Effectiveness of R1-nj Anthocyanin Marker in the Identification of In Vivo Induced Maize Haploid Embryos.

Doubled haploid (DH) technology has become integral to maize breeding programs to expedite inbred line development and increase the efficiency of breeding operations. Unlike many other plant species that use in vitro methods, DH production in maize uses a relatively simple and efficient in vivo haploid induction method. However, it takes two complete crop cycles for DH line generation, one for haploid induction and the other one for chromosome doubling and seed production. Rescuing in vivo induced haploid embryos has the potential to reduce the time for DH line development and improve the efficiency of DH line production. However, the identification of a few haploid embryos (~10%) resulting from an induction cross from the rest of the diploid embryos is a challenge. In this study, we demonstrated that an anthocyanin marker, namely R1-nj, which is integrated into most haploid inducers, can aid in distinguishing haploid and diploid embryos. Further, we tested conditions that enhance R1-nj anthocyanin marker expression in embryos and found that light and sucrose enhance anthocyanin expression, while phosphorous deprivation in the media had no affect. Validating the use of the R1-nj marker for haploid and diploid embryo identification using a gold standard classification based on visual differences among haploids and diploids for characteristics such as seedling vigor, erectness of leaves, tassel fertility, etc., indicated that the R1-nj marker could lead to significantly high false positives, necessitating the use of additional markers for increased accuracy and reliability of haploid embryo identification.

Low Cycle Threshold Value in Xpert MTB/RIF Assay May Herald False Detection of Tuberculosis and Rifampicin Resistance: A Study of Two Cases.

We report 2 cases for whom Xpert MTB/RIF falsely signaled rifampicin-resistant tuberculosis, based on unusually low cycle threshold and 3 of 5 probes missing. Other mycobacterial tests were negative. Further optimization of the Xpert MTB/RIF algorithm is warranted.

Unravelling the myths of R 0 in controlling the dynamics of COVID-19 outbreak: A modelling perspective.

COVID-19 is an emerging and rapidly evolving pandemic around the world, which causes severe acute respiratory syndrome and results in substantial morbidity and mortality. To examine the transmission dynamics of COVID-19, we investigate the spread of this pandemic using Malaysia as a case study and scrutinise its interactions with some exogenous factors such as limited medical resources and false detection problems. To do this, we employ a simple epidemiological model and analyse this system using modelling and dynamical systems techniques. We discover some contrasting findings with respect to the observations of basic reproduction number: while it is observed that R 0 seems to provide a good description of transmission dynamics in simple outbreak scenarios, this quantity might mislead the assessment on the severity of pandemic when certain complexities such as limited medical resources and false detection problems are incorporated into the model. In particular, we observe the possibility of a COVID-19 outbreak through bistable behaviour, even when the basic reproduction number is less than unity. Based on these findings, we caution policy makers not to make their decisions solely based on the guidance of the basic reproduction number only, which clearly could cause trouble.

Identifying error and accurately interpreting environmental DNA metabarcoding results: A case study to detect vertebrates at arid zone waterholes.

Environmental DNA (eDNA) metabarcoding surveys enable rapid, noninvasive identification of taxa from trace samples with wide-ranging applications from characterizing local biodiversity to identifying food-web interactions. However, the technique is prone to error from two major sources: (a) contamination through foreign DNA entering the workflow, and (b) misidentification of DNA within the workflow. Both types of error have the potential to obscure true taxon presence or to increase taxonomic richness by incorrectly identifying taxa as present at sample sites, but multiple error sources can remain unaccounted for in metabarcoding studies. Here, we use data from an eDNA metabarcoding study designed to detect vertebrate species at waterholes in Australia's arid zone to illustrate where and how in the workflow errors can arise, and how to mitigate those errors. We detected the DNA of 36 taxa spanning 34 families, 19 orders and five vertebrate classes in water samples from waterholes, demonstrating the potential for eDNA metabarcoding surveys to provide rapid, noninvasive detection in remote locations, and to widely sample taxonomic diversity from aquatic through to terrestrial taxa. However, we initially identified 152 taxa in the samples, meaning there were many false positive detections. We identified the sources of these errors, allowing us to design a stepwise process to detect and remove error, and provide a template to minimize similar errors that are likely to arise in other metabarcoding studies. Our findings suggest eDNA metabarcoding surveys need to be carefully conducted and screened for errors to ensure their accuracy.

Dual-NMS: A Method for Autonomously Removing False Detection Boxes from Aerial Image Object Detection Results.

