Imitating Manual Curation of Text-Mined Facts in Biomedicine

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts—to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine

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Body Maps on the Human Genome

The human genome possesses large-scale structure: In particular, body tissue genes map somatotopically onto the complete chromosome set. The synoptic picture is that genes highly expressed in particular tissues are not randomly distributed on the genome. Rather, they form a "genome homunculus": a multi-dimensional, genome-wide body representation extending across chromosome territories (each chromosome's preferred nucleus locale) of the entire spermcell nucleus. The antero-posterior axis of the body corresponds to the head-tail axis of the nucleus, and the dorso-ventral body axis to the central-peripheral nucleus axis. Somatotopic maps in cerebral cortex have been reported for over a century. This pervasive genome mapping merits further attention.https://doi.org/10.1186/1755-8166-6-6

Body Maps on Human Chromosomes

An exploration of the hypothesis that human genes are organized somatotopically: For each autosomal chromosome, its tissue-specific genes tend to have relative positions on the chromosome that mirror corresponding positions of the tissues in the body. In addition, there appears to be a division of labor: Such a homunculus representation on a chromosome holds significantly for either the anteroposterior or the dorsoventral body axis. In turn, anteroposterior and dorsoventral chromosomes tend to occupy separate zones in the spermcell nucleus. One functional rationale of such largescale organization is for efficient interconnections in the genome

Global Layout Optimization of Olfactory Cortex and of Amygdala of Rat

Principles of connection minimization in the nervous system apply not only to complete neural systems but also to smaller subsystems such as rat olfactory cortex and rat amygdala. These subsystems have a three-dimensional organization, rather than previously-studied two-dimensional and one-dimensional schemes. Nonetheless, wire-cost savings show optimality at similar levels, suggesting neural optimization principles are widespread, followed at multiple scales of the nervous system

Nervous system maps on the C. elegans genome

This project begins from a synoptic point of view, focusing upon the large-scale (global) landscape of the genome. This is along the lines of combinatorial network optimization in computational complexity theory [1]. Our research program here in turn originated along parallel lines in computational neuroanatomy [2,3,4,5]. Rather than mapping body structure onto the genome, the present report focuses upon statistically significant mappings of the Caenorhabditis elegans nervous system onto its genome. Via published datasets, evidence is derived for a "wormunculus", on the model of a homunculus representation, but on the C. elegans genome. The main method of testing somatic-genomic position-correlations here is via public genome databases, with r^2 analyses and p evaluations. These findings appear to yield some of the basic structural and functional organization of invertebrate nucleus and chromosome architecture. The design rationale for somatic maps on the genome in turn may be efficient interconnections. A next question this study raises: How do these various somatic maps mesh (interrelate, interact) with each other

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore ‘Challenges in Mining Drug Adverse Reactions’. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.M.K.: This work was supported in part through the collaboration between the Spanish Plan for the Advancement of Language Technology (Plan TL) and the Barcelona Supercomputing Center; we also acknowledge the 2020 Proyectos de I+D+i - RTI Tipo A (PID2020-119266RA-I00) for support. Ö.U.: This study was supported in part by the National Library of Medicine under Award Number R15LM013209 and R13LM013127.Peer ReviewedPostprint (published version

Prácticas económicas alternativas de escala local en espacios urbanos: el caso de Zaragoza

En las últimas décadas, con más intensidad a raíz de la crisis del 2008, han proliferado algunas prácticas económicas alternativas al sistema dominante, especialmente en las áreas urbanas en que los nuevos movimientos sociales han tenido más arraigo. Se toma como estudio de caso la ciudad de Zaragoza, que estuvo gobernada en la pasada legislatura por una coalición de la nueva izquierda, siendo uno de los llamados ayuntamientos del cambio. Se han seleccionado prácticas que funcionan a escala local siguiendo procedimientos alejados de los convencionales: huertos urbanos comunitarios, mercados y grupos de consumo agroecológico, monedas comunitarias (banco de tiempo, mercados de trueque y moneda social) y centros sociales autogestionados. Se emplea una metodología cualitativa basada en entrevistas semiestructuradas, combinada con cuestionarios cerrados y observación participante. Dichas prácticas se localizan de modo disperso por todo el espacio urbano. Se han puesto en relación con los movimientos sociales urbanos y con la política local. Se concluye que su impacto es limitado en términos cuantitativos, pero que disponen de un amplio potencial transformador por las características de sus practicantes y el apoyo de la administración local

Body condition estimation on cows from depth images using Convolutional Neural Networks

BCS (“Body Condition Score”) is a method used to estimate body fat reserves and accumulated energy balance of cows. BCS heavily influences milk production, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve better animal response, but this is a time-consuming and subjective task performed visually by expert scorers. Several studies have tried to automate BCS of dairy cows by applying image analysis and machine learning techniques. This work analyzes these studies and proposes a system based on Convolutional Neural Networks (CNNs) to improve overall automatic BCS estimation, whose use might be extended beyond dairy production. The developed system has achieved good estimation results in comparison with other systems in the area. Overall accuracy of BCS estimations within 0.25 units of difference from true values was 78%, while overall accuracy within 0.50 units was 94%. Similarly, weighted precision and recall, which took into account imbalance BCS distribution in the built dataset, show similar values considering those error ranges.Fil: Rodríguez Alvarez, Juan Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; ArgentinaFil: Arroqui, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; ArgentinaFil: Mangudo, Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires; ArgentinaFil: Toloza, Juan Manuel. Universidad Nacional del Centro de la Provincia de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Jatip, Daniel Esteban. Universidad Nacional del Centro de la Provincia de Buenos Aires; ArgentinaFil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Teyseyre, Alfredo Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Sanz, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Machado, Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin