Cystic Fibrosis Newborn Screening: A Systematic Review-Driven Consensus Guideline from the United States Cystic Fibrosis Foundation by Meghan E. McGarry, Karen S. Raraigh, Philip Farrell, Faith Shropshire, Karey Padding, Cambrey White, M. Christine Dorley, Steven Hicks, Clement L. Ren, Kathryn Tullis, Debra Freedenberg, Q. Eileen Wafford, Sarah E. Hempstead, Marissa A. Taylor, Albert Faro, Marci K. Sontag, Susanna A. McColley

“…Newborn screening for cystic fibrosis (CF) has been universal in the US since 2010; however, there is significant variation among newborn screening algorithms. Systematic reviews were used to develop seven recommendations for newborn screening program practices to improve timeliness, sensitivity, and equity in diagnosing infants with CF: (1) The CF Foundation recommends the use of a floating immunoreactive trypsinogen (IRT) cutoff over a fixed IRT cutoff; (2) The CF Foundation recommends using a very high IRT referral strategy in CF newborn screening programs whose variant panel does not include all CF-causing variants in CFTR2 or does not have a variant panel that achieves at least 95% sensitivity in all ancestral groups within the state; (3) The CF Foundation recommends that CF newborn screening algorithms should not limit <i>CFTR</i> variant detection to the F508del variant or variants included in the American College of Medical Genetics-23 panel; (4) The CF Foundation recommends that CF newborn screening programs screen for all CF-causing <i>CFTR</i> variants in CFTR2; (5) The CF Foundation recommends conducting <i>CFTR</i> variant screening twice weekly or more frequently as resources allow; (6) The CF Foundation recommends the inclusion of a <i>CFTR</i> sequencing tier following IRT and <i>CFTR</i> variant panel testing to improve the specificity and positive predictive value of CF newborn screening; (7) The CF Foundation recommends that both the primary care provider and the CF specialist be notified of abnormal newborn screening results. …”
Get full text

Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts: Progress and perspectives by Fan Cheng, Lei Yutian, Mo Jinhan, Wang Huilong, Wu Qiuting, Cai Jiena

“…This review aims to present the progress and perspectives on the effective utilization of novel machine learning paradigms for three major building energy management tasks, i.e., building energy predictions, fault detection and diagnosis, and control optimizations. …”
Get full text

Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning studyResearch in context by Shan Jiang, Shuai Dai, Yulin Li, Xianlong Zhou, Cheng Jiang, Cong Tian, Yana Yuan, Chengwei Li, Yan Zhao

“…However, due to its low positive predictive value (PPV), the false alarms may increase in actual clinical practice. …”
Get full text

Random-forest-based task pricing model and task-accomplished model for crowdsourced emergency information acquisition by Wenxiang Li, Shengqun Chen, Lijin Lin, Li Chen

“…Our simulation results demonstrate that the proposed method has an average reduction in Mean Squared Error (MSE) by 44.16 % for task pricing and an average increase in accuracy of 17.71 % for task-accomplished prediction compared to traditional regression models. …”
Get full text

Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

“…A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. …”
Get full text

Multi-Factor Modeling Method of the Load Sharing Ratio under Moving Train Loads by Yong Liu, Shiyu Zhang, Yang Jin, Yuxiang Song

“…At the same time, a procedure for establishing the LSR multi-factor prediction model is put forward, namely, we (1) determine the LSR function form and the fitting algorithm; (2) perform parameter sensitivity analysis to determine the main influencing parameters of the LSR function; and (3) design a quadratic regression orthogonal test to obtain the prediction formula of the LSR function coefficients. …”
Get full text

An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures by Bin Li, Xuewen Rong, Yibin Li

“…For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. …”
Get full text

Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…Evaluation revealed that XG Boost, AdaBoostM1, and Gradient Boosting achieved classification accuracies of 100%, with performance metrics including F1 score, MCC, Precision, Recall, AUC-ROC, and AUC-PR all equal to 1.00, indicating reliable predictions of diabetes presence. Researchers and practitioners can leverage the predictive model developed in this work to make quick predictions of diabetes mellitus, which could save many lives.…”
Get full text

STING is significantly increased in high-grade glioma with high risk of recurrence by Meishi Zhong, Manmei Long, Chenjie Han, Saiyan Ji, Qingyuan Yang

“…Integrated analysis showed that ATRX is positively correlated with STING in high-grade glioma (HGG) with wild-type ATRX and IDH1. Then, a relapse predictive risk-scoring model was established using the least absolute shrinkage and selection operator regression algorithms. …”
Get full text

Structural insights into SOD1: from in silico and molecular dynamics to experimental analyses of ALS-associated E49K and R115G mutants by Seyed Mahdi Hosseini Faradonbeh, Bagher Seyedalipour, Nasrin Keivan Behjou, Kimiya Rezaei, Payam Baziyar, Saman Hosseinkhani

“…Our analysis was performed through multiple algorithms on the structural characterization of the hSOD1 protein using computational predictions, molecular dynamics (MD) simulations, and experimental studies to understand the effects of amino acid substitutions. …”
Get full text

Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule by Dingyuan Fan, Fei Yang, Jinghao Ji, Zexi Zhang

“…Finally, taking Beijing as the research object, the Holt exponential smoothing method and the BP neural network are selected to verify the effect of spline interpolation and IPSO-gradient difference acceleration law on prediction accuracy. The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. …”
Get full text

Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma by Xiang Li, Donghao Yin, Jiahao Geng, Yanyu Xu, Zijing Xu, Xuemeng Yang, Quanwei Li, Zimeng Shang, Zhiyun Yang, Zhong Xu, Jiabo Wang, Enxiang Zhang, Xinhua Song

“…Results We developed an 18-gene signature based on PCD and metabolism with strong predictive value for overall survival (OS) in HCC patients. …”
Get full text

Enhancing DDoS Attack Classification through SDN and Machine Learning: A Feature Ranking Analysis by Aymen AlAwadi, Kawthar Rasoul ALesawi

Get full text

Thermal performance analysis of gas turbine power plant using soft computing techniques: a review by Ravindra S. Surase, Ramakrishna Konijeti, Ramchandra P. Chopade

Get full text

Analysis of Comprehensive Artificial Neural Network Computer Media Aided Construction of Economic Forecasting Model by Tianfeng Li

“…Based on this, this paper first analyzes the economic system prediction, then studies the ANN medium model and learning algorithm, and finally verifies the effectiveness of the ANN economic prediction model integrating computer medium.…”
Get full text

Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles by Janjhyam Venkata Naga Ramesh, C. Dastagiraiah, Suraya Mubeen, W. Deva Priya, M. Kameswara Rao, B. H. K. Bhagat Kumar

Get full text

Comment on “Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation” by Hinpetch Daungsupawong, Viroj Wiwanitkit

“…By incorporating advanced imaging techniques and machine learning algorithms, researchers can improve the accuracy and efficiency of diagnostic processes. …”
Get full text

Opinion mining in e-commerce: Evaluating machine learning approaches for sentiment analysis by L. Lakshmi, Ali B.M. Ali, K Dhana Sree Devi, Muhammad Rafiq, Iskandar Shernazarov, Nashwan Adnan Othman, M. Ijaz Khan

Get full text

Basketball teaching methods based on 3D-Convolutional neural network by Chao Huang, Xian Wu

Get full text

Performing a Multi-Stage Mathematical and Informational Task "Framing the First-Order Mandelbrot Set, Julia Sets, the Structure of Fixed Points of a Family of Polynomials of the Th... by Valeriy Sekovanov, Vladimir Ivkov, Elena Smirnova, Larisa Rybina

Get full text