scholarly journals Computational analyses of mechanism of action (MoA): data, methods and integration

2022 ◽  
Author(s):  
Maria-Anna Trapotsi ◽  
Layla Hosseini-Gerami ◽  
Andreas Bender
Keyword(s):  
Case Studies ◽  
Mechanism Of Action ◽  
Data Types ◽  
Computational Analyses

This review summarises different data, data resources and methods for computational mechanism of action (MoA) analysis, and highlights some case studies where integration of data types and methods enabled MoA elucidation on the systems-level.

scholarly journals Studying Contact without Detailed Studies of the Languages Involved: A Non-Philological Approach to Language Contact

2012 ◽  
Vol 38 ◽  
pp. 92
Author(s):  
Mark Donohue
Keyword(s):  
Case Studies ◽  
Language Contact ◽  
Social Contact ◽  
Data Types ◽  
Database Analysis ◽  
Linguistic Data ◽  
Language Data ◽  
The Right ◽  
Detailed Work

<p>Studies of contact have revealed that all kinds of language material can, in the right circumstances, be borrowed from one language to another. Detecting, describing, and analyzing such situations typically involve the detailed study of at least two languages. An alternative involves detecting contact situations through database analysis. This cannot supplant the detailed work that requires detailed descriptive work in particular fields, but can allow us to examine large enough samples of languages that we can start to better understand, through calibration against known histories and other non-linguistic data types, likelihoods of different ‘social contact’ scenarios resulting in different kinds of linguistic traces, and also allow for the more targeted investigation of specific areas and language-to-language interactions. I shall describe the method, and illustrate its application in a number of case studies in regions for which we have good samples of language data.</p>

3D data visualization: The advantages of volume graphics and big data to support geologic interpretation

2015 ◽  
Vol 3 (3) ◽  
pp. SX29-SX39 ◽  
Author(s):  
Carl Byers ◽  
Andrew Woo
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Visualization ◽  
3D Visualization ◽  
Data Types ◽  
Volume Graphics ◽  
3D Data ◽  
Geologic Interpretation ◽  
Diverse Data

The ability to integrate diverse data types from multiple live and simulated sources, manipulate them dynamically, and deploy them in integrated, visual formats and in mobile settings provides significant advantages. We have reviewed some of the benefits of volume graphics and the use of big data in the context of 3D visualization case studies, in which inherent features, such as representation efficiencies, dynamic modifications, cross sectioning, and others, could improve interpretation processes and workflows.

scholarly journals Mechanism of Action and Biology of Flow Diverters in the Treatment of Intracranial Aneurysms

Neurosurgery ◽  
2019 ◽  
Vol 86 (Supplement_1) ◽  
pp. S13-S19 ◽  
Author(s):  
Krishnan Ravindran ◽  
Amanda M Casabella ◽  
Juan Cebral ◽  
Waleed Brinjikji ◽  
David F Kallmes ◽  
...  
Keyword(s):  
Mechanism Of Action ◽  
Flow Diverter ◽  
Parent Artery ◽  
First Line ◽  
Aneurysm Neck ◽  
First Line Therapy ◽  
Preclinical Animal Models ◽  
Computational Analyses ◽  
Flow Diverters ◽  
Present Understanding

Abstract Flow diverters have drastically changed the landscape of intracranial aneurysm treatment and are now considered first-line therapy for select lesions. Their mechanism of action relies on intrinsic alteration in hemodynamic parameters, both at the parent artery and within the aneurysm sac. Moreover, the device struts act as a nidus for endothelial cell growth across the aneurysm neck ultimately leading to aneurysm exclusion from the circulation. In silico computational analyses and investigations in preclinical animal models have provided valuable insights into the underlying biological basis for flow diverter therapy. Here, we review the present understanding pertaining to flow diverter biology and mechanisms of action, focusing on stent design, induction of intra-aneurysmal thrombosis, endothelialization, and alterations in hemodynamics.

