Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

<p>Abstract</p> <p>Background</p> <p>Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels.</p> <p>Results</p> <p>We propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network.</p> <p>Conclusions</p> <p>The algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases.</p

The Munich Shoulder Questionnaire (MSQ): development and validation of an effective patient-reported tool for outcome measurement and patient safety in shoulder surgery

Background Outcome measurement in shoulder surgery is essential to evaluate the patient safety and treatment efficiency. Currently this is jeopardized by the fact that most patient-reported self-assessment instruments are not comparable. Hence, the aim was to develop a reliable self-assessment questionnaire which allows an easy follow-up of patients. The questionnaire also allows the calculation of 3 well established scoring systems, i.e. the Shoulder Pain and Disability Index (SPADI), the Constant-Murley Score (CMS), and the Disabilities of the Arm, Shoulder and Hand (DASH) Score. The subjective and objective items of these three systems were condensed into a single 30-questions form and validated against the original questionnaires. Methods A representative collective of patients of our shoulder clinic was asked to fill in the newly designed self-assessment Munich Shoulder Questionnaire (MSQ). At the same time, the established questionnaires for self-assessment of CONSTANT, SPADI and DASH scores were handed out. The obtained results were compared by linear regression analysis. Results Fifty one patients completed all questionnaires. The correlation coefficients of the results were r = 0.91 for the SPADI, r = -0.93 for the DASH and r = 0.94 for the CMS scoring system, respectively. Conclusions We developed an instrument which allows a quantitative self-assessment of shoulder function. It provides compatible data sets for the three most popular shoulder function scoring systems by one single, short 30-item. This instrument can be used by shoulder surgeons to effectively monitor the outcome, safety and quality of their treatment and also compare the results to published data in the literature

Pairs of SAT Assignment in Random Boolean Formulae

We investigate geometrical properties of the random K-satisfiability problem using the notion of x-satisfiability: a formula is x-satisfiable if there exist two SAT assignments differing in Nx variables. We show the existence of a sharp threshold for this property as a function of the clause density. For large enough K, we prove that there exists a region of clause density, below the satisfiability threshold, where the landscape of Hamming distances between SAT assignments experiences a gap: pairs of SAT-assignments exist at small x, and around x=1/2, but they donot exist at intermediate values of x. This result is consistent with the clustering scenario which is at the heart of the recent heuristic analysis of satisfiability using statistical physics analysis (the cavity method), and its algorithmic counterpart (the survey propagation algorithm). The method uses elementary probabilistic arguments (first and second moment methods), and might be useful in other problems of computational and physical interest where similar phenomena appear

Impact of olive saplings and organic amendments on soil microbial communities and effects of mineral fertilization

Plant communities and fertilization may have an impact on soil microbiome. Most commercial olive trees are minerally fertilized, while this practice is being replaced by the use of organic amendments. Organic amendments can both fertilize and promote plant growth-promoting organisms. Our aims were (i) to describe the changes in soil bacterial and fungal communities induced by the presence of young olive trees and their interaction with organic amendments and (ii) to compare the effects of mineral and organic fertilization. We set up two parallel experiments in pots using a previously homogenized soil collected from a commercial olive orchard: in the first one, we grew olive saplings in unamended and organically amended soils with two distinct composts and compared these two soils incubated without a plant, while in the second experiment, we comparatively tested the effects of organic and mineral fertilization. OTUs and the relative abundances of bacterial and fungal genera and phyla were analyzed by 16S rRNA and ITS1 gene amplicon using high-throughput sequencing. Basal respiration and substrate-induced respiration were measured by MicroRespTM. The effects of the different treatments were analyzed in all phyla and in the 100 most abundant genera. The presence of olive saplings increased substrate-induced respiration and bacterial and fungal richness and diversity. Organic amendments greatly affected both bacterial and fungal phyla and increased bacterial richness while not affecting fungal richness. Mineral fertilization increased the relative abundance of the less metabolically active bacterial phyla (Actinobacteria and Firmicutes), while it reduced the most metabolically active phylum, Bacteroidetes. Mineral fertilization increased the relative abundance of three N2-fixing Actinobacteria genera, while organic fertilization only increased one genus of Proteobacteria. In organically and minerally fertilized soils, high basal respiration rates were associated with low fungal diversity. Basidiomycota and Chytridiomycota relative abundances positively correlated with basal respiration and substrate-induced respiration, while Ascomycota correlated negatively. Indeed, the Ascomycota phyla comprised most of the fungal genera decreased by organic amendments. The symbiotrophic phylum Glomeromycota did not correlate with any of the C sources. The relative abundance of this phylum was promoted by the presence of plants but decreased when amending soils with composts

