DEPEND study protocol: early detection of patients with pancreatic cancer – a pilot study to evaluate the utility of faecal elastase-1 and 13C-mixed triglyceride breath test as scr... by Christopher D Byrne, Sam Wilding, Paul Remi Afolabi, Declan McDonnell, Victoria Goss, Jocelyn Walters, Zaed Z Hamady

“…Potential biomarkers of PC showing abilities to discriminate between patients with PC from healthy subjects or patients with chronic pancreatitis will be selected by metabolomic analysis.Ethics and dissemination The study was approved by the North of Scotland Research and Ethics Committee on 1 October 2020 (reference: 20/NS/0105, favourable opinion granted). …”
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Advances in Biomedical Missing Data Imputation: A Survey by Miriam Barrabes, Maria Perera, Victor Novelle Moriano, Xavier Giro-I-Nieto, Daniel Mas Montserrat, Alexander G. Ioannidis

“…These methods range from traditional statistical analysis to more modern approaches such as discriminative machine learning models and deep generative networks. …”
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Revisiting the Variation of the Ionospheric Irregularities in the Low Latitude Region of China Based on Small Regional Geodetic GNSS Station Network by H. Y. Gao, D. H. Zhang, Z. Z. Liu, S. J. Sun, Y. Q. Hao, Z. Xiao

“…Via a priori knowledge of scintillation morphology, the method to determine the baseline ROTI for discriminating quiet and disturbed conditions is provided, and the abnormal data of ROTI and LoL unrelated to ionosphere are found and eliminated. …”
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P53 and Expression of Immunological Markers May Identify Early Stage Thyroid Tumors by Marjory Alana Marcello, Elaine Cristina Morari, Lucas Leite Cunha, Aline Carolina De Nadai Silva, Dirce Maria Carraro, André Lopes Carvalho, Fernando Augusto Soares, José Vassallo, Laura Sterian Ward

“…P53 expression was observed more frequently in malignant than in benign lesions (P<0.0001) and helped discriminate follicular patterned lesions. In addition, p53 was more frequent in smaller (P=0.0015), unique tumors (P=0.0286), with thyroiditis (P=0.0486) and without metastasis at diagnosis (P=0.0201). …”
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Metagenomic and Transcriptomic Analysis Reveals Crosstalk Between Intratumor Mycobiome and Hosts in Early‐Stage Nonsmoking Lung Adenocarcinoma Patients by Yaohui Sun, Zhiming Gan, Shijiancong Liu, Sheng Zhang, Wei Zhong, Jian Liu, Xiuting Huang, Wei He, Hongcheng Zhong, Qingdong Cao

“…Fungi, as a potential predictive tool, can be used as an additional resource for accurately detecting and discriminating individuals with ES‐LUAD.…”
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Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network by Lisen Yan, Jun Peng, Zeyu Zhu, Heng Li, Zhiwu Huang, Dirk Uwe Sauer, Weihan Li

“…The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information. …”
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Object-Level Contrastive-Learning-Based Multi-Branch Network for Building Change Detection from Bi-Temporal Remote Sensing Images by Shiming Li, Fengtao Yan, Cheng Liao, Qingfeng Hu, Kaifeng Ma, Wei Wang, Hui Zhang

“…Finally, an attention-based discriminative feature generation and fusion block is designed to enhance the representation of multi-scale change features. …”
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A measure of modifiable lifestyle factors shaping subjective cognitive reserve in the general population by Carmen Moret-Tatay, Carmen Moret-Tatay, José María Tormos Muñoz, José María Tormos Muñoz, Alvaro Pascual-Leone, Alvaro Pascual-Leone

“…Convergent validity was confirmed through a positive correlation between SCR scores and resilience (BRCS) as well as with other cognitive reserve measures, indicating consistency in evaluating cognitive reserve across various instruments. Furthermore, discriminant validity was demonstrated by a significant negative correlation between SCR scores and subjective cognitive decline, suggesting that individuals with higher cognitive reserve experience lower levels of perceived cognitive decline. …”
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Scoring of swine lung images: a comparison between a computer vision system and human evaluators by Robert Valeris-Chacin, Beatriz Garcia-Morante, Marina Sibila, Albert Canturri, Isaac Ballarà Rodriguez, Ignacio Bernal Orozco, Ramon Jordà Casadevall, Pedro Muñoz, Maria Pieters

“…The CVS showed moderate accuracy (62–71%) in discriminating between non-lesioned and lesioned lung lobes in all but the diaphragmatic lobes. …”
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Olfaction-based learned preference assessment without the use of motivational fear or motivational weight loss by Sara E. Moss, Ekaterina S. McCurdy, Natalya N. Thomas, Danielle Gulick, Angela M. Poff, Dominic P. D'Agostino

“…We assessed whether this protocol discriminated learning deficit induced by lipopolysaccharide (LPS) administration.ConclusionWe conclude that this protocol is a viable tool for assessing learning abilities in preclinical models with auditory or visual deficits, motor impairments, or an inability to tolerate motivational weight loss.…”
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Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds by Jing Huang, Xuenan Zhang, Hang Yang, Zhenbiao Li, Zhengfang Xue, Qingqing Wang, Xinyuan Zhang, Shenghua Ding, Zisheng Luo, Yanqun Xu

“…Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). …”
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A Novel Semi-Supervised Learning Method Based on Fast Search and Density Peaks by Fei Gao, Teng Huang, Jinping Sun, Amir Hussain, Erfu Yang, Huiyu Zhou

“…When the labeled samples satisfy a certain requirement, S2DP uses fast search and a density peak clustering method to detect reliable unlabeled samples based on the weighted kernel Fisher discriminant analysis (WKFDA). Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. …”
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Serum levels of the Parkinson's disease-linked protein Parkin are specifically elevated in COVID-19 by Nadezhda G. Gumanova, Natalya L. Bogdanova, Alexander Yu. Gorshkov

“…This analysis identified Parkin as a top protein discriminating between the SARS-positive and negative status. …”
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Are lateralized and bold fish optimistic or pessimistic? by F. Berlinghieri, G. Rizzuto, L. Kruizinga, B. Riedstra, TGG. Groothuis, C. Brown

“…We used the judgement bias test with the go/no-go procedure where fish were first trained to discriminate between a black and white card and, after reaching a minimum learning criterion, tested their response to an ambiguous card (grey). …”
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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network by Xiao Feng, Xiao Feng, Zhongyuan Guo, Zhongyuan Guo, Sam Kwong

“…First, a base feature extractor is employed to extract the essential features of EEG frequencies; to enhance the discriminative feature learning ability, the residual shrinkage building unit with attention mechanism is adopted to perform adaptive feature recalibration and soft threshold denoising inside the residual network is further applied to achieve automatic feature extraction. …”
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Efficient Anomaly Detection Algorithm for Heart Sound Signal by Zhihai Liu, Wen Liu, Zheng Gu, Feng Yang

“…Currently, existing heart sound datasets suffer from imbalanced data proportions, complex feature types, and low discriminative power between systolic and diastolic murmurs, resulting in the suboptimal performance of deep learning algorithms in detection. …”
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Human Islet Amyloid Polypeptide Fibril Binding to Catalase: A Transmission Electron Microscopy and Microplate Study by Nathaniel G. N. Milton, J. Robin Harris

“…This suggests that catalase may be able to discriminate between different structural forms of IAPP fibrils. …”
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Development and validation of the gift reciprocation anxiety scale (GRAS) for youths and adults in intimate relationships by Mohd. Ashik Shahrier, Shakira Khatun

“…In addition, the item response theory (IRT) analyses confirmed that the items of the GRAS had high discriminative power, satisfactory threshold parameters, and covered a wide range of the latent trait. …”
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ZleepNet: A Deep Convolutional Neural Network Model for Predicting Sleep Apnea Using SpO2 Signal by Hnin Thiri Chaw, Thossaporn Kamolphiwong, Sinchai Kamolphiwong, Krongthong Tawaranurak, Rattachai Wongtanawijit

“…We conducted experiments to evaluate the performance of the proposed CNN using real patient data and compared them with traditional machine learning methods such as least discriminant analysis (LDA) and support vector machine (SVM), baggy representation tree, and artificial neural network (ANN) on publicly available sleep datasets using the same parameter setting. …”
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Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification by Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam

“…However, traditional deep learning backbones often produce representations that are not sufficiently discriminative or interpretable for detailed plant disease analysis. …”
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