scholarly journals Self-Potential Studies in Volcanic Environments: A Cheap and Efficient Method for Multiscale Fluid-Flow Investigations

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
N. Grobbe ◽  
S. Barde-Cabusson
Keyword(s):  
Electrical Resistivity ◽  
Fluid Flow ◽  
Case Studies ◽  
Potential Method ◽  
Hydrothermal Systems ◽  
Geochemical Data ◽  
Data Types ◽  
Spatial And Temporal Scales ◽  
Cooling Behavior ◽  
Self Potential

We demonstrate the value of using the self-potential method to study volcanic environments, and particularly fluid flow in those environments. We showcase the fact that self-potential measurements are a highly efficient way to map large areas of volcanic systems under challenging terrain conditions, where other geophysical techniques may be challenging or expensive to deploy. Using case studies of a variety of volcano types, including tuff cones, shield volcanoes, stratovolcanoes, and monogenetic fields, we emphasize the fact that self-potential signals enable us to study fluid flow in volcanic settings on multiple spatial and temporal scales. We categorize the examples into the following three multiscale fluid-flow processes: (1) deep hydrothermal systems, (2) shallow hydrothermal systems, and (3) groundwater. These examples highlight the different hydrological, hydrothermal, and structural inferences that can be made from self-potential signals, such as insight into shallow and deep hydrothermal systems, cooling behavior of lava flows, different hydrogeological domains, upwelling, infiltration, and lateral groundwater and hydrothermal fluid flow paths and velocities, elevation of the groundwater level, crater limits, regional faults, rift zones, incipient collapse limits, structural domains, and buried calderas. The case studies presented in this paper clearly demonstrate that the measured SP signals are a result of the coplay between microscale processes (e.g., electrokinetic, thermoelectric) and macroscale structural and environmental features. We discuss potential challenges and their causes when trying to uniquely interpret self-potential signals. Through integration with different geophysical and geochemical data types such as subsurface electrical resistivity distributions obtained from, e.g., electrical resistivity tomography or magnetotellurics, soil CO2 flux, and soil temperature, it is demonstrated that the hydrogeological interpretations obtained from SP measurements can be better constrained and/or validated.

Testing Assessments of Integrated Experiential Learning

2021 ◽  
pp. 288-309
Author(s):  
William F. Heinrich ◽  
Patrice M. Ludwig ◽  
Seán R. McCarthy ◽  
Erica J. Lewis ◽  
Nick Swayne ◽  
...  
Keyword(s):  
Experiential Learning ◽  
Case Studies ◽  
Design Thinking ◽  
Assessment Methods ◽  
Academic Affairs ◽  
Institutional Research ◽  
Alumni Relations ◽  
Data Types ◽  
Institutional Stakeholders ◽  
Interdisciplinary Setting

Design thinking is a powerful platform that provides the structure and process to measure integrated experiential learning (IEL). IEL situates the activities of experiential learning in an interdisciplinary setting that facilitates learning through reflection on experiences that engage deep knowledge in broad applications and span co-curricular and curricular environments. Using courses developed at two institutions as case studies, the authors describe pedagogy, instruction, and assessment methods, and focus the data types, collection, analysis, and implications of three assessment approaches (reflections, networks, and deliverables). They show how design thinking is essential to the assessment of IEL in courses and across institutional stakeholders, including student and academic affairs, alumni relations, employers and local businesses, and those focused on data for improvement in design (e.g., institutional research and legislators). Moreover, they show that the assessment phase of design thinking is essential to sustainability, scalability, and rigor of design thinking IEL projects.

scholarly journals Human Identity as Freedom Statement in the Philosophy of Child Education

wisdom ◽  
2021 ◽  
Vol 20 (4) ◽  
pp. 113-125
Author(s):  
Nadiia ADAMENKO ◽  
Liudmyla OBLOVA ◽  
Olena ALEKSANDROVA ◽  
Lana KHRYPKO ◽  
Oksana MAKSYMETS ◽  
...  
Keyword(s):  
Free Will ◽  
Case Studies ◽  
Mechanism Of Action ◽  
Methodological Error ◽  
Empirical Methods ◽  
Human Identity ◽  
Cross Sectional ◽  
Child Education ◽  
Cross Sectional Studies ◽  
New System

