Identification and comparison of Mycobacterium avium isolates from patients and foods using polymerase chain reaction

The genus Mycobacterium is commonly associated with diseases such as leprosy, tuberculosis and other pulmonary infections. Mycobacterium avium is increasingly identified as a cause of disseminated disease in AIDS patients. A need for a better understanding of possible sources and routes of transmission of this organism is necessary. This study fircCalifornia State University, Northridge. Department of Biology.Includes bibliographical references (leaves 25-30

Regional to Global Assessments of Phytoplankton Dynamics From The SeaWiFS Mission

Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the oceanmay be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone

Functional Analysis and Fine Mapping of the 9p22.2 Ovarian Cancer Susceptibility Locus.

Genome-wide association studies have identified 40 ovarian cancer risk loci. However, the mechanisms underlying these associations remain elusive. In this study, we conducted a two-pronged approach to identify candidate causal SNPs and assess underlying biological mechanisms at chromosome 9p22.2, the first and most statistically significant associated locus for ovarian cancer susceptibility. Three transcriptional regulatory elements with allele-specific effects and a scaffold/matrix attachment region were characterized and, through physical DNA interactions, BNC2 was established as the most likely target gene. We determined the consensus binding sequence for BNC2 in vitro, verified its enrichment in BNC2 ChIP-seq regions, and validated a set of its downstream target genes. Fine-mapping by dense regional genotyping in over 15,000 ovarian cancer cases and 30,000 controls identified SNPs in the scaffold/matrix attachment region as among the most likely causal variants. This study reveals a comprehensive regulatory landscape at 9p22.2 and proposes a likely mechanism of susceptibility to ovarian cancer. SIGNIFICANCE: Mapping the 9p22.2 ovarian cancer risk locus identifies BNC2 as an ovarian cancer risk gene.See related commentary by Choi and Brown, p. 439

Individual Preferences and Social Interactions Determine the Aggregation of Woodlice

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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Elucidation of the RamA Regulon in Klebsiella pneumoniae Reveals a Role in LPS Regulation

Klebsiella pneumoniae is a significant human pathogen, in part due to high rates of multidrug resistance. RamA is an intrinsic regulator in K. pneumoniae established to be important for the bacterial response to antimicrobial challenge; however, little is known about its possible wider regulatory role in this organism during infection. In this work, we demonstrate that RamA is a global transcriptional regulator that significantly perturbs the transcriptional landscape of K. pneumoniae, resulting in altered microbe-drug or microbe-host response. This is largely due to the direct regulation of 68 genes associated with a myriad of cellular functions. Importantly, RamA directly binds and activates the lpxC, lpxL-2 and lpxO genes associated with lipid A biosynthesis, thus resulting in modifications within the lipid A moiety of the lipopolysaccharide. RamA-mediated alterations decrease susceptibility to colistin E, polymyxin B and human cationic antimicrobial peptide LL-37. Increased RamA levels reduce K. pneumoniae adhesion and uptake into macrophages, which is supported by in vivo infection studies, that demonstrate increased systemic dissemination of ramA overexpressing K. pneumoniae. These data establish that RamA-mediated regulation directly perturbs microbial surface properties, including lipid A biosynthesis, which facilitate evasion from the innate host response. This highlights RamA as a global regulator that confers pathoadaptive phenotypes with implications for our understanding of the pathogenesis of Enterobacter, Salmonella and Citrobacter spp. that express orthologous RamA proteins

Home range estimation within complex restricted environments: importance of method selection in detecting seasonal change

Estimating the home ranges of animals from telemetry data can provide vital information on their spatial behaviour, which can be applied by managers to a wide range of situations including reserve design, habitat management and interactions between native and non-native species. Methods used to estimate home ranges of animals in spatially restricted environments (e.g. rivers) are liable to overestimate areas and underestimate travel distances by including unusable habitat (e.g. river bank). Currently, few studies that collect telemetry data from species in restricted environments maximise the information that can be gathered by using the most appropriate home-range estimation techniques. Simulated location datasets as well as radio-fix data from 23 northern pike (Esox lucius) were used to examine the efficiency of home-range and travel estimators, with and without correction for unusable habitat, for detecting seasonal changes in movements. Cluster analysis most clearly demonstrated changes in range area between seasons for empirical data, also showing changes in patchiness, and was least affected by unusable-environment error. Kernel analysis showed seasonal variation in range area more clearly than peripheral polygons or ellipses. Range span, a linear estimator of home range, had no significant seasonal variation. Results from all range area estimators were smallest in autumn, when cores were least fragmented and interlocation movements smallest. Cluster analysis showed that core ranges were largest and most fragmented in summer, when interlocation distances were most variable, whereas excursion-sensitive methods (e.g. kernels) recorded the largest outlines in spring, when interlocation distances were largest. Our results provide a rationale for a priori selection of home-range estimators in restricted environments. Contours containing 95% of the location density defined by kernel analyses better reflected excursive activity than ellipses or peripheral polygons, whereas cluster analyses better defined range cores in usable habitat and indicate range fragmentation

Abstract 3007: A novel five gene signature predicts overall and recurrence-free survival in NSCLC

Abstract Gene expression profiling has been used to characterize prognosis in various cancers. Applying systems biology approach to cancer stem cell data generated from expression profiling of cell lines, a novel 5 gene signature was identified. Earlier studies had shown that side population cells isolated from established Non-Small Cell Lung Cancer (NSCLC) cell lines exhibit cancer stem cell properties. Microarray data from side population (SP) and main population (MP) cells isolated from 4 NSCLC lines (A549, H1650, H460, H1975) were used to examine gene expression profiles associated with stemness. Total RNA extracted from SP and MP samples were used to generate cRNA targets, which were hybridized to Human Genome U133A Plus 2.0 probe arrays. Raw data was processed and the mean center expression level for each gene was determined. We searched for differentially expressed genes that were over or under-expressed at least two fold commonly in all 4 cell lines. It was found that 354 genes were upregulated and 126 genes were downregulated in SP cells from all the 4 cell lines, compared to MP cells; of these, 89 up and 62 downregulated genes (average 2 fold changes) were used for Principle Component Analysis (PCA) and MetaCoreTM pathway analysis. Pathway analysis demonstrated representation of 4 up regulated genes (TOP2A, AURKB, BRRN1, CDK1) in chromosome condensation pathway and 1 down regulated gene FUS in chromosomal translocation. The microarray data was validated using quantitative RT-PCR on the 5 selected genes and all showed robust correlations between microarray and qRT-PCR expression data. Further, we analyzed two independent microarray gene expression datasets that included 361 lung adenocarcinoma patients from NCI Director's Challenge Set (DCS) for overall survival (OS) and 63 adenocarcinoma patients from Sungkyunkwan University (SKKU) for recurrence free survival (RFS). Statistical analyses were done using R package. Expression values were normalized using MAS5.0 and median expression values were used to dichotomize into low and high expression levels for 5 genes. To determine whether the gene signature correlates with poor prognosis, we performed Kaplan-Meier and log-rank test analysis. Kaplan-Meier analysis for survival showed significant trend for all the 5 genes. These genes predicted poor survival of patients in 361 DCS as well as 63 adenocarcinoma samples from SKKU. Our results suggest that genes involved in chromosome condensation are likely related with cancer stem cell like properties and therefore predict survival in lung adenocarcinoma. Further studies are needed to validate the key genes to provide potential new targets to intervene progression of lung adenocarcinoma. Our findings highlight gene signature for effective identification of lung adenocarcinoma patients with poor prognosis and designing more aggressive or tailored therapeutic properties for such patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3007. doi:1538-7445.AM2012-3007</jats:p