The theoretical capacity of the Parity Source Coder

The Parity Source Coder is a protocol for data compression which is based on a set of parity checks organized in a sparse random network. We consider here the case of memoryless unbiased binary sources. We show that the theoretical capacity saturate the Shannon limit at large K. We also find that the first corrections to the leading behavior are exponentially small, so that the behavior at finite K is very close to the optimal one.Comment: Added references, minor change

Locked constraint satisfaction problems

We introduce and study the random "locked" constraint satisfaction problems. When increasing the density of constraints, they display a broad "clustered" phase in which the space of solutions is divided into many isolated points. While the phase diagram can be found easily, these problems, in their clustered phase, are extremely hard from the algorithmic point of view: the best known algorithms all fail to find solutions. We thus propose new benchmarks of really hard optimization problems and provide insight into the origin of their typical hardness.Comment: 4 pages, 2 figure

Optimal Traffic Networks

Inspired by studies on the airports' network and the physical Internet, we propose a general model of weighted networks via an optimization principle. The topology of the optimal network turns out to be a spanning tree that minimizes a combination of topological and metric quantities. It is characterized by a strongly heterogeneous traffic, non-trivial correlations between distance and traffic and a broadly distributed centrality. A clear spatial hierarchical organization, with local hubs distributing traffic in smaller regions, emerges as a result of the optimization. Varying the parameters of the cost function, different classes of trees are recovered, including in particular the minimum spanning tree and the shortest path tree. These results suggest that a variational approach represents an alternative and possibly very meaningful path to the study of the structure of complex weighted networks.Comment: 4 pages, 4 figures, final revised versio

Inflammation and infection in plasma cell disorders: how pathogens shape the fate of patients

The role of infection and chronic inflammation in plasma cell disorders (PCD) has been well-described. Despite not being a diagnostic criterion, infection is a common complication of most PCD and represents a significant cause of morbidity and mortality in this population. As immune-based therapeutic agents are being increasingly used in multiple myeloma, it is important to recognize their impact on the epidemiology of infections and to identify preventive measures to improve outcomes. This review outlines the multiple factors attributed to the high infectious risk in PCD (e.g. the underlying disease status, patient age and comorbidities, and myeloma-directed treatment), with the aim of highlighting future prophylactic and preventive strategies that could be implemented in the clinic. Beyond this, infection and pathogens as an entity are believed to also influence disease biology from initiation to response to treatment and progression through a complex interplay involving pathogen exposure, chronic inflammation, and immune response. This review will outline both the direct and indirect role played by oncogenic pathogens in PCD, highlight the requirement for large-scale studies to decipher the precise implication of the microbiome and direct pathogens in the natural history of myeloma and its precursor disease states, and understand how, in turn, pathogens shape plasma cell biology

Antimyeloma Effects of the Heat Shock Protein 70 Molecular Chaperone Inhibitor MAL3-101

Multiple myeloma (MM) is the second most common hematologic malignancy and remains incurable, primarily due to the treatment-refractory/resistant nature of the disease. A rational approach to this compelling challenge is to develop new drugs that act synergistically with existing effective agents. This approach will reduce drug concentrations, avoid treatment resistance, and also improve treatment effectiveness by targeting new and nonredundant pathways in MM. Toward this goal, we examined the antimyeloma effects of MAL3-101, a member of a new class of non-ATP-site inhibitors of the heat shock protein (Hsp) 70 molecular chaperone. We discovered that MAL3-101 exhibited antimyeloma effects on MM cell lines in vitro and in vivo in a xenograft plasmacytoma model, as well as on primary tumor cells and bone marrow endothelial cells from myeloma patients. In combination with a proteasome inhibitor, MAL3-101 significantly potentiated the in vitro and in vivo antimyeloma effects. These data support a preclinical rationale for small molecule inhibition of Hsp70 function, either alone or in combination with other agents, as an effective therapeutic strategy for MM

Epidemic mitigation by statistical inference from contact tracing data

Contact-tracing is an essential tool in order to mitigate the impact of pandemic such as the COVID-19. In order to achieve efficient and scalable contact-tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible, but before the fraction of infected people reaches the scale where a lock-down becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized and thus compatible with privacy preserving standards. We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications.Comment: 21 pages, 7 figure