Estimation of errors in text and data processing

The company Adiss Lab Lts. obtained 1 000 000 medical reports that are either in free form text, or in XML format. One of the main goals of their development is to integrate an algorithm for information extraction (IE) in their platform. The verification of the algorithm’s output for a report is done by a medical doctor (MD) for a certain fee. Validating the correctness of all data would be overwhelming and very expensive. Hence, the problem, as presented by the company, is to provide a method (algorithm) which determines the minimum amount of reports that will validate the correctness of the IE algorithm and a procedure for selecting these reports. In order to solve the problem we have considered an algorithm-centric approach uses active learning and semi-supervised learning

Learning by hiring: the effects of scientists’ inbound mobility on research performance in academia

This study investigates the effects of scientists’ inbound mobility on the research performance of incumbent scientists in an academic setting. The theoretical framework integrates insights from learning theory and social comparison theory to suggest two main mechanisms behind these effects, localized learning and social comparison. The authors propose several hypotheses about the conditions that might intensify or weaken such effects. Specifically, the arrival of new scientific personnel is likely to exert stronger positive effects on the performance of incumbent scientists with shorter (cf. longer) organizational tenure; in addition, academic departments with less diversified expertise and with higher levels of internal collaborations likely reap greater benefits from learning by hiring. The empirical findings, based on a longitudinal analysis of a sample of 94 U.S. academic chemical engineering departments, provide empirical support for these contentions

Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission

<p>Abstract</p> <p>Background</p> <p>Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems.</p> <p>Results</p> <p>Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment.</p> <p>Conclusion</p> <p>Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.</p

Clinical impact of a targeted next-generation sequencing gene panel for autoinflammation and vasculitis.

BACKGROUND: Monogenic autoinflammatory diseases (AID) are a rapidly expanding group of genetically diverse but phenotypically overlapping systemic inflammatory disorders associated with dysregulated innate immunity. They cause significant morbidity, mortality and economic burden. Here, we aimed to develop and evaluate the clinical impact of a NGS targeted gene panel, the "Vasculitis and Inflammation Panel" (VIP) for AID and vasculitis. METHODS: The Agilent SureDesign tool was used to design 2 versions of VIP; VIP1 targeting 113 genes, and a later version, VIP2, targeting 166 genes. Captured and indexed libraries (QXT Target Enrichment System) prepared for 72 patients were sequenced as a multiplex of 16 samples on an Illumina MiSeq sequencer in 150bp paired-end mode. The cohort comprised 22 positive control DNA samples from patients with previously validated mutations in a variety of the genes; and 50 prospective samples from patients with suspected AID in whom previous Sanger based genetic screening had been non-diagnostic. RESULTS: VIP was sensitive and specific at detecting all the different types of known mutations in 22 positive controls, including gene deletion, small INDELS, and somatic mosaicism with allele fraction as low as 3%. Six/50 patients (12%) with unclassified AID had at least one class 5 (clearly pathogenic) variant; and 11/50 (22%) had at least one likely pathogenic variant (class 4). Overall, testing with VIP resulted in a firm or strongly suspected molecular diagnosis in 16/50 patients (32%). CONCLUSIONS: The high diagnostic yield and accuracy of this comprehensive targeted gene panel validate the use of broad NGS-based testing for patients with suspected AID

Digital Platforms in Climate Information Service Delivery for Farming in Ghana

AbstractPhone-based applications, Internet connectivity, and big data are enabling climate change adaptations. From ICT for development and agriculture perspectives, great interest exists in how digital platforms support climate information provision for smallholder farmers in Africa. The vast majority of these platforms both private and public are for delivering climate information services and for data collection. The sheer number of digital platforms in the climate information sector has created a complex information landscape for potential information users, with platforms differing in information type, technology, geographic coverage, and financing structures and infrastructure. This chapter mapped the existing climate information services and examined their impact on policy and practices in smallholder farming development in Africa, with a focus on Ghana. Specifically, the chapter provides highlights of digital platforms available to smallholder farmers and agricultural extension agents, analyzes the public and/or private governance arrangements that underpin the implementation of digital climate information delivery, and assesses the potential of these platforms in scaling up the use of climate information. The chapter contributes to understanding the dynamics of climate information delivery with digital tools in Africa, and suggests a future research agenda

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids