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REVIEW
A M Winters, J Bakker, J Ten Hoor, H J G Bilo, P F Roodbol, M A Edens, E J Finnema
PURPOSE: It is unclear whether the Geriatric-8 (G8) has the accuracy to preselect patients for complete geriatric assessment, and has the ability to predict adverse outcomes in patients with colorectal cancer (CRC). We therefore aimed to determine whether the G8, or other variables present in the medical record, are applicable in predicting 30-day adverse outcomes in older patients undergoing surgery for CRC. METHODS: We performed a retrospective cohort study involving patients ≥70 years who had surgery for CRC between 2018 and 2020 in a general hospital in the Netherlands...
April 16, 2024: European Journal of Oncology Nursing: the Official Journal of European Oncology Nursing Society
#2
JOURNAL ARTICLE
Yina Wang, Weijiao Feng, Jie Peng, Fen Ye, Jun Song, Xiaoyan Bao, Chaosheng Li
BACKGROUND: Aspiration is a frequently observed complication in individuals diagnosed with acute ischemic stroke, leading to potentially severe consequences. However, the availability of predictive tools for assessing aspiration probabilities remains limited. Hence, our study aimed to develop and validate a nomogram for accurately predicting aspiration probability in patients with acute ischemic stroke. METHODS: We analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke...
April 22, 2024: Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia
#3
JOURNAL ARTICLE
Jiarui Zhang, Qun Yi, Chen Zhou, Yuanming Luo, Hailong Wei, Huiqing Ge, Huiguo Liu, Jianchu Zhang, Xianhua Li, Xiufang Xie, Pinhua Pan, Mengqiu Yi, Lina Cheng, Hui Zhou, Liang Liu, Adila Aili, Yu Liu, Lige Peng, Jiaqi Pu, Haixia Zhou
BACKGROUND: Noninvasive mechanical ventilation (NIV) is recommended as the initial mode of ventilation to treat acute respiratory failure in patients with AECOPD. The Noninvasive Ventilation Outcomes (NIVO) score has been proposed to evaluate the prognosis in patients with AECOPD requiring assisted NIV. However, it is not validated in Chinese patients. METHODS: We used data from the MAGNET AECOPD Registry study, which is a prospective, noninterventional, multicenter, real-world study conducted between September 2017 and July 2021 in China...
2024: Chronic Respiratory Disease
#4
JOURNAL ARTICLE
Benjamin D Pesante, Maryam Salimi, Whitney L Miller, Heather L Young, Timothy C Jenkins, Joshua A Parry
INTRODUCTION: Distinguishing between septic arthritis and crystal arthropathy flares can be challenging. The purpose of this study was to determine how the presence of synovial crystals affects the diagnostic criteria of septic arthritis. METHODS: A retrospective review identified patients undergoing joint aspirations to rule out native septic arthritis. Differences between septic arthritis presenting with and without synovial crystals were analyzed. A receiver-operating characteristic curve was plotted for laboratory markers to determine the area under the curve, or diagnostic accuracy, for septic arthritis and to evaluate thresholds that maximized sensitivity and specificity...
April 17, 2024: Journal of the American Academy of Orthopaedic Surgeons
#5
JOURNAL ARTICLE
Stefania Moro, Simon E F Spencer, Daniel W Lester, Fritz Nübling, Michael Sommer, Giovanni Costantini
Conjugated polymers have become materials of choice for applications ranging from flexible optoelectronics to neuromorphic computing, but their polydispersity and tendency to aggregate pose severe challenges to their precise characterization. Here, the combination of vacuum electrospray deposition (ESD) with scanning tunneling microscopy (STM) is used to acquire, within the same experiment, assembly patterns, full mass distributions, exact sequencing, and quantification of polymerization defects. In a first step, the ESD-STM results are successfully benchmarked against NMR for low molecular mass polymers, where this technique is still applicable...
April 23, 2024: ACS Nano
#6
JOURNAL ARTICLE
Rajeev Ranjan, Sonali Srijan, Somaiah Balekuttira, Tina Agarwal, Melissa Ramey, Madison Dobbins, Rachel Kuhn, Xiaojin Wang, Karen Hudson, Ying Li, Kranthi Varala
Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana ...
