Mixed policies give more options in multifunctional tropical forest landscapes

This archive stores data utilised in: Mixed policies give more options in multifunctional tropical forest landscapesLaw E.A., Bryan B.A., Meijaard E., Mallawaarachchi T., Struebig M.J., Watts M., Wilson K.A.Journal of Applied Ecology 2016Corresponding author:Elizabeth A. Law*The University of Queensland, School of Biological [email protected]: Jan 18, 2016----------------------DETAILS:Along with this txt file, this archive contains five csv files included, and one folder containing a shape file. These are in the typical format required for input into Marxan with Zones, available from http://www.uq.edu.au/marxan/ (also see new cloud development on Marxan.net). The data time frames relate to a start year of 2008 (see associated publication for further details.Readme.txt - this documentZones.csv - contains zone identification numbers (zoneid) and names (zonenames)FeatureTargets.csv - contains feature id (id), the targets (target; units are specified in supplementary material), species penalty factor (spf; weighting number to determine if the feature is considered as an optimisation threshold constraint, 1, or not, 0), and feature names (name; features 1-9 and 19-20 represent primate species, 10-14 forest types, and 15-17 production values smallholder agriculture, timber, and oil palm, and 18 carbon emissions reduction.Extant.csv - contains planning unit id (pu), the extant class (class; using descriptive codes), and the area (area.ha, in hectares).PuVsFeatures.csv - for every planning unit and feature combination, the 'amount' gives the maximum possible achievement for that feature in that planning unit. Units of measurement are indicated in the main text/supplementary methods and associated papers detailing data development.BaselineZoneContributions.csv - for every zone, planning unit, and feature combination (identified using zoneid, puid, featid codes found in their respective files), this gives the fraction of the full amount possible to achieve within that pu for that feature. Pulayer folder - contains a shape file created in arcgis for the planning units. Coordinate reference system is WGS 84 / UTM zone 49S. One column in the attribute table, indicating the planning unit number (pu)

Ecosystem services from a degraded peatland of Central Kalimantan: implications for policy, planning, and management

Increasingly, landscapes are managed for multiple objectives to balance social, economic, and environmental goals. The Ex-Mega Rice Project (EMRP) peatland in Central Kalimantan, Indonesia provides a timely example with globally significant development, carbon, and biodiversity concerns. To inform future policy, planning, and management in the EMRP, we quantified and mapped ecosystem service values, assessed their spatial interactions, and evaluated the potential provision of ecosystem services under future land-use scenarios. We focus on key policy-relevant regulating (carbon stocks and the potential for emissions reduction), provisioning (timber, crops from smallholder agriculture, palm oil), and supporting (biodiversity) services. We found that implementation of existing land-use plans has the potential to improve total ecosystem service provision. We identify a number of significant inefficiencies, trade-offs, and unintended outcomes that may arise. For example, the potential development of existing palm oil concessions over one-third of the region may shift smallholder agriculture into low-productivity regions and substantially impact carbon and biodiversity outcomes. While improved management of conservation zones may enhance the protection of carbon stocks, not all biodiversity features will be represented, and there will be a reduction in timber harvesting and agricultural production. This study highlights how ecosystem service analyses can be structured to better inform policy, planning, and management in globally significant but data-poor regions. Read More: http://www.esajournals.org/doi/abs/10.1890/13-2014.

Detecting co-selection through excess linkage disequilibrium in bacterial genomes

Population genomics has revolutionized our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here, we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data

Australia and New Zealand renal gene panel testing in routine clinical practice of 542 families

Genetic testing in nephrology clinical practice has moved rapidly from a rare specialized test to routine practice both in pediatric and adult nephrology. However, clear information pertaining to the likely outcome of testing is still missing. Here we describe the experience of the accredited Australia and New Zealand Renal Gene Panels clinical service, reporting on sequencing for 552 individuals from 542 families with suspected kidney disease in Australia and New Zealand. An increasing number of referrals have been processed since service inception with an overall diagnostic rate of 35%. The likelihood of identifying a causative variant varies according to both age at referral and gene panel. Although results from high throughput genetic testing have been primarily for diagnostic purposes, they will increasingly play an important role in directing treatment, genetic counseling, and family planning

The Study of Ketamine for Youth Depression (SKY-D): study protocol for a randomised controlled trial of low-dose ketamine for young people with major depressive disorder

Background: Existing treatments for young people with severe depression have limited effectiveness. The aim of the Study of Ketamine for Youth Depression (SKY-D) trial is to determine whether a 4-week course of low-dose subcutaneous ketamine is an effective adjunct to treatment-as-usual in young people with major depressive disorder (MDD). Methods: SKY-D is a double-masked, randomised controlled trial funded by the Australian Government’s National Health and Medical Research Council (NHMRC). Participants aged between 16 and 25 years (inclusive) with moderate-to-severe MDD will be randomised to receive either low-dose ketamine (intervention) or midazolam (active control) via subcutaneous injection once per week for 4 weeks. The primary outcome is change in depressive symptoms on the Montgomery-Åsberg Depression Rating Scale (MADRS) after 4 weeks of treatment. Further follow-up assessment will occur at 8 and 26 weeks from treatment commencement to determine whether treatment effects are sustained and to investigate safety outcomes. Discussion: Results from this trial will be important in determining whether low-dose subcutaneous ketamine is an effective treatment for young people with moderate-to-severe MDD. This will be the largest randomised trial to investigate the effects of ketamine to treat depression in young people. Trial registration: Australian and New Zealand Clinical Trials Registry ID: ACTRN12619000683134. Registered on May 7, 2019. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377513

The human gut virome: composition, colonization, interactions, and impacts on human health

The gut virome is an incredibly complex part of the gut ecosystem. Gut viruses play a role in many disease states, but it is unknown to what extent the gut virome impacts everyday human health. New experimental and bioinformatic approaches are required to address this knowledge gap. Gut virome colonization begins at birth and is considered unique and stable in adulthood. The stable virome is highly specific to each individual and is modulated by varying factors such as age, diet, disease state, and use of antibiotics. The gut virome primarily comprises bacteriophages, predominantly order Crassvirales, also referred to as crAss-like phages, in industrialized populations and other Caudoviricetes (formerly Caudovirales). The stability of the virome’s regular constituents is disrupted by disease. Transferring the fecal microbiome, including its viruses, from a healthy individual can restore the functionality of the gut. It can alleviate symptoms of chronic illnesses such as colitis caused by Clostridiodes difficile. Investigation of the virome is a relatively novel field, with new genetic sequences being published at an increasing rate. A large percentage of unknown sequences, termed ‘viral dark matter’, is one of the significant challenges facing virologists and bioinformaticians. To address this challenge, strategies include mining publicly available viral datasets, untargeted metagenomic approaches, and utilizing cutting-edge bioinformatic tools to quantify and classify viral species. Here, we review the literature surrounding the gut virome, its establishment, its impact on human health, the methods used to investigate it, and the viral dark matter veiling our understanding of the gut virome

Detecting co-selection through excess linkage disequilibrium in bacterial genomes

Population genomics has revolutionized our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here, we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data

Comprehensive evaluation of a prospective Australian patient cohort with suspected genetic kidney disease undergoing clinical genomic testing: a study protocol

Introduction: Recent advances in genomic technology have allowed better delineation of renal conditions, the identification of new kidney disease genes and subsequent targets for therapy. To date, however, the utility of genomic testing in a clinically ascertained, prospectively recruited kidney disease cohort remains unknown. The aim of this study is to explore the clinical utility and cost-effectiveness of genomic testing within a national cohort of patients with suspected genetic kidney disease who attend multidisciplinary renal genetics clinics. Methods and Analysis: This is a prospective observational cohort study performed at 16 centres throughout Australia. Patients will be included if they are referred to one of the multidisciplinary renal genetics clinics and are deemed likely to have a genetic basis to their kidney disease by the multidisciplinary renal genetics team. The expected cohort consists of 360 adult and paediatric patients recruited by December 2018 with ongoing validation cohort of 140 patients who will be recruited until June 2020. The primary outcome will be the proportion of patients who receive a molecular diagnosis via genomic testing (diagnostic rate) compared with usual care. Secondary outcomes will include change in clinical diagnosis following genomic testing, change in clinical management following genomic testing and the cost-effectiveness of genomic testing compared with usual care. Ethics and Dissemination: The project has received ethics approval from the Melbourne Health Human Research Ethics Committee as part of the Australian Genomics Health Alliance protocol: HREC/16/MH/251. All participants will provide written informed consent for data collection and to undergo clinically relevant genetic/genomic testing. The results of this study will be published in peer-reviewed journals and will also be presented at national and international conferences.Kushani Jayasinghe, Zornitza Stark, Chirag Patel, Amali Mallawaarachchi, Hugh McCarthy, Randall Faull, Aron Chakera, Madhivanan Sundaram, Matthew Jose, Peter Kerr, You Wu, Louise Wardrop, Ilias Goranitis, Stephanie Best, Melissa Martyn, Catherine Quinlan, Andrew J Mallet

Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications

Predicting the impact of coding and noncoding variants on splicing is challenging, particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing splice prediction tools are complementary but knowing which to use for each splicing context remains difficult. Here, we describe Introme, which uses machine learning to integrate predictions from several splice detection tools, additional splicing rules, and gene architecture features to comprehensively evaluate the likelihood of a variant impacting splicing. Through extensive benchmarking across 21,000 splice-altering variants, Introme outperformed all tools (auPRC: 0.98) for the detection of clinically significant splice variants. Introme is available at https://github.com/CCICB/introme .Patricia J. Sullivan, Velimir Gayevskiy, Ryan L. Davis, Marie Wong, Chelsea Mayoh, Amali Mallawaarachchi, Yvonne Hort, Mark J. McCabe, Sarah Beecroft, Matilda R. Jackson, Peer Arts, Andrew Dubowsky, Nigel Laing, Marcel E. Dinger, Hamish S. Scott, Emily Oates, Mark Pinese, and Mark J. Cowle