OGRE: Overlap Graph-based metagenomic Read clustEring

The microbes that live in an environment can be identified from the genomic material that is present, also referred to as the metagenome. Using Next Generation Sequencing techniques this genomic material can be obtained from the environment, resulting in a large set of sequencing reads. A proper assembly of these reads into contigs or even full genomes allows one to identify the microbial species and strains that live in the environment. Assembling a metagenome is a challenging task and can benefit from clustering the reads into species-specific bins prior to assembly. In this paper we propose OGRE, an Overlap-Graph based Read clustEring procedure for metagenomic read data. OGRE is the only method that can successfully cluster reads in species-specific bins for large metagenomic datasets without running into computation time- or memory issues

Infectious diseases co-morbidities among patients attending Kogi State University Teaching Hospital: a ten-year retrospective study

Background: Co-existence of more than one acute or chronic infectious diseases in a person either concurrently or sequentially with consequent economic burden varies differently from one part of the world to another, with regional and population specific patterns. This study aims to provide co-morbid patho-epidemiological pattern of six infectious diseases; HIV, tuberculosis (TB), malaria, syphilis, hepatitis B and hepatitis C virus infections. Methodology: This research is a ten-year retrospective review of records of patients admitted at various wards of Kogi State University Teaching hospital and referred to the Laboratory Department of the hospital for investigations between June 2012 and July 2021. HIV was screened using the national serial algorithm, TB was diagnosed with the GeneXpert MTB, malaria parasite was identified by blood film microscopy, and syphilis, hepatitis B and hepatitis C viruses were screened using rapid diagnostic kits. Data were analysed with SPSS version 23.0 and association of variables with respect to gender and age group was determined using Chi square, with p< 0.05 considered to be statistically significant. Results: A total of 223 patients were retrospectively reviewed with 102 (45.7%) males and 121 (54.3%) females. Co-morbidities occurred most frequently among age groups 21-30 years (34.1%), 31-40 years (39.0%) and 41-50 years (16.6%). The most frequent co-morbidity pattern was HIV/TB (81.6%) followed by HBV/MP (4.5%), HIV/HBV (4.0%), HIV/MP (3.1%), TB/MP (2.7%), HBV/HCV (2.2%) while HCV/MP, TB/HCV, HCV/syphilis, and TB/HBV were (0.4%) each. There was no significant difference in the frequency of co-morbidity with respect to gender and age groups of patients (p>0.05). Conclusion: Co-existence of chronic infectious disease in a person increases the risk of morbidities and mortalities. Therefore, diagnosis, and concurrent treatment and management of co-morbid infectious diseases should be incorporated into our routine healthcare system and appropriate resources should be allotted for this in health plans.   Frebch title: Co-morbidités des maladies infectieuses chez les patients fréquentant l'hôpital universitaire de l'État de Kogi: une étude rétrospective sur dix ans Contexte: La coexistence de plusieurs maladies infectieuses aiguës ou chroniques chez une personne, simultanément ou séquentiellement, avec un fardeau économique conséquent, varie différemment d'une partie du monde à l'autre, avec des schémas régionaux et spécifiques à la population. Cette étude vise à fournir le schéma patho-épidémiologique comorbide de six maladies infectieuses; VIH, tuberculose (TB), paludisme, syphilis, infections par le virus de l'hépatite B et de l'hépatite C. Méthodologie: Cette recherche est un examen rétrospectif sur dix ans des dossiers de patients admis dans divers services de l'hôpital universitaire de l'État de Kogi et référés au département de laboratoire de l'hôpital pour des enquêtes entre juin 2012 et juillet 2021. Le VIH a été dépisté à l'aide de la série nationale algorithme, la tuberculose a été diagnostiquée avec le GeneXpert MTB, le parasite du paludisme a été identifié par microscopie de frottis sanguin et les virus de la syphilis, de l'hépatite B et de l'hépatite C ont été dépistés à l'aide de kits de diagnostic rapide. Les données ont été analysées avec SPSS version 23.0 et l'association des variables par rapport au sexe et au groupe d'âge a été déterminée à l'aide du Chi carré, avec p<0,05 considéré comme statistiquement significatif. Résultats: Un total de 223 patients ont été revus rétrospectivement avec 102 (45,7%) hommes et 121 (54,3%) femmes. Les comorbidités sont survenues le plus fréquemment dans les groupes d'âge 21-30 ans (34,1 %), 31-40 ans (39,0%) et 41-50 ans (16,6%). Le schéma de comorbidité le plus fréquent était le VIH/TB (81,6%), suivi du VHB/MP (4,5%), du VIH/VHB (4,0%), du VIH/MP (3,1%), de la TB/MP (2,7%), du VHB/VHC (2,2%) tandis que VHC/MP, TB/VHC, VHC/syphilis et TB/VHB étaient (0,4%) chacun. Il n'y avait pas de différence significative dans la fréquence des comorbidités en fonction du sexe et des tranches d'âge des patients (p>0,05). Conclusion: La coexistence de maladies infectieuses chroniques chez une personne augmente le risque de morbidité et de mortalité. Par conséquent, le diagnostic, le traitement et la gestion concomitants des maladies infectieuses comorbides doivent être intégrés à notre système de soins de santé de routine et des ressources appropriées doivent être allouées à cet effet dans les plans de santé