The purpose of this article is to reveal the specific features of personality-oriented education and to consider how a person, being in dialogue with another person, can declare freedom only by an act of own free will. To achieve the goal set, the authors have used a set of theoretical and empirical methods of analysis, description, comparison, extrapolation, synthesis, hermeneutic methodology, and a method of implication. Cross-sectional studies and case studies have also been used at the intersection of philosophy and psychology. It is emphasized that in the Ukrainian framework of the representation, this problem demonstrates the following – the “old” system of education, formed on the principle of necessity, has demonstrated its inef- fective mechanism of action through a system of prohibitions and oppression. The “new” system of educa- tion, built on the principle of freedom, relies on its effectiveness, rejecting necessity and eliminating com- pulsion. However, the methodological error of creating something new by destroying the old and ineffi- cient is becoming more and more evident.

scholarly journals Remdesivir for COVID-19: A review of pharmacology, mechanism of action, in-vitro activity and clinical use based on available case studies

2020 ◽  
Vol 10 (4-s) ◽  
pp. 264-270
Author(s):  
Kirtikumar Chandulal Badgujar ◽  
Avadheshkumar H. Ram ◽  
Rahoul Zanznay ◽  
Hemant Kadam ◽  
Vivek C. Badgujar
Keyword(s):  
Adverse Events ◽  
Case Studies ◽  
Mechanism Of Action ◽  
Clinical Results ◽  
Clinical Use ◽  
Literature Study ◽  
Ongoing Research ◽  
In Vitro Inhibition

Remdesivir as a drug attracted a very serious consideration of whole Globe in treatment of the pandemic disease COVID-19. More recently published in-vitro inhibition activity and in-vivo case studies were showing promising clinical results and outcome of effective inhibition of SARS-CoV-2 virus by the use of remdesivir. However at the same time, use of the remdesivir showed substantial detrimental adverse events in patients which needs a special attention during treatment course of COVID-19. Thus, the use of remdesivir in treatment of COVID-19 is having current international interest although some more clinical evidences are still necessary in order to understand the actual efficiency and mechanism of remdesivir against COVID-19. In view of this, the present literature study spotlight the current ongoing research related to use of remdesivir which includes (i) pharmacology of remdesivir, (ii) mechanism of action of remdesivir (iii) in-vitro inhibition of remdesivir against SARS-CoV-2 virus, (iv) in-vivo analysis and clinical use of remdesivir against COVID-19. Finally possible adverse events (of use of remdesivir) are also discussed considering the pharmacovigilance concern. Keywords: Remdesivir; COVID-19; Remdesivir side effects, Remdesivir pharmacology; SARS-CoV-2 virus

scholarly journals Deep Induction: Induction Rules for (Truly) Nested Types

2020 ◽  
pp. 339-358
Author(s):  
Patricia Johann ◽  
Andrew Polonsky
Keyword(s):  
Fixed Points ◽  
Programming Languages ◽  
Case Studies ◽  
Level Structure ◽  
Structured Data ◽  
Proof Assistants ◽  
Data Types ◽  
Structural Induction ◽  
Locally Presentable Categories ◽  
Standing Problem

AbstractThis paper introduces deep induction, and shows that it is the notion of induction most appropriate to nested types and other data types defined over, or mutually recursively with, (other) such types. Standard induction rules induct over only the top-level structure of data, leaving any data internal to the top-level structure untouched. By contrast, deep induction rules induct over all of the structured data present. We give a grammar generating a robust class of nested types (and thus ADTs), and develop a fundamental theory of deep induction for them using their recently defined semantics as fixed points of accessible functors on locally presentable categories. We then use our theory to derive deep induction rules for some common ADTs and nested types, and show how these rules specialize to give the standard structural induction rules for these types. We also show how deep induction specializes to solve the long-standing problem of deriving principled and practically useful structural induction rules for bushes and other truly nested types. Overall, deep induction opens the way to making induction principles appropriate to richly structured data types available in programming languages and proof assistants. Agda implementations of our development and examples, including two extended case studies, are available.

