Differential Co-Abundance Network Analyses for Microbiome Data Adjusted for Clinical Covariates Using Jackknife Pseudo-Values

A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.Comment: 23 pages, 2 figures, 4 table

A Pseudo-Value Regression Approach for Differential Network Analysis of Co-Expression Data

The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a Pseudo-value Regression Approach for Network Analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information (MI) criteria, followed by pseudo-value calculations, which are then entered into a robust regression model. This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus (GEO) database to identify DC genes that are associated with chronic obstructive pulmonary disease (COPD) to demonstrate its utility. To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.Comment: 5 figures, 6 tables, Presented at the ISMB 2022 (NetBio COSI

Maximum-Area Rectangles in a Simple Polygon

We study the problem of finding maximum-area rectangles contained in a polygon in the plane. There has been a fair amount of work for this problem when the rectangles have to be axis-aligned or when the polygon is convex. We consider this problem in a simple polygon with n vertices, possibly with holes, and with no restriction on the orientation of the rectangles. We present an algorithm that computes a maximum-area rectangle in O(n^3 log n) time using O(kn^2) space, where k is the number of reflex vertices of P. Our algorithm can report all maximum-area rectangles in the same time using O(n^3) space. We also present a simple algorithm that finds a maximum-area rectangle contained in a convex polygon with n vertices in O(n^3) time using O(n) space

Identification of shared biological features in four different lung cell lines infected with SARS-CoV-2 virus through RNA-seq analysis

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and deaths worldwide. Understanding the biological mechanisms of SARS-CoV-2 infection is crucial for the development of effective therapies. This study conducts differential expression (DE) analysis, pathway analysis, and differential network (DN) analysis on RNA-seq data of four lung cell lines, NHBE, A549, A549.ACE2, and Calu3, to identify their common and unique biological features in response to SARS-CoV-2 infection. DE analysis shows that cell line A549.ACE2 has the highest number of DE genes, while cell line NHBE has the lowest. Among the DE genes identified for the four cell lines, 12 genes are overlapped, associated with various health conditions. The most significant signaling pathways varied among the four cell lines. Only one pathway, “cytokine-cytokine receptor interaction”, is found to be significant among all four cell lines and is related to inflammation and immune response. The DN analysis reveals considerable variation in the differential connectivity of the most significant pathway shared among the four lung cell lines. These findings help to elucidate the mechanisms of SARS-CoV-2 infection and potential therapeutic targets

Construction Workers' Absence Behavior Under Social Influence.

Due to the labor intensive nature of construction, workers’ timely attendance and operation at the site is crucial to the success of a construction project. Recently, researchers have found that worker absenteeism is subject to social influences. However, it is not clear how strongly the social control in workgroups affects worker absence behavior in construction, and nor is it known how social controls regarding absence are exerted over workers. With this background in mind, the overarching goal of this research is threefold: (1) to enhance our understanding of the dynamic processes of the emergence and exertion of social controls for worker absence behavior in construction, (2) to extend our understanding of the group-level absence phenomenon in construction, and (3) to identify effective policies and interventions to reduce absenteeism by creating favorable social norms in construction projects. To achieve these goals, five interrelated, interdisciplinary studies using survey analysis, the agent-based modeling and simulation of human behavior, and a behavioral economic experiment were conducted. These studies revealed that (1) team cohesion affects workers’ behavior in construction; (2) construction workers who perceive salient social norms in their team are less likely to be absent from a job site; (3) workers are under the influence of social norms more likely by self-categorization than by interpersonal exertion of social controls; (4) attachment and commitment to the current project are important variables for workers’ self-regulation, and therefore play a significant role in creating favorable social norms over time in workgroups; (5) workgroup’s mean level of social adaptation and mean level of formal rule adaptation can explain variance in the group-level absence rate; (6) there is a general pattern of alignment, but also a measurable difference between workers’ social norms and managers’ desired norms; and (7) workers who have emotional and/or evaluative identification with their project tend to have personal standards regarding absence that are similar to what their managers desire. These findings enhance our knowledge about the social mechanism for worker absence behavior in construction, and provide insights into how to prevent/reduce excessive absenteeism in construction projects by creating desirable social norms regarding absence.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110398/1/esjayahn_1.pd

Largest Similar Copies of Convex Polygons in Polygonal Domains

Given a convex polygon with k vertices and a polygonal domain consisting of polygonal obstacles with n vertices in total in the plane, we study the optimization problem of finding a largest similar copy of the polygon that can be placed in the polygonal domain without intersecting the obstacles. We present an upper bound O(k1

Finding causal paths between safety management system factors and accident precursors

Understanding the causal relationships between safety management system (SMS) factors and accident precursors helps construction organizations identify which factors require improvement upon observing an accident precursor. Previous research has not clearly distinguished between SMS factors and accident precursors. This background examines the relationships between SMS factors and accident precursors using empirical data. Specifically, five structural equation models (SEMs) were developed to map causal paths between SMS factors and accident precursors. Each of the SEMs helps identify what specific SMS factors would have a significant influence on the occurrence of a particular type of accident precursor. These models can thus help describe what specific SMS factors would need to be improved when a certain type of accident precursor appears on site. The SEM results show in particular that the occurrence of accident precursors can be attributed largely to adverse project conditions such as project schedule pressure, reworks, and change orders. Construction organizations may capitalize on these findings by prioritizing safety management resources to address specific observed accident precursors in a more informed and targeted manner

Origin of multi-level switching and telegraphic noise in organic nanocomposite memory devices.

The origin of negative differential resistance (NDR) and its derivative intermediate resistive states (IRSs) of nanocomposite memory systems have not been clearly analyzed for the past decade. To address this issue, we investigate the current fluctuations of organic nanocomposite memory devices with NDR and the IRSs under various temperature conditions. The 1/f noise scaling behaviors at various temperature conditions in the IRSs and telegraphic noise in NDR indicate the localized current pathways in the organic nanocomposite layers for each IRS. The clearly observed telegraphic noise with a long characteristic time in NDR at low temperature indicates that the localized current pathways for the IRSs are attributed to trapping/de-trapping at the deep trap levels in NDR. This study will be useful for the development and tuning of multi-bit storable organic nanocomposite memory device systems

Cu2Se-based thermoelectric cellular architectures for efficient and durable power generation

Thermoelectric power generation offers a promising way to recover waste heat. The geometrical design of thermoelectric legs in modules is important to ensure sustainable power generation but cannot be easily achieved by traditional fabrication processes. Herein, we propose the design of cellular thermoelectric architectures for efficient and durable power generation, realized by the extrusion-based 3D printing process of Cu2Se thermoelectric materials. We design the optimum aspect ratio of a cuboid thermoelectric leg to maximize the power output and extend this design to the mechanically stiff cellular architectures of hollow hexagonal column- and honeycomb-based thermoelectric legs. Moreover, we develop organic binder-free Cu2Se-based 3D-printing inks with desirable viscoelasticity, tailored with an additive of inorganic Se-8(2-) polyanion, fabricating the designed topologies. The computational simulation and experimental measurement demonstrate the superior power output and mechanical stiffness of the proposed cellular thermoelectric architectures to other designs, unveiling the importance of topological designs of thermoelectric legs toward higher power and longer durability