Comparison of Microbiological Profiles of Primary Hip and Knee Peri-Prosthetic Joint Infections Treated at Specialist Centers Around the World by Emin Suha Dedeogullari, Pablo Slullitel, Isabel Horton, Bulent Atilla, Saif Salih, Paul Monk, Ahmet Mazhar Tokgozoglu, Michael Goplen, Bonita Tsang, Martin Buljubasich, Hesham Abdelbary, Simon Garceau, George Grammatopoulos

“…However, most studies are limited to single-center or intra-country multicenter analyses, often including mixed cohorts of primary and revision PJI cases, with limited data regarding global antibiotic resistance patterns. This study compared the microbiological characteristics, polymicrobial culture rates, prevalence of culture-negative infections, and antibiotic resistance patterns in PJI cases across five referral centers from five continents. …”
Get full text

Immunodiagnostic plasma amino acid residue biomarkers detect cancer early and predict treatment response by Cong Tang, Patrícia Corredeira, Sandra Casimiro, Qi Shi, Qiwei Han, Wesley Sukdao, Ana Cavaco, Cecília Melo-Alvim, Carolina Ochôa Matos, Catarina Abreu, Steven Walsh, Gonçalo Nogueira-Costa, Leonor Ribeiro, Rita Sousa, Ana Lorena Barradas, João Eurico Fonseca, Luís Costa, Emma V. Yates, Gonçalo J. L. Bernardes

“…By measuring the total concentrations of cysteine, free cysteine, lysine, tryptophan, and tyrosine protein-incorporated biomarkers and analyzing the results with supervised machine learning, we identify 78% of cancers with 0% false positive rate (N = 97) with an AUROC of 0.95. The cancer, healthy, and autoimmune/infectious biomarker pattern are statistically significantly different (p < 0.0001). …”
Get full text

AEMS: Adaptive Ensemble GNNs for Multibehavior Stream Recommendation by Ritchie Natuan Caibigan, Punyaphol Horata, Pusadee Seresangtakul

“…The AEMS synergizes long-term preference patterns (derived from historical interactions) with real-time user intents and item attributes (captured through multibehavior signals), integrating them via an adaptive ensemble neural gating mechanism. …”
Get full text

Influences of coal-rock interface morphology’s spatiotemporal evolutionary characteristics on top coal caving law by Yupeng Shen, Tuo Yang, Jianzhuang Liu, Yang Li, Jianqiao Luo, Xiaonan Zhang

“…The sinusoidal periodic change pattern in top coal caving volume became more evident, which decreased the top coal recovery rate. …”
Get full text

Deep learning driven prediction and comparative study of surrounding rock deformation in high speed railway tunnels by Zeping Yang, Zhikai Cheng, Da Wu

“…The methodology incorporates quadratic exponential smoothing for outlier mitigation, followed by sequential feature extraction using convolutional neural networks (CNNs) and bidirectional gated recurrent units (GRUs). Comparative experiments demonstrate the model’s superiority over conventional architectures including RNN, LSTM, GRU, and CNN-GRU. …”
Get full text

Forecasting Sales in Live-Streaming Cross-Border E-Commerce in the UK Using the Temporal Fusion Transformer Model by Qi Zhang, Xue Li, Pengbin Gao

“…Our multimodal approach integrates diverse time series data, including historical sales, key opinion leader (KOL) influence, and seasonal patterns. The Temporal Fusion Transformer (TFT) model demonstrated consistently lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) across all forecasting horizons compared to other machine learning approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Unit(GPU)-accelerated architectures. …”
Get full text

A clustering-based federated deep learning approach for enhancing diabetes management with privacy-preserving edge artificial intelligence by Xinyi Yang, Juan Li

“…We develop tailored models that enhance prediction accuracy by clustering patients based on carbohydrate (CHO) intake patterns. Utilizing Simple Recurrent Neural Network (SimpleRNN) and Gated Recurrent Unit (GRU) methods, the study evaluates the performance of local patients who contribute to training the cluster and global (non-cluster) models. …”
Get full text

Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning by Yanli Zhao, Zongduo Liu, Junjie Pang

“…Traditional detection approaches typically rely on statistical features while overlooking the interaction patterns and structural dependencies among traffic flows. …”
Get full text

Estimating medium-term (40 years) carbon uptake in living biomass from Life Terra’s afforestation and reforestation actions: challenges and recommendations by Jorge Palmero-Barrachina, Petr Blazek, Santiago Sabaté, Santiago Sabaté, Teresa Sauras-Yera, Samuel Allasia-Grau, Daniel Nadal-Sala, Daniel Nadal-Sala, Sven Kallen, Tiago de Santana, Emil Cienciala, Emil Cienciala

“…The study showed significant regional variations in planting density and growth patterns. Initial planting densities in timber plantations varied substantially across biogeographic regions (1,869–7,702 trees/ha), following exponential decline patterns over time. …”
Get full text