In the field of aerial image object detection based on deep learning, it's difficult to extract features because the images are obtained from a top-down perspective. Therefore, there are numerous false detection boxes. The existing post-processing methods mainly remove overlapped detection boxes, but it's hard to eliminate false detection boxes. The proposed dual non-maximum suppression (dual-NMS) combines the density of detection boxes that are generated for each detected object with the corresponding classification confidence to autonomously remove the false detection boxes. With the dual-NMS as a post-processing method, the precision is greatly improved under the premise of keeping recall unchanged. In vehicle detection in aerial imagery (VEDAI) and dataset for object detection in aerial images (DOTA) datasets, the removal rate of false detection boxes is over 50%. Additionally, according to the characteristics of aerial images, the correlation calculation layer for feature channel separation and the dilated convolution guidance structure are proposed to enhance the feature extraction ability of the network, and these structures constitute the correlation network (CorrNet). Compared with you only look once (YOLOv3), the mean average precision (mAP) of the CorrNet for DOTA increased by 9.78%. Commingled with dual-NMS, the detection effect in aerial images is significantly improved.

ICR142 Benchmarker: evaluating, optimising and benchmarking variant calling performance using the ICR142 NGS validation series.

Evaluating, optimising and benchmarking of next generation sequencing (NGS) variant calling performance are essential requirements for clinical, commercial and academic NGS pipelines. Such assessments should be performed in a consistent, transparent and reproducible fashion, using independently, orthogonally generated data. Here we present ICR142 Benchmarker, a tool to generate outputs for assessing germline base substitution and indel calling performance using the ICR142 NGS validation series, a dataset of Illumina platform-based exome sequence data from 142 samples together with Sanger sequence data at 704 sites. ICR142 Benchmarker provides summary and detailed information on the sensitivity, specificity and false detection rates of variant callers. ICR142 Benchmarker also automatically generates a single page report highlighting key performance metrics and how performance compares to widely-used open-source tools. We used ICR142 Benchmarker with VCF files outputted by GATK, OpEx and DeepVariant to create a benchmark for variant calling performance. This evaluation revealed pipeline-specific differences and shared challenges in variant calling, for example in detecting indels in short repeating sequence motifs. We next used ICR142 Benchmarker to perform regression testing with DeepVariant versions 0.5.2 and 0.6.1. This showed that v0.6.1 improves variant calling performance, but there was evidence of minor changes in indel calling behaviour that may benefit from attention. The data also allowed us to evaluate filters to optimise DeepVariant calling, and we recommend using 30 as the QUAL threshold for base substitution calls when using DeepVariant v0.6.1. Finally, we used ICR142 Benchmarker with VCF files from two commercial variant calling providers to facilitate optimisation of their in-house pipelines and to provide transparent benchmarking of their performance. ICR142 Benchmarker consistently and transparently analyses variant calling performance based on the ICR142 NGS validation series, using the standard VCF input and outputting informative metrics to enable user understanding of pipeline performance. ICR142 Benchmarker is freely available at https://github.com/RahmanTeamDevelopment/ICR142_Benchmarker/releases.

Adjustable Method for Real-Time Gait Pattern Detection Based on Ground Reaction Forces Using Force Sensitive Resistors and Statistical Analysis of Constant False Alarm Rate.

A new approach is proposed to detect the real-time gait patterns adaptively through measuring the ground contact forces (GCFs) by force sensitive resistors (FSRs). Published threshold-based methods detect the gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. To overcome these drawbacks, this study utilized the constant false alarm rate (CFAR) to analyze the characteristics of GCF signals. Specifically, a sliding window detector is built to record the lasting time of the curvature of the GCF signals and one complete gait cycle could be divided into three areas, such as continuous ascending area, continuous descending area and unstable area. Then, the GCF values in the unstable area are used to compute a threshold through the CFAR. Finally, the new gait pattern detection rules are proposed which include the results of the sliding window detector and the division results through the computed threshold. To verify this idea, a data acquisition board is designed to collect the GCF data from able-bodied subjects. Meanwhile, in order to test the reliability of the proposed method, five threshold-based methods in the literature are introduced as reference methods and the reliability is validated by comparing the detection results of the proposed method with those of the reference methods. Experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively and obtain high reliabilities compared with the reference methods.

Phase singularity detection through phase map interpolation: Theory, advantages and limitations.

BACKGROUND: During a cardiac arrhythmia, reentrant waves rotate around critical points called phase singularities (PS). PS detection is relevant to the quantitative description of the dynamics and to the identification of potential targets for ablation. Phase interpolation techniques have been proposed to increase the accuracy of PS localization. Our aim is to provide a theoretical basis and a comparative analysis of these methods. METHOD: Different electrode configurations representing mapping systems or catheter multi-electrode arrays were considered: triangular mesh, regular square grid and circular arrays. Linear, spline and inverse squared distance interpolation were used to create a continuous map from discrete measurements of phase. Synthetic phase maps with a PS and background noise were generated. Monte-Carlo simulations were run over millions of realizations to estimate PS locations and calculate the false negative and false positive rates as a function of noise variance. RESULTS: Linear interpolation is shown to be exactly equivalent to the standard discrete approach without interpolation. Spline interpolation had lower false negative rate at the expense of a higher false positive rate in the presence of noise. Inverse squared distance interpolation reduced false positives and was more robust to noise but was more likely to fail to detect a PS. Phase interpolation decreased PS localization error down to 0.17 interelectrode distance. The error was largest when the PS was near an electrode. CONCLUSION: Phase interpolation methods offer additional flexibility to find the adequate trade-off between reducing false positives and false negatives.

