Retinal optical coherence tomography intensity spatial correlation features as new biomarkers for confirmed Alzheimer’s disease by Zi Jin, Xinmin Wang, Ying Lang, Yufeng Song, Huangxiong Zhan, Wuge Shama, Yingying Shen, Guihua Zeng, Faying Zhou, Hongjian Gao, Shuling Ye, Yanjiang Wang, Fan Lu, Meixiao Shen

“…Except retinal thickness, we developed the grey level co-occurrence matrix algorithm to extract retinal OCT intensity spatial correlation features (OCT-ISCF), including angular second matrix (ASM), correlation (COR), and homogeneity (HOM), one-way analysis of variance was used to compare the differences in retinal parameters among the groups, and to analyze the correlation with brain Aβ plaques and cognitive scores. …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm. Results For task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. …”
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Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach by Victor Chaumeau, Sunisa Sawasdichai, Thu Zar Ma Ma Moe Min, Thithiwarada Kularbkeeree, Naw Jaruwan, Naw Gloria, Naw Yu Lee, Muesuwa Trackoolchengkaew, Monticha Phanaphadungtham, Patcharamai Rongthong, Aritsara Inta, Wanitda Watthanaworawit, François Nosten

“…A simulation experiment was carried out by querying the reference database with the spectra of the test panel to evaluate the performance of species identification with MALDI–TOF MS at varying thresholds of the cross-correlation index for the algorithm to output an identification result and with varying numbers of technical replicates for the tested specimens, considering PCR identification results as the reference. …”
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Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topso... by Calogero Schillaci, Simone Scarpa, Felipe Yunta, Aldo Lipani, Fernando Visconti, Gábor Szatmári, Kitti Balog, Triven Koganti, Mogens Greve, Giulia Bondi, Georgios Kargas, Paraskevi Londra, Fuat Kaya, Giuseppe Lo Papa, Panos Panagos, Luca Montanarella, Arwyn Jones

“…In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. The final model resulted in an R2 of 0.302 with an RMSE of 0.265 dS m−1 for soil samples not used for model calibration. …”
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Implementation of Compensation-Sensitive Shot-Style-Feedback of Aiming Point Training Data at the Swiss Shooting Federation by Dino Tartaruga, Ralf Kredel

“…The analysis is performed according to the methods proposed in our study, including the use of a validated movement phase segmentation algorithm of aiming point trajectory data and the identification of compensation-sensitive shot styles based on this data. …”
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EpCare: Prototipe Sistem Detektor Pre-Iktal Pasien Epilepsi Berbasis Fitur CSI dari Sinyal EKG 1 Kanal Menggunakan AD8232 by Diah P. Wulandari, Arief Kurniawan, Alvin Nathanael, Santi W. Purnami, Anda L. Juniani, Wardah R. Islamiyah

“…Patient’s heart electricity is measured using 3 electrodes which are connected to AD8232 to represent 1 channel signal. Pan-Tompkins algorithm is implemented to obtain RR intervals of ECG signals. …”
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Comparison of IMO vifa24plus(1-2) and Humphrey Field Analyzer 24-2 by Takagi Y, Asano R, Yamashita K, Sakai Y, Yokoyama S, Ichikawa K, Ichikawa K

“…On the same day, the HFA 24– 2 test using the Swedish Interactive Threshold Algorithm Standard and the IMO vifa 24plus(1– 2) test using the Ambient Interactive Zippy Estimated strategy were performed. …”
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Prospects for the Use of Quasi-Mersen Numbers in the Design of Parallel-Serial Processors by Aruzhan Kadyrzhan, Kaisarali Kadyrzhan, Akhat Bakirov, Ibragim Suleimenov

“…It is shown that a serial-parallel processor, comparable in bit capacity to a 16-bit binary processor, can be implemented based on an algorithm built on the residue number system, a distinctive feature of which is the use of the first four quasi-Mersenne numbers, i.e., prime numbers representable as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>p</mi><mi>k</mi></msub><mo>=</mo><msup><mn>2</mn><mi>k</mi></msup><mo>+</mo><mn>1</mn><mo>,</mo><mo> </mo><mi>k</mi><mo>=</mo><mn>1</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>3</mn><mo>,</mo><mn>4</mn></mrow></semantics></math></inline-formula>. …”
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Pembentukan Daftar Stopword Menggunakan Term Based Random Sampling Pada Analisis Sentimen Dengan Metode Naïve Bayes (Studi Kasus: Kuliah Daring Di Masa Pandemi) by Raditya Rinandyaswara, Yuita Arum Sari, Muhammad Tanzil Furqon

“…This study uses a stopword list formed by the Term Based Random Sampling algorithm. In Term Based Random Sampling, there are 3 parameters, namely Y for the number of random word retrieval repetitions, X for the lowest number of weights in Y repetitions, and L as the percentage of the number of stopwords you want to use. …”
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VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction by Omer Nacar, Mohamed Abdelkader, Lahouari Ghouti, Kahled Gabr, Abdulrahman Al-Batati, Anis Koubaa

“…Quantitatively, the proposed GRU-based prediction algorithm demonstrates superior performance, achieving a mean square error (MSE) ranging from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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Analisis Dampak Kabut Asap dari Kebakaran Hutan dan Lahan dengan Pendekatan Text Mining by Zuliar Efendi, Imas Sukaesih Sitanggang, Lailan Syaufina

“…The method used is a text mining technique that uses a clustering algorithm. The data used are tweet data related to haze in Riau Province in the range 11-17 September 2019 as well as hotspot data and Sentinel-2 imagery. …”
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Non-Luminal Disease Score for Everolimus in Patients with&nbsp;Hormone Receptor&#x2011;positive and Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: A Mult... by Tan Y, Jiang H, Tian X, Ma F, Wang J, Zhang P, Xu B, Fan Y, Zhao W

“…Yujing Tan,1,&ast; Hanfang Jiang,2,&ast; Xinzhu Tian,1,&ast; Fei Ma,1 Jiayu Wang,1 Pin Zhang,1 Binghe Xu,1 Ying Fan,1 Weihong Zhao3 1Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, People’s Republic of China; 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Breast Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China; 3Department of Medical Oncology, Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ying Fan; Weihong Zhao, Email fanying@cicams.ac.cn; zhaowh0818@163.comPurpose: This study aims to explore the role of the non-luminal disease score (NOLUS) for everolimus in patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC).Methods: NOLUS has previously been established as an algorithm: NOLUS (0– 100) = − 0.45 × ER(%) − 0.28 × PR(%) + 0.27 × Ki67(%) + 73. …”
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Analisis Sentimen Berbasis Aspek Pada Ulasan Pelanggan Restoran Menggunakan Algoritma Support Vector Machine (Studi Kasus: Depot Bamara) by Muhammad Fariz Firdaus, Dian Eka Ratnawati, Nanang Yudi Setiawan

“…Terms found in the reviews were weighted using the frequency inverse document frequency (TF-IDF) weighting technique and classified using the support vector machine (SVM) algorithm. The classification model was tested with k-fold cross validation, resulting in an average accuracy of 92%, indicating good performance. …”
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Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis by Sharad Patel, Gurkeerat Singh, Samson Zarbiv, Kia Ghiassi, Jean-Sebastien Rachoin

“…We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. …”
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Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics by Simin Luo, Yuhong Zhu, Zhanli Guo, Chuan Zheng, Xi Fu, Fengming You, Xueke Li

“…The relationship of key genes with immune cells and other cells was evaluated by immune infiltration algorithm and single-cell transcription analysis. Drug prediction was performed by cMAP and the obtained drugs were molecularly docked with the key genes. …”
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Norepinephrine use in cardiogenic shock patients is associated with increased 30 day mortality by Xin Lu, Xue Wang, Yanxia Gao, Joseph Harold Walline, Shiyuan Yu, Zengzheng Ge, Mubing Qin, Huadong Zhu, Yi Li