Exciton-phonon coupling and phonon-assisted exciton relaxation dynamics in In1-xGaxP quantum dots by Beiye C. Li, Kailai Lin, Ping-Jui E. Wu, Aritrajit Gupta, Kaiyue Peng, Siddhartha Sohoni, Justin C. Ondry, Zirui Zhou, Caitlin C. Bellora, Young Jay Ryu, Stella Chariton, David J. Gosztola, Vitali B. Prakapenka, Richard D. Schaller, Dmitri V. Talapin, Eran Rabani, Gregory S. Engel

“…We identify a slower hot exciton cooling rate in In0.62Ga0.38P/ZnS, attributed to the presence of ‘energy-retaining’ valley exciton states with strong exciton-phonon coupling. …”
Get full text

Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</sc>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. …”
Get full text

From access to reserve: antimicrobial resistance among etiological agents of central line-associated bloodstream infections in the view of WHO’s AWaRe antimicrobial spectrum by Anand, Gargee, Lahariya, Rijhul, Priyadarshi, Ketan, Sarfraz, Asim

“…Furthermore, a fluctuation in CLABSI rates was also observed, with a significant reduction in infection rates in 2024 after the implementation of enhanced infection control practices. …”
Get full text

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…Abstract Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures. …”
Get full text

Classification of hoarding and comorbid neuropsychiatric symptoms by Sara K. Nutley, Catherine W. Striley, Linda B. Cottler, Joseph Eichenbaum, Rachel L. Nosheny, R. Scott Mackin, Carol A. Mathews

“…Conclusions: Neuropsychiatric symptom patterns among individuals with hoarding are heterogenous in nature and uniquely associated with clinical features and functional outcomes. …”
Get full text

Monitoring and Assessment of Slope Hazards Susceptibility Around Sarez Lake in the Pamir by Integrating Small Baseline Subset InSAR with an Improved SVM Algorithm by Yang Yu, Changming Zhu, Majid Gulayozov, Junli Li, Bingqian Chen, Qian Shen, Hao Zhou, Wen Xiao, Jafar Niyazov, Aminjon Gulakhmadov

“…The maximum deformation rate along the shoreline increased from 280 mm/yr to 480 mm/yr, with a marked acceleration observed between 2022 and 2023. …”
Get full text

Toward AI-Augmented Formal Verification: A Preliminary Investigation of ENGRU and Its Challenges by Chanon Dechsupa, Teerapong Panboonyuen, Wiwat Vatanawood, Praisan Padungweang, Chakchai So-In

“…In this paper, we present ENGRU (Enhanced Gated Recurrent Units), a novel deep learning-based approach for formal verification. …”
Get full text

Community-Based Evaluation of Hypertension Awareness and Knowledge Among Underserved Bateyes in the Dominican Republic by Dang Nguyen, Ryan Shargo, Nityanand Jain, Yusuf Qureshi, Lovepriya Suthaharan, Ramez M. Odat, Cameron Sabet, Miyaz Ansari, Hamzah Khuddus, Phat K. Huynh, Michael J. Diaz, Reginald Kerolle, Ernest Kissi Kontor, Lindy G. Davidson

“…Background: Globally, hypertension rates are rising, particularly in low-income nations where access to healthcare and education remains limited. …”
Get full text

CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING by Melina Melina, Sukono Sukono, Herlina Napitupulu, Norizan Mohamed, Yulison Herry Chrisnanto, Asep ID Hadiana, Valentina Adimurti Kusumaningtyas

“…This research aims to build a cryptocurrency forecasting model with a machine learning-based time series approach using the gated recurrent units (GRU) algorithm. The dataset used is historical Bitcoin closing price data from January 1, 2017, to July 31, 2024. …”
Get full text

Assessing patient-level risk factors for evidence-based early diagnosis of maternal sepsis by Philip Emeka Anyanwu, Paul Expert, Kate Honeyford, Oluwasomidoyin Bello, Mobolaji Modinat Salawu, Ikeola Adeoye, Ayo Stephen Adebowale, Amen-Patrick Nwosu, Summia Zaher, Peter Ghazal, Adeniyi Francis Fagbamigbe, Magbagbeola David Dairo, Ceire Costelloe

“…Patients with sepsis had the lowest rate of live birth (29.7%) compared to those with (41.8%) and without (82.1%) an infection. …”
Get full text

Exploring the relationship between vitamin B12, methylmalonic acid levels and all-cause mortality in heart failure populations: insights from the NHANES database by Wei Yu, Yao Wang, Yao Wang, Yao Wang, Yulu Zhou, Danyu Wu, Danyu Wu, Danyu Wu

“…Elevated serum MMA levels were significantly associated with an increased risk of all-cause mortality (Tertile 3 compared with Tertile 1: adjusted HR: 1.52; 95% CI: 1.09, 2.13; p = 0.01), demonstrating a dose–response pattern (26% increased mortality risk per unit increase in lnMMA). …”
Get full text