The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases

Utility of a Short Neuropsychological Protocol for Detecting HIV-Associated Neurocognitive Disorders in Patients with Asymptomatic HIV-1 Infection

Human Immunodeficiency Virus type 1 (HIV-1) infection is a chronic disease that affects ~40 million people worldwide. HIV-associated neurocognitive disorders (HAND) are common in individuals with HIV-1 Infection, and represent a recent public health problem. Here we evaluate the performance of a recently proposed short protocol for detecting HAND by studying 60 individuals with HIV-1-Infection and 60 seronegative controls from a Caribbean community in Barranquilla, Colombia. The short evaluation protocol used significant neuropsychological tests from a previous study of asymptomatic HIV-1 infected patients and a group of seronegative controls. Brief screening instruments, i.e., the Mini-mental State Examination (MMSE) and the International HIV Dementia Scale (IHDS), were also applied. Using machine-learning techniques, we derived predictive models of HAND status, and evaluated their performance with the ROC curves. The proposed short protocol performs exceptionally well yielding sensitivity, specificity, and overall prediction values >90%, and better predictive capacity than that of the MMSE and IHDS. Community-specific cut-off values for HAND diagnosis, based on the MMSE and IHDS, make this protocol suitable for HAND screening in individuals from this Caribbean community. This study shows the effectivity of a recently proposed short protocol to detect HAND in individuals with asymptomatic HIV-1-Infection. The application of community-specific cut-off values for HAND diagnosis in the clinical setting may improve HAND screening accuracy and facilitate patients’ treatment and follow-up. Further studies are needed to assess the performance of this protocol in other Latin American populations

Computationally predicted gene regulatory networks in molluscan biomineralization identify extracellular matrix production and ion transportation pathways

Acknowledgements We would like to thank Prof Peter Kille for constructive comments on this work. Funding This work was supported by the Natural Environment Research Council Core Funding to the British Antarctic Survey, a DTG Studentship (Project Reference: NE/J500173/1) to V.A.S. and a Junior Research Fellowship to V.A.S from Wolfson College, University of Cambridge. Conflict of Interest: none declared.Peer reviewedPublisher PD

Coordination of meristem and boundary functions by transcription factors in the SHOOT MERISTEMLESS regulatory network

The Arabidopsis homeodomain transcription factor SHOOT MERISTEMLESS (STM) is crucial for shoot apical meristem (SAM) function, yet the components and structure of the STMgene regulatory network (GRN) are largely unknown. Here, we show that transcriptional regulators are overrepresented among STM-regulated genes and, using these as GRN components in Bayesian network analysis, we infer STM GRN associations and reveal regulatory relationships between STM and factors involved in multiple aspects of SAM function. These include hormone regulation, TCP-mediated control of cell differentiation, AIL/PLT-mediated regulation of pluripotency and phyllotaxis, and specification of meristem-organ boundary zones via CUC1. We demonstrate a direct positive transcriptional feedback loop between STM and CUC1, despite their distinct expression patterns in the meristem and organ boundary, respectively. Our further finding that STM activates expression of the CUC1-targeting microRNA miR164c combined with mathematical modelling provides a potential solution for this apparent contradiction, demonstrating that these proposed regulatory interactions coupled with STM mobility could be sufficient to provide a mechanism for CUC1 localisation at the meristem-organ boundary. Our findings highlight the central role for the STM GRN in coordinating SAM functions

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Akustisches Bildverständnis für Sehbehinderte basierend auf einem modularen Computer Visions Sonifikations Modell

Die vorliegende Arbeit beschreibt ein System das blinden Menschen einen direkt erfahrbaren Zugang zu Bildern mit Hilfe akustischer Signale anbietet. Der Benutzer exploriert ein Bild interaktiv auf einem berührungsempfindlichen Bildschirm und erhält eine akustische Rückmeldung über den Bildinhalt an der jeweiligen Fingerposition. Die Gestaltung eines solchen Systems beinhaltet zwei größere Herausforderungen: Welche ist die relevante Bildinformation, und wie kann möglichst viel Information in einem Audiosignal untergebracht werden. Wir behandeln diese Probleme basierend auf einem modularen Computer Vision Sonikations Modell, welches wir als grundlegendes Gerüst für die Aufnahme, Exploration und Sonikation von visueller Information zur Unterstützung blinder Menschen vorstellen. Es werden einige Ansätze vorgestellt, welche hierzu die Information auf verschiedenen Abstraktionsebenen kombinieren. So z.B. sehr grundlegende Information wie Farbe, Kanten und Rauigkeit und komplexere Information welche durch die Verwendung von Machine Learning Algorithmen gewonnen werden kann. Diese Machine Learning Algorithmen behandeln sowohl das Erkennen von Objekten als auch die Klassikation von Bildregionen in "künstlich" und "natürlich", basierend auf einem neu entwickelten Typs eines probabilistischen graphischen Modells. Wir zeigen, dass dieser Mehr-Ebenen Ansatz dem Benutzer direkten Zugang zum Wesen und Position von Objekten und Strukturen im Bild ermöglicht und gleichzeitig das Potential neuester Entwicklungen im Bereich Computer Vision und Machine Learning ausnutzt. Während der Exploration kann der Benutzer erkannte "künstliche" Strukturen oder bestimmte natürliche Regionen als Referenzpunkte verwenden um andere natürliche Regionen mit Hilfe deren individueller Position, Farbe und Texturen zu klassizieren. Wir werden zeigen, dass geburtsblinde Teilnehmer diese Strategie erfolgreich einsetzen um ganze Szenen zu interpretieren und zu verstehen.This thesis presents a system that strives to give visually impaired people direct perceptual access to images via an acoustic signal. The user explores the image actively on a touch screen or touch pad and receives auditory feedback about the image content at the current position. The design of such a system involves two major challenges: what is the most useful and relevant image information, and how can as much information as possible be captured in an audio signal. We address those problems, based on a Modular Computer Vision Sonication Model, which we propose as a general framework for acquisition, exploration and sonication of visual information to support visually impaired people. General approaches are presented that combine low-level information, such as color, edges, and roughness, with mid- and high-level information obtained from Machine Learning algorithms. This includes object recognition and the classication of regions into the categories "man-made" versus "natural" based on a novel type of discriminative graphical model. We argue that this multi-level approach gives users direct access to the identity and location of objects and structures in the image, yet it still exploits the potential of recent developments in Computer Vision and Machine Learning. During exploration, the user can utilize detected man made structures or specic natural regions as reference points to classify other natural regions by their individual location, color and texture. We show that congenital blind participants employ that strategy successfully to interpret and understand whole scenes

Una pausa en el camino real (Cuenca del Arroyo Pavón-del Sauce, Provincia de Santa Fe)

La primera línea fronteriza del sur santafesino se estableció sobre el camino real y de carretas constituyéndose como parte del itinerario de una red viaria mayor que unía centros administrativos virreinales. Una de las pausas entre los trayectos del camino que unía Buenos Aires y Córdoba sobre el Arroyo del Sauce (Santa Fe) fue India Muerta, un punto estratégico e imprescindible de guardia militar, abastecimiento de agua, alimentos y muda de caballos para continuar el viaje. A partir de la documentación histórica, catastral, topográfica, y de las nuevas tecnologías aplicadas a la arqueología, se localizaron dos fuertes India Muerta, el poblado homónimo y las trazas del camino adyacente. Las plantas arquitectónicas de los fuertes son cuadriláteros de similares medidas y uno de ellos presenta baluartes en sus esquinas. Las construcciones se realizaron con un lapso de tiempo estimado entre una y otra, de 10 años como mínimo (ca. 1766 y 1776). Tras desmantelar el último fuerte en servicio, el poblado continuó hasta fines del siglo XI

A Modular Computer Vision Sonification Model For The Visually Impaired

Presented at the 18th International Conference on Auditory Display (ICAD2012) on June 18-21, 2012 in Atlanta, Georgia.Reprinted by permission of the International Community for Auditory Display, http://www.icad.org.This paper presents a Modular Computer Vision Sonification Model which is a general framework for acquisition, exploration and sonification of visual information to support visually impaired people. The model exploits techniques from Computer Vision and aims to convey as much information as possible about the image to the user, including color, edges and what we refer to as Orientation maps and Micro-Textures. We deliberatively focus on low level features to provide a very general image analysis tool. Our sonification approach relies on MIDI using "real-world" instead of synthetic instruments. The goal is to provide direct perceptual access to images or environments actively and in real time. Our system is already in use, at an experimental stage, at a local residential school, helping congenital blind children develop various cognitive abilities such as geometric understanding and spatial sense as well as offering an intuitive approach to colors and textures