5,053 results match your criteria optimization class

Efavirenz Loaded Mixed Polymeric Micelles: Formulation, Optimization, and Characterization.

Assay Drug Dev Technol 2021 Jun 14. Epub 2021 Jun 14.

Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Ultimo, New South Wales, Australia.

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Automatic retrosynthetic route planning using template-free models.

Chem Sci 2020 Mar 3;11(12):3355-3364. Epub 2020 Mar 3.

BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University Beijing 100871 PR China

Retrosynthetic route planning can be considered a rule-based reasoning procedure. The possibilities for each transformation are generated based on collected reaction rules, and then potential reaction routes are recommended by various optimization algorithms. Although there has been much progress in computer-assisted retrosynthetic route planning and reaction prediction, fully data-driven automatic retrosynthetic route planning remains challenging. Read More

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Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint.

Front Neurosci 2021 28;15:679847. Epub 2021 May 28.

School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China.

Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. Read More

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Process parameter optimization for removable partial denture frameworks manufactured by selective laser melting.

J Prosthet Dent 2021 Jun 9. Epub 2021 Jun 9.

Full Professor, Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, Texas.

Statement Of Problem: Selective laser melting (SLM), an additive manufacturing technology, is expected to replace the traditional lost-wax casting process used in producing removable partial denture (RPD) frameworks. However, studies comparing the accuracy of RPD frameworks and the effects of process parameters are lacking.

Purpose: The purpose of this in vitro study was to optimize SLM process parameters and use a quantitative analysis method to improve the accuracy of 3D-printed RPD frameworks. Read More

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Discovery of a novel class of heteroaryl-pyrrolidinones as positive allosteric modulators of the muscarinic acetylcholine receptor M.

Bioorg Med Chem Lett 2021 Jun 9;47:128193. Epub 2021 Jun 9.

Warren Center for Neuroscience Drug Discovery, Nashville, TN 37232, United States; Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, United States. Electronic address:

This Letter describes the synthesis and optimization of a series of heteroaryl-pyrrolidinone positive allosteric modulators (PAMs) of the muscarinic acetylcholine receptor M (mAChR M). Through the continued optimization of M PAM tool compound VU0453595, with a focus on replacement of the 6,7-dihydro-5H-pyrrolo[3,4-b]pyridin-5-one with a wide variety of alternative 4,5-dihydropyrrolo-fused heteroaromatics, the generation of M PAMs with structurally novel chemotypes is disclosed. Two compounds from these subseries, 8b (VU6005610) and 20a (VU6005852), show robust selectivity for the M mAChR, and no M agonism. Read More

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A Generative-Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space.

Neuroimage 2021 Jun 9:118200. Epub 2021 Jun 9.

Department of Electrical and Computer Engineering, Johns Hopkins University, USA.

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. Read More

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Synthesis and Characterization of the Potential Melt-Castable Explosive 3-(1,2,4-Oxadiazolyl)-5-Nitratomethyl Isoxazole.

Chempluschem 2021 Jun;86(6):875-878

CCDC US Army Research Laboratory, Polymers Branch, Aberdeen, Proving Ground, MD 21005, USA.

The synthesis of 3-(1,2,4-oxadiazolyl)-5-nitratomethyl isoxazole (C H N O ), its physical properties, and its theoretical performances are described. This energetic material was found to have a melting point range of 76.6-79. Read More

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A first-in-class anticancer dual HDAC2/FAK inhibitors bearing hydroxamates/benzamides capped by pyridinyl-1,2,4-triazoles.

Eur J Med Chem 2021 May 29;222:113569. Epub 2021 May 29.

Department of Medicinal Chemistry, Faculty of Pharmacy, Minia University, Minia, 61519, Egypt; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Deraya University, Minia, Egypt. Electronic address:

Novel 5-pyridinyl-1,2,4-triazoles were designed as dual inhibitors of histone deacetylase 2 (HDAC2) and focal adhesion kinase (FAK). Compounds 5d, 6a, 7c, and 11c were determined as potential inhibitors of both HDAC2 (IC = 0.09-1. Read More

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Maximum Margin Multi-Dimensional Classification.

IEEE Trans Neural Netw Learn Syst 2021 Jun 9;PP. Epub 2021 Jun 9.

Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. The heterogeneity of class spaces leads to incomparability of the modeling outputs from different class spaces, which is the major difficulty in designing MDC approaches. In this article, we make a first attempt toward adapting maximum margin techniques for MDC problem and a novel approach named M³MDC is proposed. Read More

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Improved disease course associated with early initiation of biologics in untreated polyarticular Juvenile Idiopathic Arthritis: A trajectory analysis of the STOP-JIA study.

Arthritis Rheumatol 2021 Jun 8. Epub 2021 Jun 8.

Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.

Objective: To investigate the effects of early introduction of biologic disease-modifying antirheumatic drugs (bDMARDs) on disease course in untreated polyarticular JIA (pJIA).

Methods: We analyzed data of pJIA subjects participating in the Start Time Optimization of Biologics in Polyarticular JIA study (STOP-JIA; n=400) and a comparator cohort (n=248) from the Childhood Arthritis and Rheumatology Research Alliance Registry. Latent class trajectory modeling (LCTM) was applied to identify subgroups of subjects with distinct disease course based on disease activity (clinical Juvenile Arthritis Disease Activity Score) over 12 months from baseline. Read More

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GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning.

J Chem Inf Model 2021 Jun 8. Epub 2021 Jun 8.

Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland.

Machine learning classifiers trained on class imbalanced data are prone to overpredict the majority class. This leads to a larger misclassification rate for the minority class, which in many real-world applications is the class of interest. For binary data, the classification threshold is set by default to 0. Read More

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Macrocyclization of bis-indole quinolines for selective stabilization of G-quadruplex DNA structures.

Chem Sci 2020 Sep 16;11(38):10529-10537. Epub 2020 Sep 16.

Department of Chemistry, Umeå University 90187 Umeå Sweden

The recognition of G-quadruplex (G4) DNA structures as important regulatory elements in biological mechanisms, and the connection between G4s and the evolvement of different diseases, has sparked interest in developing small organic molecules targeting G4s. However, such compounds often lack drug-like properties and selectivity. Here, we describe the design and synthesis of a novel class of macrocyclic bis-indole quinolines based on their non-macrocyclic lead compounds. Read More

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September 2020

A multiple combined method for rebalancing medical data with class imbalances.

Comput Biol Med 2021 May 31;134:104527. Epub 2021 May 31.

Department of Information Management, National Yunlin University of Science & Technology, Touliou, Yunlin, 640, Taiwan. Electronic address:

Most classification algorithms assume that classes are in a balanced state. However, datasets with class imbalances are everywhere. The classes of actual medical datasets are imbalanced, severely impacting identification models and even sacrificing the classification accuracy of the minority class, even though it is the most influential and representative. Read More

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Surface-Mounted Metal-Organic Frameworks: Past, Present, and Future Perspectives.

Langmuir 2021 Jun 3;37(23):6847-6863. Epub 2021 Jun 3.

Chair of Inorganic and Metal-Organic Chemistry, Catalysis Research Center, Ernst-Otto-Fischer Straße 1 and Department of Chemistry, Technical University of Munich, Lichtenbergstraße 4, 85748 Garching b. München, Germany.

Metal-organic frameworks (MOFs) are an emerging class of porous materials composed of organic linkers and metal centers/clusters. The integration of MOFs onto the solid surface as thin films/coatings has spurred great interest, thanks to leveraging control over their morphology (such as size- and shape-regulated crystals) and orientation, flexible processability, and easy recyclability. These aspects, in synergy, promise a wide range of applications, including but not limited to gas/liquid separations, chemical sensing, and electronics. Read More

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Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration after Subthalamic Deep Brain Stimulation.

Mov Disord 2021 Jun 3. Epub 2021 Jun 3.

Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.

Background: Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline. Read More

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Ranking-Based Convolutional Neural Network Models for Peptide-MHC Class I Binding Prediction.

Front Mol Biosci 2021 17;8:634836. Epub 2021 May 17.

Computer Science and Engineering Department, The Ohio State University, Columbus, OH, United States.

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method , have been developed to predict the binding affinities between peptides and MHC molecules. Read More

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Detailed cell-level analysis of sperm nuclear quality among the different hypo-osmotic swelling test (HOST) classes.

J Assist Reprod Genet 2021 Jun 2. Epub 2021 Jun 2.

Département de Génétique Médicale, Sorbonne Université, INSERM, Maladies génétiques d'expression pédiatrique, APHP, Hôpital d'Enfants Armand Trousseau, 26 avenue du Dr Arnold Netter, F-75012, Paris, France.

Purpose: We studied the quality differences between the different hypo-osmotic swelling test (HOST) classes, as measured by criteria of DNA fragmentation, DNA decondensation, and nuclear architecture. The aim was to find particular HOST classes associated with good-quality metrics, which may be potentially used in ICSI (intra-cytoplasmic sperm injection).

Methods: Ten patients from the Department of Reproductive Medicine at Tenon Hospital (Paris, France) were included. Read More

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Preparation and Characterization of Monoclonal Antibodies with High Affinity and Broad Class Specificity against Zearalenone and Its Major Metabolites.

Toxins (Basel) 2021 05 27;13(6). Epub 2021 May 27.

College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China.

This study aimed to detect and monitor total Zearalenone (ZEN) and its five homologs (ZENs) in cereals and feed. The monoclonal antibodies (mAbs) with a high affinity and broad class specificity against ZENs were prepared, and the conditions of a heterologous indirect competitive ELISA (icELISA) were preliminarily optimized based on the ZEN mAbs. The immunogen ZEN-BSA was synthesized using the oxime active ester method (OAE) and identified using infrared (IR) and ultraviolet (UV). Read More

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Technologies for High-Throughput Identification of Antibiotic Mechanism of Action.

Antibiotics (Basel) 2021 May 12;10(5). Epub 2021 May 12.

CIMOSM-Centro de Investigação em Modelação e Optimização de Sistemas Multifuncionais, ISEL-Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal.

There are two main strategies for antibiotic discovery: target-based and phenotypic screening. The latter has been much more successful in delivering first-in-class antibiotics, despite the major bottleneck of delayed Mechanism-of-Action (MOA) identification. Although finding new antimicrobial compounds is a very challenging task, identifying their MOA has proven equally challenging. Read More

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Estimating Homogeneous Data-driven BRDF Parameters from a Reflectance Map under Known Natural Lighting.

IEEE Trans Vis Comput Graph 2021 Jun 1;PP. Epub 2021 Jun 1.

In this paper we demonstrate robust estimation of the model parameters of a fully-linear data-driven BRDF model from a reflectance map under known natural lighting. To regularize the estimation of the model parameters, we leverage the reflectance similarities within a material class. We approximate the space of homogeneous BRDFs using a Gaussian mixture model, and assign a material class to each Gaussian in the mixture model. Read More

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Efficient Low-Rank Semidefinite Programming with Robust Loss Functions.

IEEE Trans Pattern Anal Mach Intell 2021 Jun 1;PP. Epub 2021 Jun 1.

In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness of a large class of learning algorithms that are formulated as low-rank semi-definite programming (SDP) problems. Traditional formulations use the square loss, which is notorious for being sensitive to outliers. Read More

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Mix Contrast for COVID-19 Mild-to-critical Prediction.

IEEE Trans Biomed Eng 2021 Jun 1;PP. Epub 2021 Jun 1.

