Analyzing combined vehicle routing and break scheduling from a distributed decision making perspective

We analyze the problem of combined vehicle routing and break scheduling from a distributed decision making perspective. The problem of combined vehicle routing and break scheduling can be defined as the problem of finding vehicle routes to serve a set of customers such that a cost criterion is minimized and legal rules on driving and working hours are observed. In the literature, this problem is always analyzed from a central planning perspective. In practice, however, this problem is solved interactively between planners and drivers. In\ud many practical scenarios, the planner first clusters the customer requests and instructs the drivers which customers they have to visit. Subsequently, the drivers decide upon the routes to be taken and their break schedules. We apply a framework for distributed decision making to model this planning scenario and propose various ways for planners to anticipate the drivers' planning behavior. Especially in the case of antagonistic objectives, which are often encountered in practice, a distributed decision making perspective is necessary to analyze this planning process. Computational experiments demonstrate that a high degree of anticipation by the planner has a strong positive impact on the overall planning quality, especially in the case of conflicting planner's and drivers' objectives

Transcriptional dynamics of microRNAs and their targets during Drosophila neurogenesis

During Drosophila melanogaster embryogenesis, tight regulation of gene expression in time and space is required for the orderly emergence of specific cell types. While the general importance of microRNAs in regulating eukaryotic gene expression has been well-established, their role in early neurogenesis remains to be addressed. In this survey, we investigate the transcriptional dynamics of microRNAs and their target transcripts during neurogenesis of Drosophila melanogaster . To this end, we use the recently developed DIV-MARIS protocol, a method for enriching specific cell types from the Drosophila embryo in vivo, to sequence cell-type-specific transcriptomes. We generate dedicated small and total RNA-seq libraries for neuroblasts, neurons and glia cells at early (6-8 h after egg laying (AEL)) and late (18-22 h AEL) stage. This allows us to directly compare these transcriptomes and investigate the potential functional roles of individual microRNAs with spatio-temporal resolution genome-wide, which is beyond the capabilities of existing in-situ hybridization studies. Overall, we identify 74 microRNAs that are significantly differentially expressed between the three cell types and the two developmental stages. In all cell types, predicted target genes of down-regulated microRNAs show a significant enrichment of their target genes related to neurogenesis. We also investigate how microRNAs regulate the transcriptome by targeting transcription factors and find many candidate microRNAs with putative roles in neurogenesis. Our survey highlights the roles of miRNAs as regulators of differentiation and glioneurognesis in the fruit fly and provides distinct starting points for dedicated functional follow-up studies

Multivariate side-band subtraction using probabilistic event weights

A common situation in experimental physics is to have a signal which can not be separated from a non-interfering background through the use of any cut. In this paper, we describe a procedure for determining, on an event-by-event basis, a quality factor ($Q$-factor) that a given event originated from the signal distribution. This procedure generalizes the "side-band" subtraction method to higher dimensions without requiring the data to be divided into bins. The $Q$-factors can then be used as event weights in subsequent analysis procedures, allowing one to more directly access the true spectrum of the signal.Comment: 17 pages, 9 figure

CoBold: a method for identifying different functional classes of transient RNA structure features that can impact RNA structure formation in vivo

RNA structure formation in vivo happens co-transcriptionally while the transcript is being made. The corresponding co-transcriptional folding pathway typically involves transient RNA structure features that are not part of the final, functional RNA structure. These transient features can play important functional roles of their own and also influence the formation of the final RNA structure in vivo. We here present CoBold, a computational method for identifying different functional classes of transient RNA structure features that can either aid or hinder the formation of a known reference RNA structure. Our method takes as input either a single RNA or a corresponding multiple-sequence alignment as well as a known reference RNA secondary structure and identifies different classes of transient RNA structure features that could aid or prevent the formation of the given RNA structure. We make CoBold available via a web-server which includes dedicated data visualisation

Synchronizing Automata on Quasi Eulerian Digraph

In 1964 \v{C}ern\'{y} conjectured that each $n$-state synchronizing automaton posesses a reset word of length at most $(n-1)^2$. From the other side the best known upper bound on the reset length (minimum length of reset words) is cubic in $n$. Thus the main problem here is to prove quadratic (in $n$) upper bounds. Since 1964, this problem has been solved for few special classes of \sa. One of this result is due to Kari \cite{Ka03} for automata with Eulerian digraphs. In this paper we introduce a new approach to prove quadratic upper bounds and explain it in terms of Markov chains and Perron-Frobenius theories. Using this approach we obtain a quadratic upper bound for a generalization of Eulerian automata.Comment: 8 pages, 1 figur

