Artificial intelligence (AI) is a new buzz word for various public health solutions, AI is expected to transform India’s dream of affordable universal healthcare into reality. India has been rapidly ...transformed the power of AI in diagnostic services, addressing shortage of human resource, and hospital management services. However, there is a huge potential to transform the power of machine learning for examining the huge data and records collected under mother and child tracking systems for health and nutrition at risk assessment.
3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ...ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond.
This article is categorized under:
Computer and Information Science > Chemoinformatics
Computer and Information Science > Computer Algorithms and Programming
Molecular and Statistical Mechanics > Molecular Interactions
3D pharmacophores have become an essential technique for in silico drug discovery. Recent algorithmic advances with respect to machine learning and molecular dynamics simulations as well as increased availability of computing resources allowed the evolution of classic pharmacophore modeling techniques toward powerful flexibility‐ and knowledge‐enriched techniques.
The future of pathology is digital Pallua, J.D.; Brunner, A.; Zelger, B. ...
Pathology, research and practice,
September 2020, 2020-09-00, 20200901, Letnik:
216, Številka:
9
Journal Article
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Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of ...cases’ referral information, as needed for example, by the pathologist, and this support will be increased in the future even more.
In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range.
Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.
This article explores the rapidly developing field of Critical AI Studies and its relation to issues of class and capitalism through a hybrid approach based on distant reading of a newly collected ...corpus of 300 full-text scientific articles, the creation of which is itself a first attempt at properly delineating the field. We find that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up their own distinct subfield of Critical AI Studies, in contrast to, e.g., issues of race and gender, which are more broadly present in the corpus.
Artificial Intelligence has advanced at an unprecedented pace, backing recent breakthroughs in natural language processing, speech recognition, and computer vision: domains where the data is ...euclidean in nature. More recently, considerable progress has been made in engineering deep-learning architectures that can accept non-Euclidean data such as graphs and manifolds: geometric deep learning. This progress is of considerable interest to the drug discovery community, as molecules can naturally be represented as graphs, where atoms are nodes and bonds are edges. In this work, we explore the performance of geometric deep-learning methods in the context of drug discovery, comparing machine learned features against the domain expert engineered features that are mainstream in the pharmaceutical industry.
Most existing industrial control systems (ICSs), such as building energy management systems (EMSs), were installed when potential security threats were only physical. With advances in connectivity, ...ICSs are now, typically, connected to communications networks and, as a result, can be accessed remotely. This extends the attack surface to include the potential for sophisticated cyber attacks, which can adversely impact ICS operation, resulting in service interruption, equipment damage, safety concerns, and associated financial implications. In this work, a novel cyber-physical security framework for ICSs is proposed, which incorporates an analytics tool for attack detection and executes a reliable estimation-based attack-resilient control policy, whenever an attack is detected. The proposed framework is adaptable to already implemented ICS and the stability and optimal performance of the controlled system under attack has been proved. The performance of the proposed framework is evaluated using a reduced order model of a real EMS site and simulated attacks.
The annual microplastic (MP) release into soils is 4-23 times higher than that into oceans, significantly impacting soil quality. However, the mechanisms underlying how MPs impact soil properties ...remain largely unknown. Soil-MP interactions are complex because of soil heterogeneity and varying MP properties. This lack of understanding was exacerbated by the diverse experimental conditions and soil types used in this study. Predicting changes in soil properties in the presence of MPs is challenging, laborious, and time-consuming. To address these issues, machine learning was applied to fit datasets from peer-reviewed publications to predict and interpret how MPs influence soil properties, including pH, dissolved organic carbon (DOC), total P, NO
-N, NH
-N, and acid phosphatase enzyme activity (acid P). Among the developed models, the gradient boost regression (GBR) model showed the highest R
(0.86-0.99) compared to the decision tree and random forest models. The GBR model interpretation showed that MP properties contributed more than 50% to altering the acid P and NO
-N concentrations in soils, whereas they had a negligible impact on total P and 10-20% impact on soil pH, DOC, and NH
-N. Specifically, the size of MPs was the dominant factor influencing acid P (89.3%), pH (71.6%), and DOC (44.5%) in soils. NO
-N was mainly affected by the MP type (52.0%). The NH
-N was mainly affected by the MP dose (46.8%). The quantitative insights into the impact of MPs on soil properties of this study could aid in understanding the roles of MPs in soil systems.
German companies are currently facing challenges like the Demographic Change. Due to the retirement of many long-standing employees, companies risk losing tacit experimental know-how. This specific ...type of knowledge is difficult to capture and transfer to unexperienced workers. The objective of the research project “KI_eeper - Know how to keep” is to solve this challenge by using Artificial Intelligence (AI) for the development of a self-learning assistance system. Knowledge transfer as well as AI can cause fears among workforces, which hampers user acceptance of new AI-based tools. Therefore, the employees got proactively involved from an early development stage on through different measures, like e. g. workshops and process analyses. In particular the ELSI+UX workshop openly embraces potential challenges of AI implementation and the fears of employees. Furthermore, the employees get the chance to provide direct feedback about the technical solution of how user-friendly it is for their work process. This contribution focuses on the conception and realization of an ELSI+UX workshop. In the workshop the ethical, legal and social implications (ELSI) as well as user experience (UX) of the technical conception were assessed by the employees. This approach promoted substantial progress of the project and nurtured user acceptance of the AI system.
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