“…After PSM, 222 cases from each group were matched using a 1:1 matching algorithm. Thirty day mortality for patients treated with norepinephrine was significantly higher than for those in the non‐norepinephrine group (41% vs. 30%, OR 1.61, 95% CI 1.09–2.39, P = 0.017; HR 1.50, 95% CI 1.09–2.06, P = 0.013). …”
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The azimuthal correlation between the leading jet and the scattered lepton in deep inelastic scattering at HERA by ZEUS Collaboration, I. Abt, R. Aggarwal, V. Aushev, O. Behnke, A. Bertolin, I. Bloch, I. Brock, N. H. Brook, R. Brugnera, A. Bruni, P. J. Bussey, A. Caldwell, C. D. Catterall, J. Chwastowski, J. Ciborowski, R. Ciesielski, A. M. Cooper-Sarkar, M. Corradi, R. K. Dementiev, S. Dusini, J. Ferrando, B. Foster, E. Gallo, D. Gangadharan, A. Garfagnini, A. Geiser, G. Grzelak, C. Gwenlan, D. Hochman, N. Z. Jomhari, I. Kadenko, U. Karshon, P. Kaur, R. Klanner, U. Klein, I. A. Korzhavina, N. Kovalchuk, M. Kuze, B. B. Levchenko, A. Levy, B. Löhr, E. Lohrmann, A. Longhin, F. Lorkowski, E. Lunghi, I. Makarenko, J. Malka, S. Masciocchi, K. Nagano, J. D. Nam, Yu. Onishchuk, E. Paul, I. Pidhurskyi, A. Polini, M. Przybycień, A. Quintero, M. Ruspa, U. Schneekloth, T. Schörner-Sadenius, I. Selyuzhenkov, M. Shchedrolosiev, L. M. Shcheglova, N. Sherrill, I. O. Skillicorn, W. Słomiński, A. Solano, L. Stanco, N. Stefaniuk, B. Surrow, K. Tokushuku, O. Turkot, T. Tymieniecka, A. Verbytskyi, W. A. T. Wan Abdullah, K. Wichmann, M. Wing, S. Yamada, Y. Yamazaki, A. F. Żarnecki, O. Zenaiev

“…Jets were reconstructed using the $$k_\textrm{T}$$ k T algorithm with the radius parameter $$R = 1$$ R = 1 . …”
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Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study by Chi Pui Pang, Weiqi Chen, Mingzhi Zhang, Tsz Kin Ng, Yi Zheng, Guihua Zhang, Jian-Wei Lin, Ji Wang, Jie Ji, Ling-Ping Cen, Peiwen Xie, Yongqun Xiong, Hanfu Wu, Dongjie Li

“…The performance of the DL was compared with DR experts.Setting DR screening programmes from three hospitals and the Lifeline Express Diabetic Retinopathy Screening Program in China.Participants 83 465 images of 39 836 eyes from 21 716 patients were annotated, of which 53 211 images were used as the development set and 30 254 images were used as the external validation set, split based on centre and period.Main outcomes Accuracy, F1 score, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), Cohen’s unweighted κ and Gwet’s AC1 were calculated to evaluate the performance of the DL algorithm.Results In the external validation set, the five classifiers achieved an accuracy of 0.915–0.980, F1 score of 0.682–0.966, sensitivity of 0.917–0.978, specificity of 0.907–0.981, AUROC of 0.9639–0.9944 and AUPRC of 0.7504–0.9949. …”
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Computational analysis of reproducibility and image quality of 2011 and 2017 models of Heidelberg Spectralis SD-OCT machines by Kyoung A Viola Lee, Corey Tesdahl, Keith Zimmerman, Kimberly Jun, Sabrina Khalil, Alexander Shahin, Abdullah Abou-Samra, Ramesh Ayyala, Radouil Tzekov

“…Methods and testing: TRT measurements were performed using the Posterior Pole Algorithm (PPA) and Early Treatment Diabetic Retinopathy Study (ETDRS) grid protocols. …”
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High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds by S. Bovo, M. Bolner, G. Schiavo, G. Galimberti, F. Bertolini, S. Dall’Olio, A. Ribani, P. Zambonelli, M. Gallo, L. Fontanesi

“…The molecular data were analysed using a bioinformatics pipeline specifically designed for identifying differentially abundant metabolites between the two breeds in a robust and statistically significant manner, including the Boruta algorithm, which is a Random Forest wrapper, and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) for feature selection. …”
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