Scalable Parallel Delaunay Image-to-Mesh Conversion for Shared and Distributed Memory Architectures

Mesh generation is an essential component for many engineering applications. The ability to generate meshes in parallel is critical for the scalability of the entire Finite Element Method (FEM) pipeline. However, parallel mesh generation applications belong to the broader class of adaptive and irregular problems, and are among the most complex, challenging, and labor intensive to develop and maintain. In this thesis, we summarize several years of the progress that we made in a novel framework for highly scalable and guaranteed quality mesh generation for finite element analysis in three dimensions. We studied and developed parallel mesh generation algorithms on both shared and distributed memory architectures. In this thesis we present a novel two-level parallel tetrahedral mesh generation framework capable of delivering and sustaining close to 6000 of concurrent work units (cores). We achieve this by leveraging concurrency at two different granularity levels by using a hybrid message passing and multi-threaded execution model which is suitable to the hierarchy of the hardware architecture of the distributed memory clusters. An end-user productivity and scalability study was performed on up to 6000 cores, and indicated very good end-user productivity with about 300 million tets per second and about 3600 weak scaling speedup. Both of these results suggest that: compared to the best previous algorithm, we have seen an improvement of more than 7000 times in performance, measured in terms of speed (elements per second) by using about 180 times more CPUs, for geometries that are by many orders of magnitude more complex

Extreme-Scale Parallel Mesh Generation: Telescopic Approach

In this poster we focus and present our preliminary results pertinent to the integration of multiple parallel Delaunay mesh generation methods into a coherent hierarchical framework. The goal of this project is to study our telescopic approach and to develop Delaunay-based methods to explore concurrency at all hardware layers using abstractions at (a) medium-grain level for many cores within a single chip and (b) coarse-grain level, i.e., sub-domain level using proper error metric- and application-specific continuous decomposition methods

Efficient Core Utilization in a Hybrid Parallel Delaunay Meshing Algorithm on Distributed-Memory Cluster

Most of the current supercomputer architectures consist of clusters of nodes that are used by many clients (users). A user wants his/her job submitted in the job queue to be scheduled promptly. However, the resource sharing and job scheduling policies that are used in the scheduling system to manage the jobs are usually beyond the control of users. Therefore, in order to reduce the waiting time of their jobs, it is becoming more and more crucial for the users to consider how to implement the algorithms that are suitable to the system scheduling policies and are able to effectively and efficiently utilize the available resources of the supercomputers. We proposed a hybrid MPI+Threads parallel mesh generation algorithm on distributed memory clusters with efficient core utilization. The algorithm takes the system scheduling information into account and is able to utilize the nodes that have been partially occupied by the jobs of other users. The experimental results demonstrated that the algorithm is effective and efficient to utilize available cores, which reduces the waiting time of the algorithm in the system job scheduling queue. It is up to 12.74 times faster than the traditional implementation without efficient core utilization when a mesh with 2.58 billion elements is created for 400 cores

IsoTree: A New Framework for De novo Transcriptome Assembly from RNA-seq Reads

High-throughput sequencing of mRNA has made the deep and efficient probing of transcriptome more affordable. However, the vast amounts of short RNA-seq reads make de novo transcriptome assembly an algorithmic challenge. In this work, we present IsoTree, a novel framework for transcripts reconstruction in the absence of reference genomes. Unlike most of de novo assembly methods that build de Bruijn graph or splicing graph by connecting $k-mers$ which are sets of overlapping substrings generated from reads, IsoTree constructs splicing graph by connecting reads directly. For each splicing graph, IsoTree applies an iterative scheme of mixed integer linear program to build a prefix tree, called isoform tree. Each path from the root node of the isoform tree to a leaf node represents a plausible transcript candidate which will be pruned based on the information of paired-end reads. Experiments showed that in most cases IsoTree performs better than other leading transcriptome assembly programs. IsoTree is available at https://github.com/Jane110111107/IsoTree

Can a permutation be sorted by best short swaps?

A short swap switches two elements with at most one element caught between them. Sorting permutation by short swaps asks to find a shortest short swap sequence to transform a permutation into another. A short swap can eliminate at most three inversions. It is still open for whether a permutation can be sorted by short swaps each of which can eliminate three inversions. In this paper, we present a polynomial time algorithm to solve the problem, which can decide whether a permutation can be sorted by short swaps each of which can eliminate 3 inversions in O(n) time, and if so, sort the permutation by such short swaps in O(n^2) time, where n is the number of elements in the permutation. A short swap can cause the total length of two element vectors to decrease by at most 4. We further propose an algorithm to recognize a permutation which can be sorted by short swaps each of which can cause the element vector length sum to decrease by 4 in O(n) time, and if so, sort the permutation by such short swaps in O(n^2) time. This improves upon the O(n^2) algorithm proposed by Heath and Vergara to decide whether a permutation is so called lucky

The Longest Common Exemplar Subsequence Problem

In this paper, we propose to find order conserved subsequences of genomes by finding longest common exemplar subsequences of the genomes. The longest common exemplar subsequence problem is given by two genomes, asks to find a common exemplar subsequence of them, such that the exemplar subsequence length is maximized. We focus on genomes whose genes of the same gene family are in at most s spans. We propose a dynamic programming algorithm with time complexity O(s4 s mn) to find a longest common exemplar subsequence of two genomes with one genome admitting s span genes of the same gene family, where m, n stand for the gene numbers of those two given genomes. Our algorithm can be extended to find longest common exemplar subsequences of more than one genomes

Alcohol misuse, health-related behaviors, and burnout among clinical therapists in China during the early Covid-19 pandemic: A Nationwide survey

ObjectivesThis study aimed to assess the extent of alcohol use and misuse among clinical therapists working in psychiatric hospitals in China during the early COVID-19 Pandemic, and to identify associated factors.MethodsAn anonymous nationwide survey was conducted in 41 tertiary psychiatric hospitals. We collected demographic data as well as alcohol use using the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) and burnout using the Maslach Burnout Inventory Human Services Survey.ResultsIn total, 396 clinical therapists completed the survey, representing 89.0% of all potential participants we targeted. The mean age of participants was 33.8 years old, and more than three-quarters (77.5%) were female. Nearly two-fifths (39.1%) self-reported as current alcohol users. The overall prevalence of alcohol misuse was 6.6%. Nearly one-fifth (19.9%) reported symptoms of burnout with high emotional exhaustion in 46 (11.6%), and high depersonalization in 61 (15.4%). Multiple logistic regression showed alcohol use was associated with male gender (OR = 4.392; 95% CI =2.443–7.894), single marital status (OR = 1.652; 95% CI =0.970–2.814), smoking habit (OR = 3.847; 95%CI =1.160–12.758) and regular exercise (OR = 2.719; 95%CI =1.490–4.963). Alcohol misuse was associated with male gender (OR = 3.367; 95% CI =1.174–9.655), a lower education level (OR = 3.788; 95%CI =1.009–14.224), smoking habit (OR = 4.626; 95%CI =1.277–16.754) and high burnout (depersonalization, OR = 4.848; 95%CI =1.433–16.406).ConclusionDuring the COVID-19 pandemic, clinical therapists’ alcohol consumption did not increase significantly. Male gender, cigarette smoking, and burnout are associated with an increased risk of alcohol misuse among clinical therapists. Targeted intervention is needed when developing strategies to reduce alcohol misuse and improve clinical therapists’ wellness and mental health

A Mesh Generation and Machine Learning Framework for Drosophila Gene Expression Pattern Image Analysis

Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions. Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/. Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods

A Mesh Generation and Machine Learning Framework for Drosophila Gene Expression Pattern Image Analysis

Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions. Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/. Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods

Prevalence and associated factors of depression, anxiety and stress among clinical therapists in China in the context of early COVID-19 pandemic

ObjectivesTo study the socio-demographic characteristics and the prevalence of depression, anxiety, and stress among clinical therapists in China during the early Coronavirus disease 2019 (COVID-19) pandemic and to identify associated factors.MethodThis cross-sectional study was part of a multicenter, nationally representative survey conducted through WeChat from January 2021 to March 2021. Data, including socio-demographics, health-related behaviors, and information on whether they participated in the frontline work of treating COVID-19, were collected anonymously. Respondents also completed the Depression Anxiety Stress Scales-21 (DASS-21).ResultsIn total, 396 clinical therapists in the selected hospitals completed the questionnaires, with a response rate of 89.0%. Respondents were predominantly female (77.3%). About 6.6% of the participants were current tobacco users, and 20.7% had participated in the frontline work of treating COVID-19. Overall, 22.0%, 17.9%, and 8.8% of participants were classified as having clinically meaningful depression, anxiety, and stress, respectively, based on DASS-21 scores. Multiple logistic regression in Model 1 and Model 2 showed that depression, anxiety, and stress were associated with regular physical activity and frequent insomnia (all, p < 0.05). In anxiety model 2, the associated factors for anxiety during the pandemic were identified as education (master’s degree or more, OR=0.520; 95% CI=0.283-0.955), marital status (single, OR=2.064; 95% CI=1.022-4.168), tobacco use (OR=4.265; 95% CI=1.352-13.454), regular physical activity (OR=0.357; 95% CI=0.192-0.663), frequent insomnia (OR=6.298; 95% CI =2.522-15.729), and participation in the frontline work of treating COVID-19 (OR=3.179; 95% CI=1.697-5.954). The COVID-19 epidemic did not significantly increase the depression and stress levels among clinical therapists, but it did significantly increase anxiety levels.ConclusionDuring the COVID-19 pandemic, depression, anxiety and stress were relatively common among clinical therapists in China. Regular physical activity and good sleep were important protective factors against emotional problems. Therefore, encouraging regular physical activity and actively addressing clinical therapists’ sleep problems is beneficial to improving the ability to cope with negative emotions. The COVID-19 epidemic significantly increased anxiety, and awareness and interventions should be recommended to reduce anxiety among clinical therapists during the COVID-19 pandemic