Topic Review
β-Adrenergic Stimulation
β-adrenergic receptor stimulation (β-ARS) is a physiological mechanism that regulates cardiovascular function under stress conditions or physical exercise, producing a positive inotropic (enhanced contraction), lusitropic (faster relaxation), and chronotropic (increased heart rate) effect. 
  • 195
  • 04 Aug 2021
Topic Review
Vancomycin with Muramyl Pentapeptide
Vancomycin and a native muramyl pentapeptide ended with D-alanine (MPP-D-Ala), and vancomycin and a modified muramyl pentapeptide ended with D-serine (MPP-D-Ser) form complexes in a very specific way. This complexes provide a basis for characterizing the type and stability of the connection. The type of experimentally measured and computer-simulated interactions opens the field for discussion on possible modifications to the structure of vancomycin or muramyl pentapeptide to obtain their desired characteristics.
  • 150
  • 07 Feb 2022
Topic Review
Swarm Robotics
Swarm robotics is a dynamic research field that integrates two important concepts: Swarm Intelligence (SI) and Multi-Robotics System (MRS).
  • 180
  • 02 Jun 2022
Topic Review
Smoothed-Particle Hydrodynamics
Smoothed-particle hydrodynamics is a computational mesh-free Lagrangian method developed by Gingold, Monaghan, and Lucy in 1977, initially intended for use in astrophysics.
  • 290
  • 08 Apr 2021
Topic Review
Predicting the Evolution of Syntenies
Syntenies are genomic segments of consecutive genes identified by a certain conservation in gene content and order. The notion of conservation may vary from one definition to another, the more constrained requiring identical gene contents and gene orders, while more relaxed definitions just require a certain similarity in gene content, and not necessarily in the same order. Regardless of the way they are identified, the goal is to characterize homologous genomic regions, i.e., regions deriving from a common ancestral region, reflecting a certain gene co-evolution that can enlighten important functional properties.
  • 157
  • 02 Jun 2021
Topic Review
NNetEn Entropy
NNetEn is the first entropy measure that is based on artificial intelligence methods. The method modifies the structure of the LogNNet classification model so that the classification accuracy of the MNIST-10 digits dataset indicates the degree of complexity of a given time series. The calculation results of the proposed model are similar to those of existing methods, while the model structure is completely different and provides considerable advantages.
  • 134
  • 19 Apr 2022
Topic Review
ML-Based Detection Approaches of Coal Workers’ Pneumoconiosis
Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate.  The feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR) can be summarised into three categories: classical methods including computer and international labor organization (ILO) classification-based detection; traditional machine learning methods; and CNN methods.
  • 62
  • 10 Jun 2022
Topic Review
Machine Learning-Based for Depressive Syndrome
The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression.
  • 109
  • 24 Sep 2021
Topic Review
Machine Learning for Process Monitoring
In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry.
  • 107
  • 17 Sep 2021
Topic Review
LncRNA-Protein Interactions
LncRNA can act as gene regulators, and like other epigenetic mechanisms are involved in numerous biological processes. They achieve their regulatory function with their ability to interact with a wide range of biological molecules, such as other nucleic acids and proteins. These lncRNA-protein interactions (LPI) are involved in many biological pathways including development and disease. A variety of computational LPI predictors exist, each applying different strategies to achieve their goals, and are dependent on a few biological databases containing subsets of experimentally validated LPI. Most modern lncRNA-protein interaction (LPI) prediction algorithms use machine learning approaches, where algorithms are trained on large datasets with attributes of interest.
  • 290
  • 05 Jul 2021
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