Unlabelled: MultiPLX is a new program for automatic grouping of PCR primers. It can use many different parameters to estimate the compatibility of primers, such as primer-primer interactions, primer-product interactions, difference in melting temperatures, difference in product length and the risk of generating alternative products from the template. A unique feature of the MultiPLX is the ability to perform automatic grouping of large number (thousands) of primer pairs.
Availability: Binaries for Windows, Linux and Solaris are available from http://bioinfo.ebc.ee/download/. A graphical version with limited capabilities can be used through a web interface at http://bioinfo.ebc.ee/multiplx/. The source code of the program is available on request for academic users.
Contact: maido.remm@ut.ee.
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http://dx.doi.org/10.1093/bioinformatics/bti219 | DOI Listing |
Am J Orthod Dentofacial Orthop
February 2025
Department of Orthodontics, Faculty of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.
Background: Placement of right precordial leads in higher intercostal spaces (EEP-ECG) improves the detection of Brugada Syndrome (BrS). Given the potential difficulty of lead placement and the transient nature of BrS ECG patterns, we developed a model to predict EEP-ECG from a standard 12‑lead ECG.
Objective: To create and validate a model that derives EEP-ECG leads from a standard 12‑lead ECG.
Comput Biol Med
January 2025
Neurological Sciences and Cerebrovascular Research Laboratory, Department of Neurology and Stroke Centre, Neurology and Cerebrovascular Disease Group, Neuroscience Area La Paz Institute for Health Research (idiPAZ), (La Paz University Hospital- Universidad Autónoma de Madrid), Spain. Electronic address:
The quantitative evaluation of motor function in experimental stroke models is essential for the preclinical assessment of new therapeutic strategies that can be transferred to clinical research; however, conventional assessment tests are hampered by the evaluator's subjectivity. We present an artificial intelligence-based system for the automatic, accurate, and objective analysis of target parameters evaluated by the ledged beam walking test, which offers higher sensitivity than the current methodology based on manual and visual counting. This system employs a residual deep network model, trained with DeepLabCut (DLC) to extract target paretic hindlimb coordinates, which are categorized to provide a ratio measurement of the animal's neurological deficit.
View Article and Find Full Text PDFBr J Hosp Med (Lond)
January 2025
Department of Gastroenterology, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, and reduce the subjectivity of the operating physician, which may help to achieve standardization and normalization of colonoscopy. In this study, we aimed to explore the value of using an AI-driven intestinal image recognition model to evaluate intestinal preparation before colonoscopy. In this retrospective analysis, we analyzed the clinical data of 98 patients who underwent colonoscopy in Nantong First People's Hospital from May 2023 to October 2023.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, Spain.
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF).
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