Using Mathematical Models In A Unified Approach To Predicting The Next Emerging Infectious Disease

Emerging infectious diseases (EIDs) pose a significant threat to human health, global economies, and conservation (Smolinski et al. 2003). They are defined as diseases that have recently increased in incidence (rate of the development of new cases during a given time period), are caused by pathogens that recently moved from one host population to another, have recently evolved, or have recently exhibited a change in pathogenesis (Morse 1993; Krause 1994). Some EIDs threaten global public health through pandemics with large-scale mortality (e.g., HN/AIDS). Others cause smaller outbreaks but have high case fatality ratios or lack effective therapies or vaccines (e.g. Ebola virus or methicillin-resistant Staphylococcus aureus). As a group, EIDs cause hundreds of thousands of deaths each year, and some outbreaks (e.g., SARS, H5N1) have cost the global economy tens of billions of dollars. Emerging diseases also affect plants, livestock, and wildlife and are recognized as a Significant threat to the conservation of biodiversity (Daszak et al. 2000). Approximately 60% of emerging human disease events are zoonotic, and over 75% of these diseases originate in wildlife (Jones et al. 2008). The global response to such epidemics is frequently reactive, and the effectiveness of conventional disease control operations is often too little, too late\u27: With rising globalization, the ease with which diseases spread globally has increased dramatically in recent times. Also, interactions between humans and wildlife have intensified through trade markets, agricultural intensification, logging and mining, and other forms of development that encroach into wild areas. Rapid human population growth, land use change, and change in global trade and travel require a shift toward a proactive, predictive, and preventive approaches for the next zoonotic pandemic

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(sup 1) per degC. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

Amyloid-beta oligomerization is associated with the generation of a typical peptide fragment fingerprint

Amyloid-beta (A beta) peptide oligomerization plays a central role in the pathogenesis of Alzheimer's disease (AD), and A beta oligomers are collectively considered an appealing therapeutic target for the treatment of AD. However, the molecular mechanisms leading to the pathologic accumulation of oligomers are unclear, and the exact structural composition of oligomers is being debated. Using targeted and quantitative mass spectrometry, we reveal site-specific A beta autocleavage during the early phase of aggregation, producing a typical A beta fragment signature and that truncated A beta peptides can form stable oligomeric complexes with full-length A beta peptide. We show that the use of novel anti-A beta antibodies raised against these truncated A beta isoforms allows for monitoring and targeting the accumulation of truncated A beta. fragments. Antibody-enabled screening of transgenic models of AD as well as human postmortem brain tissue and cerebrospinal fluid revealed that aggregation-associated A beta cleavage is a highly relevant clinical feature of AD. (C) 2016 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved

miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades

microRNAs (miRNAs) are a large class of small non-coding RNAs which post-transcriptionally regulate the expression of a large fraction of all animal genes and are important in a wide range of biological processes. Recent advances in high-throughput sequencing allow miRNA detection at unprecedented sensitivity, but the computational task of accurately identifying the miRNAs in the background of sequenced RNAs remains challenging. For this purpose, we have designed miRDeep2, a substantially improved algorithm which identifies canonical and non-canonical miRNAs such as those derived from transposable elements and informs on high-confidence candidates that are detected in multiple independent samples. Analyzing data from seven animal species representing the major animal clades, miRDeep2 identified miRNAs with an accuracy of 98.6–99.9% and reported hundreds of novel miRNAs. To test the accuracy of miRDeep2, we knocked down the miRNA biogenesis pathway in a human cell line and sequenced small RNAs before and after. The vast majority of the >100 novel miRNAs expressed in this cell line were indeed specifically downregulated, validating most miRDeep2 predictions. Last, a new miRNA expression profiling routine, low time and memory usage and user-friendly interactive graphic output can make miRDeep2 useful to a wide range of researchers

The genome of the yellow potato cyst nematode, Globodera rostochiensis, reveals insights into the basis of parasitism and virulence

BACKGROUND: The yellow potato cyst nematode, Globodera rostochiensis, is a devastating plant pathogen of global economic importance. This biotrophic parasite secretes effectors from pharyngeal glands, some of which were acquired by horizontal gene transfer, to manipulate host processes and promote parasitism. G. rostochiensis is classified into pathotypes with different plant resistance-breaking phenotypes. RESULTS: We generate a high quality genome assembly for G. rostochiensis pathotype Ro1, identify putative effectors and horizontal gene transfer events, map gene expression through the life cycle focusing on key parasitic transitions and sequence the genomes of eight populations including four additional pathotypes to identify variation. Horizontal gene transfer contributes 3.5 % of the predicted genes, of which approximately 8.5 % are deployed as effectors. Over one-third of all effector genes are clustered in 21 putative ‘effector islands’ in the genome. We identify a dorsal gland promoter element motif (termed DOG Box) present upstream in representatives from 26 out of 28 dorsal gland effector families, and predict a putative effector superset associated with this motif. We validate gland cell expression in two novel genes by in situ hybridisation and catalogue dorsal gland promoter element-containing effectors from available cyst nematode genomes. Comparison of effector diversity between pathotypes highlights correlation with plant resistance-breaking. CONCLUSIONS: These G. rostochiensis genome resources will facilitate major advances in understanding nematode plant-parasitism. Dorsal gland promoter element-containing effectors are at the front line of the evolutionary arms race between plant and parasite and the ability to predict gland cell expression a priori promises rapid advances in understanding their roles and mechanisms of action.SE-vdA is supported by BBSRC grant BB/M014207/1. Sequencing was funded by BBSRC grant BB/F000642/1 to the University of Leeds and grant BB/F00334X/1 to the Wellcome Trust Sanger Institute). DRL was supported by a fellowship from The James Hutton Institute and the School of Biological Sciences, University of Edinburgh. GK was supported by a BBSRC PhD studentship. The James Hutton Institute receives funding from the Scottish Government. JAC and NEH are supported by the Wellcome Trust through its core funding of the Wellcome Trust Sanger Institute (grant 098051). This work was also supported by funding from the Canadian Safety and Security Program, project number CRTI09_462RD

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma

Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients