Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic ...practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.
Genomic islands (clusters of genes of probable horizontal origin; GIs) play a critical role in medically important adaptations of bacteria. Recently, several computational methods have been developed ...to predict GIs that utilize either sequence composition bias or comparative genomics approaches. IslandViewer is a web accessible application that provides the first user-friendly interface for obtaining precomputed GI predictions, or predictions from user-inputted sequence, using the most accurate methods for genomic island prediction: IslandPick, IslandPath-DIMOB and SIGI-HMM. The graphical interface allows easy viewing and downloading of island data in multiple formats, at both the chromosome and gene level, for method-specific, or overlapping, GI predictions. Availability: The IslandViewer web service is available at http://www.pathogenomics.sfu.ca/islandviewer and the source code is freely available under the GNU GPL license. Contact: brinkman@sfu.ca
Bacterial genomes contain clusters of genes that are acquired by horizontal transfer, called genomic islands (GIs). GIs are frequently associated with microbial adaptations that are of medical and ...environmental interest, and they have had a substantial impact on bacterial evolution. Therefore, there is growing interest in efficiently identifying GIs in newly sequenced bacterial genomes. Several computational methods for detecting GIs have been developed recently, presenting researchers with a myriad of choices. Here, we discuss the limitations and benefits of the main approaches that are available and present guidelines to aid researchers in effectively identifying these important genomic regions.
Individuals often avoid or delay seeking professional help for mental health problems. Stigma may be a key deterrent to help-seeking but this has not been reviewed systematically. Our systematic ...review addressed the overarching question: What is the impact of mental health-related stigma on help-seeking for mental health problems? Subquestions were: (a) What is the size and direction of any association between stigma and help-seeking? (b) To what extent is stigma identified as a barrier to help-seeking? (c) What processes underlie the relationship between stigma and help-seeking? (d) Are there population groups for which stigma disproportionately deters help-seeking?
Five electronic databases were searched from 1980 to 2011 and references of reviews checked. A meta-synthesis of quantitative and qualitative studies, comprising three parallel narrative syntheses and subgroup analyses, was conducted.
The review identified 144 studies with 90,189 participants meeting inclusion criteria. The median association between stigma and help-seeking was d = - 0.27, with internalized and treatment stigma being most often associated with reduced help-seeking. Stigma was the fourth highest ranked barrier to help-seeking, with disclosure concerns the most commonly reported stigma barrier. A detailed conceptual model was derived that describes the processes contributing to, and counteracting, the deterrent effect of stigma on help-seeking. Ethnic minorities, youth, men and those in military and health professions were disproportionately deterred by stigma.
Stigma has a small- to moderate-sized negative effect on help-seeking. Review findings can be used to help inform the design of interventions to increase help-seeking.
IslandViewer (http://pathogenomics.sfu.ca/islandviewer) is a widely used web-based resource for the prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are ...clusters of genes of probable horizontal origin, and are of high interest since they disproportionately encode genes involved in medically and environmentally important adaptations, including antimicrobial resistance and virulence. We now report a major new release of IslandViewer, since the last release in 2013. IslandViewer 3 incorporates a completely new genome visualization tool, IslandPlot, enabling for the first time interactive genome analysis and gene search capabilities using synchronized circular, horizontal and vertical genome views. In addition, more curated virulence factors and antimicrobial resistance genes have been incorporated, and homologs of these genes identified in closely related genomes using strict filters. Pathogen-associated genes have been re-calculated for all pre-computed complete genomes. For user-uploaded genomes to be analysed, IslandViewer 3 can also now handle incomplete genomes, with an improved queuing system on compute nodes to handle user demand. Overall, IslandViewer 3 represents a significant new version of this GI analysis software, with features that may make it more broadly useful for general microbial genome analysis and visualization.
Genomic islands (GIs) are clusters of genes in prokaryotic genomes of probable horizontal origin. GIs are disproportionately associated with microbial adaptations of medical or environmental ...interest. Recently, multiple programs for automated detection of GIs have been developed that utilize sequence composition characteristics, such as G+C ratio and dinucleotide bias. To robustly evaluate the accuracy of such methods, we propose that a dataset of GIs be constructed using criteria that are independent of sequence composition-based analysis approaches.
We developed a comparative genomics approach (IslandPick) that identifies both very probable islands and non-island regions. The approach involves 1) flexible, automated selection of comparative genomes for each query genome, using a distance function that picks appropriate genomes for identification of GIs, 2) identification of regions unique to the query genome, compared with the chosen genomes (positive dataset) and 3) identification of regions conserved across all genomes (negative dataset). Using our constructed datasets, we investigated the accuracy of several sequence composition-based GI prediction tools.
Our results indicate that AlienHunter has the highest recall, but the lowest measured precision, while SIGI-HMM is the most precise method. SIGI-HMM and IslandPath/DIMOB have comparable overall highest accuracy. Our comparative genomics approach, IslandPick, was the most accurate, compared with a curated list of GIs, indicating that we have constructed suitable datasets. This represents the first evaluation, using diverse and, independent datasets that were not artificially constructed, of the accuracy of several sequence composition-based GI predictors. The caveats associated with this analysis and proposals for optimal island prediction are discussed.
The Red MSX Source (RMS) survey has identified a sample of ∼1200 massive young stellar objects (MYSOs), compact and ultra-compact H ii regions from a sample of ∼2000 MSX and Two Micron All Sky Survey ...(2MASS) colour-selected sources. We have used the 100-m Green Bank Telescope to search for 22-24 GHz water maser and ammonia (1,1), (2,2) and (3,3) emission towards ∼600 RMS sources located within the northern Galactic plane. We have identified 308 H2O masers which corresponds to an overall detection rate of ∼50 per cent. We find no significant difference in the detection rate for H ii regions and MYSOs which would suggest that the conditions required to produce maser emission are equally likely in both phases. Comparing the detection rates as a function of luminosity, we find the H2O detection rate has a positive dependence on the source luminosity, with the detection rate increasing with increasing luminosity.
We detect ammonia emission towards 479 of these massive young stars, which corresponds to ∼80 per cent. Ammonia is an excellent probe of high-density gas allowing us to measure key parameters such as gas temperatures, opacities and column densities, as well as providing an insight into the gas kinematics. The average kinetic temperature, full width at half-maximum linewidth and total NH3 column density for the sample are approximately 22 K, 2 km s−1 and 2 × 1015 cm−2, respectively. We find that the NH3 (1,1) linewidth and kinetic temperature are correlated with luminosity, and finding no underlying dependence of these parameters on the evolutionary phase of the embedded sources, we conclude that the observed trends in the derived parameters are more likely to be due to the energy output of the central source and/or the linewidth-clump mass relationship.
The velocities of the peak H2O masers and the NH3 emission are in excellent agreement with each other, which would strongly suggest an association between the dense gas and the maser emission. Moreover, we find the bolometric luminosity of the embedded source and the isotropic luminosity of the H2O maser are also correlated. We conclude from the correlations of the cloud and water maser velocities and the bolometric and maser luminosity that there is a strong dynamical relationship between the embedded young massive star and the H2O maser.
It has been noted that many bacterial virulence factor genes are located within genomic islands (GIs; clusters of genes in a prokaryotic genome of probable horizontal origin). However, such studies ...have been limited to single genera or isolated observations. We have performed the first large-scale analysis of multiple diverse pathogens to examine this association. We additionally identified genes found predominantly in pathogens, but not non-pathogens, across multiple genera using 631 complete bacterial genomes, and we identified common trends in virulence for genes in GIs. Furthermore, we examined the relationship between GIs and clustered regularly interspaced palindromic repeats (CRISPRs) proposed to confer resistance to phage.
We show quantitatively that GIs disproportionately contain more virulence factors than the rest of a given genome (p<1E-40 using three GI datasets) and that CRISPRs are also over-represented in GIs. Virulence factors in GIs and pathogen-associated virulence factors are enriched for proteins having more "offensive" functions, e.g. active invasion of the host, and are disproportionately components of type III/IV secretion systems or toxins. Numerous hypothetical pathogen-associated genes were identified, meriting further study.
This is the first systematic analysis across diverse genera indicating that virulence factors are disproportionately associated with GIs. "Offensive" virulence factors, as opposed to host-interaction factors, may more often be a recently acquired trait (on an evolutionary time scale detected by GI analysis). Newly identified pathogen-associated genes warrant further study. We discuss the implications of these results, which cement the significant role of GIs in the evolution of many pathogens.