Evaluation of acute kidney injury in dogs with complicated or uncomplicated Babesia rossi infection

Dogs with babesiosis can present with multiple complications, including acute kidney injury (AKI). The objective of this study was to characterize AKI in dogs with babesiosis caused by Babesia rossi at presentation and after treatment. Thirty-five client-owned dogs with B. rossi infection and 10 control dogs were included in this prospective observational study. Blood and urine were collected in Babesia-infected dogs at presentation (T-0, n = 35), after 24 h (T-24h, n = 11), and after 1 month (T-1m, n = 9). The following urinary kidney injury biomarkers were assessed: urinary protein to creatinine ratio (UPC), urinary glomerular injury biomarkers (immunoglobulin G (uIgG) and C-reactive protein (uCRP)), and urinary tubular injury biomarkers (retinol-binding protein (uRBP) and neutrophil gelatinase-associated lipocalin (uNGAL)). Serum functional renal biomarkers were creatinine (sCr) and symmetric dimethylarginine (sSDMA). Post-mortem kidney biopsies were analyzed by light and transmission electron microscopy. At T-0, all kidney injury biomarkers were significantly higher in Babesia-infected dogs compared to healthy controls (P 0.05). At T-24h, all urinary tubular injury biomarkers and UPC decreased significantly (P 0.5). Significant changes in functional renal biomarkers were not seen after treatment (P > 0.05). Dogs with complicated babesiosis had significantly higher glomerular injury biomarkers, UPC, and sSDMA compared to uncomplicated cases (P 0.1). Dogs with babesiosis caused by B. rossi showed transient kidney injury, which was detected by all kidney injury biomarkers, but remained undetected by functional biomarkers. All infected dogs, irrespective of disease severity, suffered comparable kidney injury based on tubular injury biomarker concentrations, while loss of function was seen more often in dogs with complicated babesiosis based on sSDMA results

A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix

Ab initio study of ferroelectric domain walls in PbTiO3

We have investigated the atomistic structure of the 180-degree and 90-degree domain boundaries in the ferroelectric perovskite compound PbTiO3 using a first-principles ultrasoft-pseudopotential approach. For each case we have computed the position, thickness and creation energy of the domain walls, and an estimate of the barrier height for their motion has been obtained. We find both kinds of domain walls to be very narrow with a similar width of the order of one to two lattice constants. The energy of the 90-dergree domain wall is calculated to be 35 mJ/m^2, about a factor of four lower than the energy of its 180-degree counterpart, and only a miniscule barrier for its motion is found. As a surprising feature we detected a small offset of 0.15-0.2 eV in the electrostatic potential across the 90-degree domain wall.Comment: 12 pages, with 9 postscript figures embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/bm_dw/index.htm

A gene expression atlas of embryonic neurogenesis in Drosophila reveals complex spatiotemporal regulation of lncRNAs

Cell type specification during early nervous system development in Drosophila melanogaster requires precise regulation of gene expression in time and space. Resolving the programs driving neurogenesis has been a major challenge owing to the complexity and rapidity with which distinct cell populations arise. To resolve the cell type-specific gene expression dynamics in early nervous system development, we have sequenced the transcriptomes of purified neurogenic cell types across consecutive time points covering crucial events in neurogenesis. The resulting gene expression atlas comprises a detailed resource of global transcriptome dynamics that permits systematic analysis of how cells in the nervous system acquire distinct fates. We resolve known gene expression dynamics and uncover novel expression signatures for hundreds of genes among diverse neurogenic cell types, most of which remain unstudied. We also identified a set of conserved long noncoding RNAs (lncRNAs) that are regulated in a tissue-specific manner and exhibit spatiotemporal expression during neurogenesis with exquisite specificity. lncRNA expression is highly dynamic and demarcates specific subpopulations within neurogenic cell types. Our spatiotemporal transcriptome atlas provides a comprehensive resource for investigating the function of coding genes and noncoding RNAs during crucial stages of early neurogenesis