April 30, 2024: Proceedings of the National Academy of Sciences of the United States of America
#7
JOURNAL ARTICLE
Sushant Sharma Chaudhary, Andrew Toivonen, Gaurav Waratkar, Geoffrey Mo, Deep Chatterjee, Sarah Antier, Patrick Brockill, Michael W Coughlin, Reed Essick, Shaon Ghosh, Soichiro Morisaki, Pratyusava Baral, Amanda Baylor, Naresh Adhikari, Patrick Brady, Gareth Cabourn Davies, Tito Dal Canton, Marco Cavaglia, Jolien Creighton, Sunil Choudhary, Yu-Kuang Chu, Patrick Clearwater, Luke Davis, Thomas Dent, Marco Drago, Becca Ewing, Patrick Godwin, Weichangfeng Guo, Chad Hanna, Rachael Huxford, Ian Harry, Erik Katsavounidis, Manoj Kovalam, Alvin K Y Li, Ryan Magee, Ethan Marx, Duncan Meacher, Cody Messick, Xan Morice-Atkinson, Alexander Pace, Roberto De Pietri, Brandon Piotrzkowski, Soumen Roy, Surabhi Sachdev, Leo P Singer, Divya Singh, Marek Szczepanczyk, Daniel Tang, Max Trevor, Leo Tsukada, Verónica Villa-Ortega, Linqing Wen, Daniel Wysocki
Multimessenger searches for binary neutron star (BNS) and neutron star-black hole (NSBH) mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to gravitational wave (GW)s has resumed with ALIGO's, AdVirgo's and KAGRA's fourth observing run (O4). To support this effort, public semiautomated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a mock data challenge (MDC) in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables...
April 30, 2024: Proceedings of the National Academy of Sciences of the United States of America
#8
JOURNAL ARTICLE
Wei-Hsin Hsu, Kai-Wen Cheng, Tzu-Hsuan Feng, Ju-Yu Chen, Guan-Yuan Chen, Lian-Yu Chen, Te I Weng, Cheng-Chih Hsu
Drug abuse is a severe social problem worldwide. Particularly, the issue of new psychoactive substances (NPSs) have increasingly emerged. NPSs are structural or functional analogs of traditional illicit drugs, such as cocaine, cannabis, and amphetamine; these molecules provide the same or more severe neurological effects. Usually, immunoassays are utilized in the preliminary screening method. However, NPSs have poor detectability in commercially available immunoassay kits. Meanwhile, various chromatography combined with the mass spectrometry platform have been developed to quantify NPSs...
April 23, 2024: Journal of the American Society for Mass Spectrometry
#9
JOURNAL ARTICLE
Bartek Rajwa, Md Mobasshir Arshed Naved, Mohammad Adibuzzaman, Ananth Y Grama, Babar A Khan, M Murat Dundar, Jean-Christophe Rochet
As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms...
April 2024: PLOS Digit Health
#10
JOURNAL ARTICLE
Maryam Motamedi, Jessica Dawson, Na Li, Douglas G Down, Nancy M Heddle
Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, five different demand forecasting methods, ARIMA (Auto Regressive Integrated Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator), random forest, and LSTM (Long Short-Term Memory) networks are utilized and evaluated via a rolling window method...
2024: PloS One
#11
JOURNAL ARTICLE
Dong Ouyang, Yong Liang, Jinfeng Wang, Le Li, Ning Ai, Junning Feng, Shanghui Lu, Shuilin Liao, Xiaoying Liu, Shengli Xie
Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation...
April 2024: PLoS Computational Biology
#12
JOURNAL ARTICLE
C P Ranjith, Mayakannan Krishnan, Vysakh Raveendran, Lalit Chaudhari, Siddhartha Laskar
Utilising Machine Learning (ML) models to predict dosimetric parameters in pencil beam scanning proton therapy presents a promising and practical approach. The study developed Artificial Neural Network (ANN) models to predict proton beam spot size and relative positional errors using 9000 proton spot data. The irradiation log files as input variables and corresponding scintillation detector measurements as the label values. The ANN models were developed to predict six variables: spot size in the x -axis, y -axis, major axis, minor axis, and relative positional errors in the x -axis and y -axis...
April 22, 2024: Biomedical Physics & Engineering Express
#13
JOURNAL ARTICLE
Sina Aeeneh, Nikola Zlatanov, Jiangshan Yu
Majority voting is a simple mathematical function that returns the most frequently occurring value within a given set. As a popular decision fusion technique (DFT), the majority voting function (MVF) finds applications in resolving conflicts, where several independent voters report their opinions on a classification problem. Despite its importance and its various applications in ensemble learning, data crowdsourcing, remote sensing, and data oracles for blockchains, the accuracy of the MVF for the general multiclass classification problem has remained unknown...
