data exploration
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2022 ◽  
Author(s):  
Jakob McBroome ◽  
Jennifer Martin ◽  
Adriano de Bernardi Schneider ◽  
Yatish Turakhia ◽  
Russell Corbett-Detig

The unprecedented SARS-CoV-2 global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic summary statistic which quickly and efficiently identifies newly introduced strains in a region, resulting clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and is congruent with a more sophisticated analysis performed during the pandemic. We also introduce Cluster Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from inter-regional transmission across the United States, streamlining public health tracking of local viral diversity and emerging infection clusters. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely-sampled pathogens.


2022 ◽  
Vol 6 (1) ◽  
pp. 1-10
Author(s):  
Gunawan Widjaja ◽  
Hotmaria Hertawaty Sijabat

This health paper analysis discusses what experts think about the work of the COVID-19 vaccine in the human body. This study is part of general public health literacy. To facilitate the discussion, we obtained data through a Google search engine on many well-known publications concerned with health issues, especially the coronavirus prevention vaccination program. The publication journals we mean are Medpub, Google Book, Esavier, Sagepub, Academic research, Taylor and France, and several other publications. We managed this paper in a qualitative design for secondary data exploration. Meanwhile, our research efforts are carried out. Namely, we use data coding, evaluation, and in-depth interpretation to draw conclusions that can answer the questions of this study validly and reliably. The result is that vaccine programs function by training the immune system to detect and fight viruses and bacteria. Do this; pathogenic molecules must be delivered into the body to elicit an immune response. These molecules, known as antigens, can be found in all viruses and bacteria.


2022 ◽  
pp. 146-164
Author(s):  
Duygu Bagci Das ◽  
Derya Birant

Explainable artificial intelligence (XAI) is a concept that has emerged and become popular in recent years. Even interpretation in machine learning models has been drawing attention. Human activity classification (HAC) systems still lack interpretable approaches. In this study, an approach, called eXplainable HAC (XHAC), was proposed in which the data exploration, model structure explanation, and prediction explanation of the ML classifiers for HAR were examined to improve the explainability of the HAR models' components such as sensor types and their locations. For this purpose, various internet of things (IoT) sensors were considered individually, including accelerometer, gyroscope, and magnetometer. The location of these sensors (i.e., ankle, arm, and chest) was also taken into account. The important features were explored. In addition, the effect of the window size on the classification performance was investigated. According to the obtained results, the proposed approach makes the HAC processes more explainable compared to the black-box ML techniques.


2022 ◽  
Vol 924 (1) ◽  
pp. L6
Author(s):  
Christian Möstl ◽  
Andreas J. Weiss ◽  
Martin A. Reiss ◽  
Tanja Amerstorfer ◽  
Rachel L. Bailey ◽  
...  

Abstract We report the result of the first search for multipoint in situ and imaging observations of interplanetary coronal mass ejections (ICMEs) starting with the first Solar Orbiter (SolO) data in 2020 April–2021 April. A data exploration analysis is performed including visualizations of the magnetic-field and plasma observations made by the five spacecraft SolO, BepiColombo, Parker Solar Probe (PSP), Wind, and STEREO-A, in connection with coronagraph and heliospheric imaging observations from STEREO-A/SECCHI and SOHO/LASCO. We identify ICME events that could be unambiguously followed with the STEREO-A heliospheric imagers during their interplanetary propagation to their impact at the aforementioned spacecraft and look for events where the same ICME is seen in situ by widely separated spacecraft. We highlight two events: (1) a small streamer blowout CME on 2020 June 23 observed with a triple lineup by PSP, BepiColombo and Wind, guided by imaging with STEREO-A, and (2) the first fast CME of solar cycle 25 (≈1600 km s−1) on 2020 November 29 observed in situ by PSP and STEREO-A. These results are useful for modeling the magnetic structure of ICMEs and the interplanetary evolution and global shape of their flux ropes and shocks, and for studying the propagation of solar energetic particles. The combined data from these missions are already turning out to be a treasure trove for space-weather research and are expected to become even more valuable with an increasing number of ICME events expected during the rise and maximum of solar cycle 25.


Author(s):  
Jimmy Moore ◽  
Pascal Goffin ◽  
Jason Wiese ◽  
Miriah Meyer

Whether investigating research questions or designing systems, many researchers and designers need to engage users with their personal data. However, it is difficult to successfully design user-facing tools for interacting with personal data without first understanding what users want to do with their data. Techniques for raw data exploration, sketching, or physicalization can avoid the perils of tool development, but prevent direct analytical access to users' rich personal data. We present a new method that directly tackles this challenge: the data engagement interview. This interview method incorporates an analyst to provide real-time personal data analysis, granting interview participants the opportunity to directly engage with their data, and interviewers to observe and ask questions throughout this engagement. We describe the method's development through a case study with asthmatic participants, share insights and guidance from our experience, and report a broad set of insights from these interviews.


Author(s):  
Zhe Wang ◽  
Dylan Cashman ◽  
Mingwei Li ◽  
Jixian Li ◽  
Matthew Berger ◽  
...  

Author(s):  
Paolo Bethaz ◽  
Khalid Belhajjame ◽  
Genoveva Vargas-Solar ◽  
Tania Cerquitelli
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