scholarly journals Multi-Omics Data Fusion for Cancer Molecular Subtyping Using Sparse Canonical Correlation Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Lin Qi ◽  
Wei Wang ◽  
Tan Wu ◽  
Lina Zhu ◽  
Lingli He ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Correlation Analysis ◽  
Data Fusion ◽  
Case Studies ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Omics Data ◽  
Data Types ◽  
Molecular Subtyping ◽  
Sparse Canonical Correlation Analysis

It is now clear that major malignancies are heterogeneous diseases associated with diverse molecular properties and clinical outcomes, posing a great challenge for more individualized therapy. In the last decade, cancer molecular subtyping studies were mostly based on transcriptomic profiles, ignoring heterogeneity at other (epi-)genetic levels of gene regulation. Integrating multiple types of (epi)genomic data generates a more comprehensive landscape of biological processes, providing an opportunity to better dissect cancer heterogeneity. Here, we propose sparse canonical correlation analysis for cancer classification (SCCA-CC), which projects each type of single-omics data onto a unified space for data fusion, followed by clustering and classification analysis. Without loss of generality, as case studies, we integrated two types of omics data, mRNA and miRNA profiles, for molecular classification of ovarian cancer (n = 462), and breast cancer (n = 451). The two types of omics data were projected onto a unified space using SCCA, followed by data fusion to identify cancer subtypes. The subtypes we identified recapitulated subtypes previously recognized by other groups (all P- values &lt; 0.001), but display more significant clinical associations. Especially in ovarian cancer, the four subtypes we identified were significantly associated with overall survival, while the taxonomy previously established by TCGA did not (P- values: 0.039 vs. 0.12). The multi-omics classifiers we established can not only classify individual types of data but also demonstrated higher accuracies on the fused data. Compared with iCluster, SCCA-CC demonstrated its superiority by identifying subtypes of higher coherence, clinical relevance, and time efficiency. In conclusion, we developed an integrated bioinformatic framework SCCA-CC for cancer molecular subtyping. Using two case studies in breast and ovarian cancer, we demonstrated its effectiveness in identifying biologically meaningful and clinically relevant subtypes. SCCA-CC presented a unique advantage in its ability to classify both single-omics data and multi-omics data, which significantly extends the applicability to various data types, and making more efficient use of published omics resources.

scholarly journals PENGGUNAAN PERANGKAT MOBILE TERHADAP SUATU TINDAK KEJAHATAN (STUDI KASUS PADA TEMUAN BUKTI DIGITAL SHORT MESSAGE SERVICE (SMS) DI UNALLOCATED DATA)

2019 ◽  
Vol 2 (1) ◽  
pp. 30-37
Author(s):  
Tommy Nugraha Manoppo
Keyword(s):  
Mobile Phone ◽  
Case Studies ◽  
Mobile Devices ◽  
Short Message Service ◽  
Daily Life ◽  
Electronic Devices ◽  
Digital Evidence ◽  
Short Message ◽  
Data Types ◽  
Message Service

Mobile devices usage has become a part of daily life modern humans today. And one of mobile devices that is almost owned by mobile devices users is mobile phone, including a smartphones. As an electronic devices that can store data, even various kinds of data types, that information stored from data on mobile phone can reflect the user’s activity. So the information can be described as a chronologically if needed. In this study, there are case studies about findings and handlings of digital evidence form of Short Message Services (SMS) that obtained on unallocated data in an android smartphone. By practically testing the procedures, the results show that steps and stages used for handling mobile devices evidence could be run dynamically, in the sense that, there are several procedural steps can be run as simultaneously, but the activity stages could be run regularly too. So the artefacts  has a forensically sound elements and can be proven as a scientific with the clear stages.

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