April 23, 2024: IEEE Transactions on Neural Networks and Learning Systems
#14
JOURNAL ARTICLE
Chang Liu, Lixin Tang, Kainan Zhang, Xuanqi Xu
In industrial production processes, the mechanical properties of materials will directly determine the stability and consistency of product quality. However, detecting the current mechanical property is time-consuming and labor-intensive, and the material quality cannot be controlled in time. To achieve high-quality steel materials, developing a novel intelligent manufacturing technology that can satisfy multitask predictions for material properties has become a new research trend. This article proposes a multiobjective evolutionary learning method based on a two-stage model with topological sparse autoencoder (TSAE) and ensemble learning...
April 23, 2024: IEEE Transactions on Neural Networks and Learning Systems
#15
JOURNAL ARTICLE
Christos Kyrkou
High-efficiency deep learning (DL) models are necessary not only to facilitate their use in devices with limited resources but also to improve resources required for training. Convolutional neural networks (ConvNets) typically exert severe demands on local device resources and this conventionally limits their adoption within mobile and embedded platforms. This brief presents work toward utilizing static convolutional filters generated from the space of local binary patterns (LBPs) and Haar features to design efficient ConvNet architectures...
April 23, 2024: IEEE Transactions on Neural Networks and Learning Systems
#16
JOURNAL ARTICLE
Yue Zhao, Yantao Shen, Yuanjun Xiong, Shuo Yang, Wei Xia, Zhuowen Tu, Bernt Schiele, Stefano Soatto
Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy by forcing a new model to imitate the old models, or use ensembles, which multiply inference cost prohibitively. We analyze the role of ensembles in reducing NFR and observe that they remove negative flips that are typically not close to the decision boundary, but often exhibit large deviations in the distance among their logits...
April 23, 2024: IEEE Transactions on Pattern Analysis and Machine Intelligence
#17
JOURNAL ARTICLE
Guangshun Wei, Long Ma, Yuanfeng Zhou, Chen Wang, Jianmin Zheng, Ying He
The paper presents a new method for constructing self-supporting surfaces using arch beams that are designed to convert their thrust into supporting force, thereby eliminating shear stress and bending moments. Our method allows for the placement of the arch beams on the boundary or within a surface and partitions the surface into multiple self-supporting parts. The use of arch beams enhances stability and durability, adds aesthetic appeal, and allows for greater flexibility in the design process. We develop an iterative algorithm for designing selfsupporting surfaces with arch beams that enables the user to control the shape of the beams and surface through intuitive parameters and specify the desired location of the arch beams...
April 23, 2024: IEEE Transactions on Visualization and Computer Graphics
#18
JOURNAL ARTICLE
Xiaoyue Ji, Zhekang Dong, Liyan Zhu, Chenhao Hu, Chun Sing Lai
Human activity recognition has played a crucial role in healthcare information systems due to the fast adoption of artificial intelligence (AI) and the internet of thing (IoT). Most of the existing methods are still limited by computational energy, transmission latency, and computing speed. To address these challenges, we develop an efficient human activity recognition in-memory computing architecture for healthcare monitoring. Specifically, a mechanism-oriented model of Ag/a-Carbon/Ag memristor is designed, serving as the core circuit component of the proposed in-memory computing system...
April 23, 2024: IEEE Journal of Biomedical and Health Informatics
#19
JOURNAL ARTICLE
Xu Lu, Zengzhen Cui, Yihua Sun, Hee Guan Khor, Ao Sun, Longfei Ma, Fang Chen, Shan Gao, Yun Tian, Fang Zhou, Yang Lv, Hongen Liao
Proximal femoral fracture segmentation in computed tomography (CT) is essential in the preoperative planning of orthopedic surgeons. Recently, numerous deep learning-based approaches have been proposed for segmenting various structures within CT scans. Nevertheless, distinguishing various attributes between fracture fragments and soft tissue regions in CT scans frequently poses challenges, which have received comparatively limited research attention. Besides, the cornerstone of contemporary deep learning methodologies is the availability of annotated data, while detailed CT annotations remain scarce...
April 23, 2024: IEEE Transactions on Medical Imaging
#20
JOURNAL ARTICLE
Jonathan Parkinson, Wei Wang
MOTIVATION: Antibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity. RESULTS: In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins...
April 23, 2024: Bioinformatics
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