In this paper we investigated the possibility of using the VisNIR spectra of dry ground soils to predict properties of soils scanned in their natural physical state with unknown water content. We ...regard this as an important nexus between large soil spectral libraries (based on dried and ground soil samples) and in-field use of VisNIR where variable soil moisture and secondary structure affect predictions of soil properties of interest. External parameter orthogonalization — EPO — was used in developing a partial least squares regression (PLSR) model to predict clay and organic C contents of soil samples with variable moisture contents. The Texas Soil Spectral Library based on spectra of dried and ground soil samples along with spectra of intact and dried and ground soil cores from Central Texas USA were used for EPO–PLSR model calibration and validation.
Using EPO, three matrices were developed to project VisNIR reflectance spectra to a subspace insensitive to soil moisture. The first matrix P1 was developed using spectra collected from rewetted samples that had been dried and ground. The second and third matrices, P2 and P3 were developed using spectra from soil core samples that were in their natural physical state when scanned and either field-moist (P2), or air-dried (P3). The results showed that EPO–PLSR successfully removed the effect of moisture without knowing the moisture at time of scanning and substantially improved the prediction of clay content compared to organic C content. For clay content, the validation results were as follows: No correction, R2=0.63, RMSEP=355gkg−1; P1 correction, R2=0.73, RMSEP=141gkg−1; and P2 correction, R2=0.77, RMSEP=90gkg−1. For organic C content, the validation statistics were: No correction, R2=0.49, RMSEP=9.4gkg−1; P1 correction, R2=0.51, RMSEP=7.5gkg−1; and P2 correction, R2=0.53, RMSEP=7.3gkg−1. Corrections of soil intactness alone had the following results for clay content: No correction RMSEP=125gkg−1 and P3 correction RMSEP=97gkg−1, and for organic C content: No correction RMSEP=7.5gkg−1, and P3 correction RMSEP=7.4gkg−1. Model results using the P2 matrix were consistently better than using P1. Particularly in predicting clay, P2 reduced the bias, non-unity, and lack of correlation, possibly because P2 accounted for the effect of natural aggregation of the soil in addition to soil moisture. Improvements for correction for intactness alone were small in clay and not significant in organic C content. We concluded that it is feasible to apply the EPO algorithm to employ VisNIR models from dried ground spectral libraries for prediction of soil properties based on field scans of soils in the natural physical state and at variable water contents.
•Lab-based VisNIR models can be used to predict properties of moist intact spectra.•The EPO algorithm removed the effect of soil moisture in VisNIR spectra.•Prior information on soil moisture is not needed for the EPO.•Removing the effect of moisture and intactness gave the best result.•The matrix used to remove the soil moisture effects was similar to another study.•The EPO correction has a more effect on the clay prediction than organic C.
Visible and near-infrared (VisNIR, 400–2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, inexpensive sensing method that has shown promise for lab-based soil characterization. However, ...little has been reported on how DRS will work in a field setting on intact soil cores. Seventy-two soil cores, representing 21 soil series and four parent materials, were extracted from six fields in Central Texas. Each soil core was scanned with a VisNIR spectrometer with a spectral range of 350–2500 nm in four combinations of moisture content and pre-treatment, including field-moist intact, air-dried intact, field-moist smeared intact, and air-dried ground. Visible near-infrared spectra were then used to predict soil organic and inorganic carbon (C) using partial least squares (PLS) regression. The PLS model was validated with 30% of the original soil cores that were randomly selected and withheld from the calibration model. The organic C validation had a root mean squared deviation (RMSD) of 5.4 g kg
−
1
and 4.1 g kg
−
1
for the field-moist and air-dried intact scans, respectively. The RMSD values for inorganic C were 8.7 g kg
−
1
and 7.8 g kg
−
1
for the field-moist and air-dried intact scans, respectively. Smearing the samples had minimal effects on prediction accuracies for organic and inorganic C. Variable soil moisture did reduce prediction accuracies. Soil color, pH, and soil reaction to 1 N HCL were added as auxiliary predictors. Soil color improved organic C predictions by 0.2 to 0.4 g kg
−
1
. The field-moist intact inorganic C model improved with soil color (RMSD
=
8.0 g kg
−
1
), soil pH (8.3 g kg
−
1
), and soil reaction to HCl (6.5 g kg
−
1
). These results show that in situ spectroscopy can measure organic and inorganic C with some loss of accuracy compared to dried ground samples. In inorganic C predictions, an easy-to-measure auxiliary variable, like soil reaction with 1 N HCl, can improve in situ predictions compared to dried ground predictions.
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