Objective: In a few patients with mild COVID-19, there is a possibility of the infection becoming severe or critical in the future. This work aims to identify high-risk patients who have a high probability of changing from mild to critical COVID-19 (only account for 5% of cases). Methods: Using traditional convolutional neural networks for classification may not be suitable to identify this 5% of high-risk patients from an entire dataset due to the highly imbalanced label distribution. Read More

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Mechanical evaluation of a patient-specific additively manufactured subperiosteal jaw implant (AMSJI) using finite-element analysis.

Int J Oral Maxillofac Surg 2021 May 28. Epub 2021 May 28.

Department of Mechanical Engineering, Biomechanics section, KU Leuven, Leuven, Belgium. Electronic address:

Edentulism with associated severe bone loss is a widespread condition that hinders the use of common dental implants. An additively manufactured subperiosteal jaw implant (AMSJI) was designed as an alternative solution for edentulous patients with Cawood and Howell class V-VIII bone atrophy. A biomechanical evaluation of this AMSJI for the maxilla in a Cawood and Howell class V patient was performed via finite-element analysis. Read More

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An overview of rational design of mRNA-based therapeutics and vaccines.

Expert Opin Drug Discov 2021 May 31. Epub 2021 May 31.

Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China.

Introduction: Messenger RNA (mRNA)-based therapeutics and vaccines have emerged as a disruptive new drug class for various applications, including regenerative medicine, cancer treatment, and prophylactic and therapeutic vaccinations.

Areas Covered: This review provides an update about the rational structure-based design of various formats of mRNA-based therapeutics. The authors discuss the recent advances in the mRNA modifications that have been used to enhance stability, promote translation efficiency and regulate immunogenicity for specific applications. Read More

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Functionalized Scout Fragments for Site-Specific Covalent Ligand Discovery and Optimization.

ACS Cent Sci 2021 Apr 5;7(4):613-623. Epub 2021 Apr 5.

The Department of Chemistry and The Skaggs Institute for Chemical Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92307, United States.

Covalent ligands are a versatile class of chemical probes and drugs that can target noncanonical sites on proteins and display differentiated pharmacodynamic properties. Chemical proteomic methods have been introduced that leverage electrophilic fragments to globally profile the covalent ligandability of nucleophilic residues, such as cysteine and lysine, in native biological systems. Further optimization of these initial ligandability events without resorting to the time-consuming process of individualized protein purification and functional assay development, however, presents a persistent technical challenge. Read More

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Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner.

Comput Math Methods Med 2021 17;2021:6662420. Epub 2021 May 17.

Department of Computer Technology, Anna University, Chennai 600025, India.

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. Read More

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Enzyme Replacement Therapy for Genetic Disorders Associated with Enzyme Deficiency.

Curr Med Chem 2021 May 26. Epub 2021 May 26.

Institute of Biophysics, National Research Council, Via Moruzzi 1, 56124, Pisa, Italy.

Mutations in human genes might lead to loss of functional proteins, causing diseases. Among these genetic disorders, a large class is associated with the deficiency in metabolic enzymes, resulting in both an increase in the concentration of substrates and a loss in the metabolites produced by the catalyzed reactions. The identification of therapeutic actions based on small molecules represents a challenge to medicinal chemists because the target is missing. Read More

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Optimization of Phase-Change Material-Elastomer Composite and Integration in Kirigami-Inspired Voxel-Based Actuators.

Front Robot AI 2021 10;8:672934. Epub 2021 May 10.

TIPs Dpt, Université Libre de Bruxelles, Brussels, Belgium.

Phase-change material-elastomer composite (PCMEC) actuators are composed of a soft elastomer matrix embedding a phase-change fluid, typically ethanol, in microbubbles. When increasing the temperature, the phase change in each bubble induces a macroscopic expansion of the matrix. This class of actuators is promising for soft robotic applications because of their high energy density and actuation strain, and their low cost and easy manufacturing. Read More

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Clustering of cancer data based on Stiefel manifold for multiple views.

BMC Bioinformatics 2021 May 25;22(1):268. Epub 2021 May 25.

College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.

Background: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Read More

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