A conceptual framework for studying collective reactions to events in location-based social media

Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events

The Diagnostic Potential of Transition Region Lines under-going Transient Ionization in Dynamic Events

We discuss the diagnostic potential of high cadence ultraviolet spectral data when transient ionization is considered. For this we use high cadence UV spectra taken during the impulsive phase of a solar flares (observed with instruments on-board the Solar Maximum Mission) which showed excellent correspondence with hard X-ray pulses. The ionization fraction of the transition region ion O V and in particular the contribution function for the O V 1371A line are computed within the Atomic Data and Analysis Structure, which is a collection of fundamental and derived atomic data and codes which manipulate them. Due to transient ionization, the O V 1371A line is enhanced in the first fraction of a second with the peak in the line contribution function occurring initially at a higher electron temperature than in ionization equilibrium. The rise time and enhancement factor depend mostly on the electron density. The fractional increase in the O V 1371A emissivity due to transient ionization can reach a factor of 2--4 and can explain the fast response in the line flux of transition regions ions during the impulsive phase of flares solely as a result of transient ionization. This technique can be used to diagnostic the electron temperature and density of solar flares observed with the forth-coming Interface Region Imaging Spectrograph.Comment: 18 pages, 6 figure

Spin decay and quantum parallelism

We study the time evolution of a single spin coupled inhomogeneously to a spin environment. Such a system is realized by a single electron spin bound in a semiconductor nanostructure and interacting with surrounding nuclear spins. We find striking dependencies on the type of the initial state of the nuclear spin system. Simple product states show a profoundly different behavior than randomly correlated states whose time evolution provides an illustrative example of quantum parallelism and entanglement in a decoherence phenomenon.Comment: 6 pages, 4 figures included, version to appear in Phys. Rev.

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disea

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Motivation: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective. Results: Our approach identifies potentially ALS-associated promoter regions, and generally outperforms other classification methods. Test results support the hypothesis that non-additive combinations of variants contribute to ALS. Architectures and protocols developed are tailored toward processing population-scale, whole-genome data. We consider this a relevant first step toward deep learning assisted genotype-phenotype association in whole genome-sized data

Inpatient Transition to Virtual Care During COVID-19 Pandemic

Introduction: During the coronavirus disease 2019 (COVID-19) outbreak, novel approaches to diabetes care have been employed. Care in both the inpatient and outpatient setting has transformed considerably. Driven by the need to reduce the use of personal protective equipment and exposure for patients and providers alike, we transitioned inpatient diabetes management services to largely "virtual" or remotely provided care at our hospital. Methods: Implementation of a diabetes co-management service under the direction of the University of North Carolina division of endocrinology was initiated in July 2019. In response to the COVID-19 pandemic, the diabetes service was largely transitioned to a virtual care model in March 2020. Automatic consults for COVID-19 patients were implemented. Glycemic outcomes from before and after transition to virtual care were evaluated. Results: Data over a 15-week period suggest that using virtual care for diabetes management in the hospital is feasible and can provide similar outcomes to traditional face-to-face care. Conclusion: Automatic consults for COVID-19 patients ensure that patients with serious illness receive specialized diabetes care. Transitioning to virtual care models does not limit the glycemic outcomes of inpatient diabetes care and should be employed to reduce patient and provider exposure in the setting of COVID-19. These findings may have implications for reducing nosocomial infection in less challenging times and might address shortage of health care providers, especially in the remote areas