should also promote the implementation of people-centered rather than disease-centered measures in the health delivery system to achieve value transformation.
Nugget and corona bond size measurement through active thermography and transfer learning model
Authors: Santoro Luca , Razza Valentino , De Maddis Manuela 
Published: March 2024

Abstract
Resistance Spot Welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). The investigation starts by examining the phase and amplitude gradient of the thermal signal. A convolutional neural network (CNN) is employed to estimate the size of the nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study presents a suggestion to adopt a new methodology for automatic RSW quality control based on thermal signal analysis.
High Sensitivity of Targeted Next-Generation Sequencing on Patients with Suspected Mycobacteria Infectious Diseases: A Prospective Matched Cohort Study
Authors: Hang Tian-Xing , Chen Yu , Ren Yun-Yao , Liu Xu-Hui , Hu Chun-Mei 
Published: March 2024

Abstract
Objectives
To evaluate the diagnostic accuracy of Mycobacterium-targeted next-generation sequencing (M-tNGS)technique forpatients with suspected Mycobacterium infectious diseases.
Methods
We consecutively enrolled patients with suspected Mycobacterium infectious diseases, collected samples (respiratory tract sample and tissue biopsy), and tested them using M-tNGS and GeneXpert MTB RIF (Xpert) assays. The results of these tests were compared with the clinical diagnosis and Mycobacterium culture results.
Results
Overall,244 patients were enrolled, from whom 206 respiratory samples and 38 tissue samples were obtained. The sensitivity of M-tNGS was superior to that of Xpert in both respiratory and biopsy samples (92.2% vs. 43.6% and 90.0% vs. 46.0%, respectively). Conversely, M-tNGS was less specific than Xpert in respiratory and biopsy samples (79.7% vs. 100% and 87.5% vs. 100% , respectively). Meanwhile, M-tNGS more frequently detected drug resistance and nontubercular mycobacteria (NTM), with sensitivity of 70.91% and 13.11%, respectively. Based on comparison with a composite reference standard, M-tNGS was more accurate than the Xpert assay and Mycobacteriumtuberculosis(MTB) culture, with areas under the curve of 0.86, 0.72, and 0.6, respectively.
Conclusion
M-tNGS is suitable for the early screening of Mycobacterium infectious diseases. Notably, M-tNGS can provide more information on drug resistance and Mycobacterium species identification, facilitating accurate treatment.
A Pilot Study: Deep Multi-Instance Learning for Origin Tracing of Brain Metastases
Authors: Yu Hui , Zhang Zhongzhou , Yang Ziyuan , Wang Tao , Wang Zhiwen , Wang Zhongxian , Liu Lunxin , Liu Yan , Zhang Yi 
Published: March 2024

Abstract
Treatment decisions for brain metastasis heavily rely on identifying the primary site, which is typically accomplished through biomarker-based techniques such as genomics and histopathology. However, limited healthcare resources sometimes can hinder their availability. Therefore, we innovatively transform origin tracing into an image classification task. Based on T1ce-MRI, we develop a non-invasive and cost-effective pipeline, called deep multi-instance learning (DMIL). The DMIL-based pipeline includes three steps: pre-processing, training and testing. Particularly, in pre-processing, mix-modal data decoration is proposed to learn multiple modal knowledge. For DMIL training, center-point-based lesion identification is employed to automatically crop ROIs, eliminating the need for manual intervention. Additionally, self-adaptive lesion classification aims to achieve slice-wise origin tracing. During the inference stage, to address the uncertainty stemming from heterogeneity within a patient's volume, we design a voting majority mechanism to make final patient-wise predictions. Evaluated on the clinical dataset, our DMIL-based pipeline demonstrated promising results. The best patient-wise results achieved at 87.27% (accuracy), 85.00% (PPV) and 83.33% (sensitivity).
A Phase I Study using Bortezomib (Velcade), Cladribine, and Rituximab (VCR) in Treating Elderly Patients with Mantle Cell Lymphoma
Authors: Pu Jeffrey J. , Berger Kristin N. , Zheng Chunlei , Do Nhan , Brophy Mary T. , Claxton David F. , Ehmann W Christopher , Drabick Joseph J. , Li Haiquan , Loughran Thomas P. , Epner Elliot M. 
Published: March 2024

Abstract
Cladribine indirectly downregulates methylations of DNA, RNA and histone by blocking transferring methyl groups from S-adenosyl-methionine. Cladribine and Rituximab combination showed a synergetic effect in treating B cell lymphomas. Bortezomib (Velcade) is a FDA approved proteasome inhibitor for treating mantle cell lymphoma (MCL).
In this single arm phase I study, we evaluated the safety, dose limiting toxicity, and efficiency of Bortezomib, Cladribine, and Rituximab (VCR) combination treatment in elderly MCL patients. We also proposed potential DNA methylation biomarkers for VCR treatment. A standard 3+3 dose escalation scheme was designed to determine the maximum tolerable cladribine dose. The therapy consisted of 6 28-day cycles.
Most patients tolerated this regimen well. The overall responding (OR) rate was 84.6% and complete remission (CR) rate was 84.6%. In newly diagnosed subject cohort, the OR and CR was 100% respectively, 1 year overall survival rate was 90%, and progression free survival rate was 80%. Low grade hematological toxicity and mild fatigue were observed. No severe systemic toxicity was observed. Five hypermethylated regions located at gene promoters were identified as potential biomarkers for an effective treatment response.
In conclusion, VCR combination is a well-tolerated, low toxicity and effective regimen for elderly untreated MCL.
Clinicaltrials.gov #: NCT01439750.
Affordances, aesthetics, and sustainability: on the relationship between materials, language and space in everyday object perception
Authors: Grasso Eduardo Naddei , Strappini Francesca , Borghi Anna M. , Scorolli Claudia 
Published: March 2024

Abstract

Everyday objects offer invitations to act (affordances). Most empirical work on affordances has focused on the shape and size of objects, neglecting the analysis of other visual properties, such as texture, investigated instead by designers. Texture perception depends on the type of material, which changes the familiarity, aesthetics, and action(s) an object offers. Here, we investigated the influence of aesthetics and material sustainability on affordance activation. We thus employed objects in materials. We re-adapted a well-established paradigm in literature, showing objects with the same shape, made from sustainable and non-sustainable materials (namely, plastic vs. wood) with varying degrees of familiarity and pleasantness. The objects were positioned at a near or far distance, and followed by different kinds of verbs (observation, manipulation, function, interaction). Participants also completed the Pro-Environmental Behaviors Scale (PEBS) and rated the objects according to seven dimensions: pleasantness, familiarity, sustainability, hardness, heaviness, ownership, and sociality. Results showed greater facilitation with plastic objects compared to the wooden ones when presented in the near space and followed by function verbs. However, we also found a main effect of the material, with response times (RTs) shorter for wooden objects, especially when followed by interaction verbs. We found no correlation between RTs and PEBS, though several significant interactions occurred, and we argue that the material component affected affordances within the specific interplay between object location and linguistic elaboration, hypothesizing that beauty might have transversely modulated the responses.
The Quality and Reliability of Short Videos about Thyroid Nodules on Bilibili and Tiktok: Cross-sectional Study
Authors: Chen YuYing , Wang Qianyu , Huang Xuanwei , Zhang Yu , Li Yuanhui , Ni Tianyu , Pan Gang , Luo Dingcun , Ni Yeqin 
Published: March 2024

Abstract
Purpose
The detection rate of thyroid nodules has witnessed a significant surge recently. Short video platforms such as Tiktok and Bilibili show tremendous potential in the dissemination of health information. There is a plethora of videos about thyroid nodules on Tiktok and bilibili with uncertain quality and reliability. garnering widespread popularity among users. The aim of study is to investigate the quality and reliability of these videos.
Methods
On December 3rd 2023, the top 100 short videos related to thyroid nodules on Bilibili and Tiktok were collected respectively. After extracting the basic information, the quality and reliability of each video was assessed by using the Global Quality Score (GQS) and DISCERN score. Further, Spearman correlation analyses were applied to examine the correlation among video variables, GQS and DISCERN score.
Results
Compared to Bilibili, Tiktok exhibits greater popularity, as evidenced by higher counts of likes, comments and shares. The median (interquartile range) scores of GQS and DISCERN score were 3 (2–3) on Tiktok while 2 (2–3) on Bilibili. Moreover, it was observed that the videos shared by thyroid specialists demonstrated higher scores both in GQS and DISCERN score than others on Tiktok. Spearman correlation analysis revealed no significant correlation between video variables and scores.
Conclusion
This study revealed that the quality and reliability of thyroid nodules videos on Bilibili and Tiktok were unsatisfactory. Notably, videos shared by thyroid specialists are more likely to exhibit superior quality and reliability, holding greater informative value. Consequently, people should be caution when perusing short videos.
Stability Assessment for Improved Mustard Production in Ecologically Diverse Regions of Jharkhand: Insights from AMMI and GGE
Authors: Kumar Vankadari Akhil , Kumar Niraj , Jagadeesh Kommineni , Kumar Arun , Mahto Chandrasekhar , Lal Hemchandra , Kumar Binay , Ahmed Ekhalaque , Kumar Sunil , Prasad Pradeep , Dubey Himanshu 
Published: March 2024

Abstract
The present study investigates the genetic variability and stability of 15 mustard (Brassica juncea) genotypes across four diverse locations in Jharkhand, employing a randomized block design with three replications. Significant differences among the genotypes are observed, with environmental factors and their interactions exerting a considerable influence. Utilizing the AMMI and GGE biplot methods, the study delves into the intricate interactions affecting economically vital traits such as seed yield per plant and oil percentage. The combined effect of environment and interaction explains a substantial portion of the observed variation of 79.6 and 58.9% on seed yield per plant and oil % respectively. The first two principal components together explained larger portion of 85.2% and 89.5% of the GXE variation of seed yield per plant and oil % respectively. The AMMI analysis had identified that, the genotypes Kranthi, PA-5232 and BAUM-09-12-1 for seed yield per plant and BAUM-08-18, Shivani, DRMRCI-70 and Pusa Bold for oil % are stable performers. The GGE biplot analysis and AMMI have commonly identified BAUM-09-12-1 and Pusa Bold as high yielding and most stable for seed yield per plant and oil % respectively. The results of AMMI identified ranchi as most ideal environment for selection of genotypes for both seed yield per plant and oil%, but GGE differs in-terms with Ranchi as ideal only for oil% and dumka for seed yield per plant. The availability of the above information of genetic variability and stability of genotypes for seed yield per plant and oil % can aid improving mustard production levels and self-sufficiency in edible oils.
Risk and predictive factors of rate of lymph node invasion in breast cancer with axillary involvement
Authors: RICHARD Maxime , HOTTON Judicaël , RAUCH Philippe , MARCHAL Frédéric , GOMES-DAVID Manuel , HEJL Lorraine , Pierret Marion , Abel-Decollogne Fabienne , Routiot Thierry 
Published: March 2024

Abstract

Perpose
Breast cancer is the most common cancer among women in France. Axillary lymph node invasion has a major prognostic impact. The aim of this study was to evaluate the risk and predictive factors of the rate of lymph node invasion in breast cancer with axillary involvement.
Methods
A retrospective study was conducted at the Lorraine Cancer Institute between 1 January 2014 and 31 December 2015. It included patients with breast cancer who underwent breast surgery associated with ALND (either immediately in case of positive FNAC, or secondarily in case of positive SLNB).
Results
In total, 194 patients were included. 99 underwent SLNB and ALND after (Group 1), 95 had a positive node in the FNAC and underwent ALND (Group 2). Multivariate analysis shown that only age at diagnosis (RR 2.79 [1.27; 5.74]), ultrasound tumor size (RR 2.97 [1.20; 7.64]), LVSI (RR 1.88 [1.07; 2.74]), multifocality (RR 2.27[1.35; 5.41]) and high histological grade (RR 3.38 [1.45; 9.74]) were significantly associated with a high axillary involvement. There was a 1.28% recurrence rate at 5 years in Group 1, compared with 28.21% recurrence rate at 5 years in Group 2 (p<0.001).
Conclusions
Age, tumor size (on ultrasound), lymphovascular invasion, multifocality and histological grade are risk and predictors factors of the rate of lymph node invasion. Preoperative positive FNB have a greater degree of lymph node involvement and a worse overall and progression-free survival than patients whose lymph node involvement was discovered at the time of the SLNB.
Development of a local binary pattern descriptor for texture analysis using deep convolutional neural network
Authors: SINGH HARDEEP SINGH , GAGAN GAGANDEEP 
Published: March 2024

Abstract
The machine learning is an important branch of artificial intelligence. In machine learning, the facial expression recognition continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary patterns and its extended variants. This research paper expresses the strength of deep learning techniques that is the deep convolutional neural networks for classification of faces in selected dataset. We applied proposed Deep CNN architecture with local binary patterns and histogram of oriented gradient method. Viola Jones algorithm is applied for detection of faces from Face Recognition dataset. The features of faces are extracted by Binary Phase Component Analysis and information correlation factor gain. Then the classification of images is performed by our proposed deep convolutional neural network (Deep CNN). The performance of the model is evaluated by accuracy, precision, recall, f1-score and confusion matrix. The architecture of CNN constitutes convolutional layer, max-pooling, dense and flatten layers with dropout. The proposed architecture is validated on the Face Recognition dataset. We obtained 0.98 as accuracy which is the very high accuracy of deep CNN model for classification.
TINN: Time-step integration neural network for dynamic analysis of nonlinear structure systems subjected to seismic excitation
Authors: Li Changqing , Li Shuang , Chen Yicheng , Zhai Changhai 
Published: March 2024

Abstract
Dynamic analysis is an essential method for evaluating the seismic response of structures and predicting their seismic damage. The traditional time-step integration methods are computationally time-consuming and sensitive to structural complexity. This study proposes a novel time-step integration neural network (TINN) to perform dynamic analysis for nonlinear structures. TINN is composed of a fully connected feed-forward network and a residual network (ResNet) to simulate the calculation procedure of a single time step in the time-step integration method, and the recursive mechanism is applied to calculate the time history of structural responses. In addition, several techniques are applied to reduce the cumulative error due to the recursive mechanism to improve the accuracy and stability of TINN. TINN could accurately predict the seismic responses of all degrees of freedom (DOF) of the structures, including displacement, velocity, acceleration, and internal force. In particular, the efficiency of TINN doesn't decrease with the increase of structural DOF and the degree of nonlinearity, and it has no restrictions on the amplitude and duration time of the ground motion records. The results on three numerical cases show that TINN performs well in predicting the structural seismic responses with very limited training data available.
High throughput rapid amplicon sequencing for multilocus sequence typing of M. ovipneumoniae using DNA obtained from clinical samples
Authors: Framst Isaac , Wolking Rebecca M. , Schonfeld Justin , Ricker Nicole , Beeler-Marfisi Janet , Chalmers Gabhan , Kamath Pauline L. , Maboni Grazieli 
Published: March 2024

Abstract
Background
Spillover events of Mycoplasma ovipneumoniae have devastating effects on wild bighorn sheep populations. Multilocus sequence typing (MLST), a common method for tracking bacterial lineages, is used to monitor spillover events and the spread of M. ovipneumoniae between populations. Most work involving M. ovipneumoniae typing has used Sanger sequencing, however, this technology is time consuming, expensive, and is not well suited to efficient batch sample processing. Our study aimed to develop and validate a workflow for multilocus sequence typing of M. ovipneumoniae using Nanopore Rapid Barcoding sequencing and multiplex PCR. We compare the workflow with Nanopore Native Barcoding library preparation and Illumina MiSeq amplicon protocols to determine the most accurate and cost-effective method for sequencing multiplex amplicons.
Results
A multiplex PCR was optimized for four housekeeping genes of M. ovipneumoniae using archived DNA samples from wild sheep. Sequences recovered from Nanopore Rapid Barcoding correctly identified all MLST types with the shortest total workflow time, and lowest cost per sample when compared to Nanopore Native Barcoding, and Illumina MiSeq methods.
Conclusion
Our proposed workflow serves as a convenient and effective diagnostic method for strain typing of M. ovipneumoniae, and could be applied to other bacterial MLST schemes. The workflow is suitable for diagnostic settings where reduced hands-on time, cost and multiplexing capabilities are important.
Robust multivariate regression for high-dimensional data
Authors: Santana Juan Fernando Herrera , González Rubén Darío Guevara 
Published: March 2024

Abstract
Multivariate multiple linear regression is a widely used statistical technique for modelling relationships between some response variables and several predictor variables. Traditional likelihood-based methods can produce very misleading results in the presence of outliers. In this work, we propose two robust multivariate regression methods designed to handle high-dimensional data: one based on the minimum regularized covariance determinant estimator, a robust estimator of location and scatter for high-dimensional data; and another based on dimensionality reduction using robust sparse principal component analysis. Through a study simulation, we evaluate the robustness and efficiency of the estimators obtained, the ability of the methodologies to correctly classify observations in contaminated datasets, and the computational cost. A real data application illustrates the use of the proposed methodologies.
Prognostication of mechanical properties of Banana and Jute fiber reinforced concrete using AI
Authors: B.T Ashwini , Y.M Vijaya kumar , B.S Keerthi Gowda , D.T Naveen Kumar 
Published: March 2024

Abstract
In the present study, using data from experiments, an artificial neural network (ANN) model has been created to predict the strengths of concrete with banana and jute fibers at concrete ages of 7 and 28 days. The parameters considered for the ANN inputs are Grade of Concrete, FA, CA, W/C, Super Plasticizer, length, diameter and Density of fiber, curing in days, NaOH Concentration, Fiber treatment in hours, percentage of fiber added. The results from training and testing models have shown the great potential of ANN in predicting the compressive and flexural strengths of banana and jute fiber reinforced concrete.