Neural responses to emotional involuntary memories in posttraumatic stress disorder: Differences in timing and activity.

Background: Involuntary memories are a hallmark symptom of posttraumatic stress disorder (PTSD), but studies of the neural basis of involuntary memory retrieval in posttraumatic stress disorder (PTSD) are sparse. The study of the neural correlates of involuntary memories of stressful events in PTSD focuses on the voluntary retrieval of memories that are sometimes recalled as intrusive involuntary memories, not on involuntary retrieval while being scanned. Involuntary memory retrieval in controls has been shown to elicit activity in the parahippocampal gyrus, precuneus, inferior parietal cortex, and posterior midline regions. However, it is unknown whether involuntary memories are supported by the same mechanisms in PTSD. Because previous work has shown that both behavioral and neural responsivity is slowed in PTSD, we examined the spatiotemporal dynamics of the neural activity underlying negative and neutral involuntary memory retrieval. Methods: Twenty-one individuals with PTSD and 21 non-PTSD, trauma-exposed controls performed an involuntary memory task, while undergoing a functional magnetic resonance imaging scan. Environmental sounds served as cues for well-associated pictures of negative and neutral scenes. We used a finite impulse response model to analyze temporal differences between groups in neural responses. Results: Compared with controls, participants with PTSD reported more involuntary memories, which were more emotional and more vivid, but which activated a similar network of regions. However, compared to controls, individuals with PTSD showed delayed neural responsivity in this network and increased vmPFC/ACC activity for negative > neutral stimuli. Conclusions: The similarity between PTSD and controls in neural substrates underlying involuntary memories suggests that, unlike voluntary memories, involuntary memories elicit similar activity in regions critical for memory retrieval. Further, the delayed neural responsivity for involuntary memories in PTSD suggests that factors affecting cognition in PTSD, like increased fatigue, or avoidance behaviors could do so by delaying activity in regions necessary for cognitive processing. Finally, compared to neutral memories, negative involuntary memories elicit hyperactivity in the vmPFC, whereas the vmPFC is typically shown to be hypoactive in PTSD during voluntary memory retrieval. These patterns suggest that considering both the temporal dynamics of cognitive processes as well as involuntary cognitive processes would improve existing neurobiological models of PTSD.

The multiple-comparison trap and the Raven's paradox-perils of using null hypothesis testing in environmental assessment.

Detecting and quantifying environmental thresholds is frequently an important step in understanding ecological responses to environmental stressors. We discuss two statistical issues often encountered in threshold detection and quantification when statistical null hypothesis testing is used as a main analytical tool. The hidden multiple-comparison trap (leading to a much higher risk of a false detection) and Raven's paradox (rendering a "detection" meaningless) are often obscured when statistical hypothesis testing is used as part of a more elaborate model, especially models based on computer-intensive methods. Using two examples, we show that the hidden multiple-comparison trap can be exposed using computer simulation to estimate the probability of making a false detection; Raven's paradox can be avoided by clearly stating the null and alternative hypotheses using scientific terms to substantiate that the rejection of the null is equivalent to proving that the alternative of interest is true. The hidden multiple-comparison trap implies that a null hypothesis testing based on a computer-intensive method should be used with caution. The implication of Raven's paradox requires that we focus on providing evidence supporting the proposed hypothesis or model, rather than seeking evidence against the frequently irrelevant null hypothesis. These two problems, and many others related to null hypothesis testing, suggest that statistical hypothesis testing should be used only as a component of the body of evidence, perhaps, as the devil's advocate.

Two stage algorithm vs commonly used approaches for the suspect screening of complex environmental samples analyzed via liquid chromatography high resolution time of flight mass spectroscopy: A test study.

LC-HR-QTOF-MS recently has become a commonly used approach for the analysis of complex samples. However, identification of small organic molecules in complex samples with the highest level of confidence is a challenging task. Here we report on the implementation of a two stage algorithm for LC-HR-QTOF-MS datasets. We compared the performances of the two stage algorithm, implemented via NIVA_MZ_Analyzer™, with two commonly used approaches (i.e. feature detection and XIC peak picking, implemented via UNIFI by Waters and TASQ by Bruker, respectively) for the suspect analysis of four influent wastewater samples. We first evaluated the cross platform compatibility of LC-HR-QTOF-MS datasets generated via instruments from two different manufacturers (i.e. Waters and Bruker). Our data showed that with an appropriate spectral weighting function the spectra recorded by the two tested instruments are comparable for our analytes. As a consequence, we were able to perform full spectral comparison between the data generated via the two studied instruments. Four extracts of wastewater influent were analyzed for 89 analytes, thus 356 detection cases. The analytes were divided into 158 detection cases of artificial suspect analytes (i.e. verified by target analysis) and 198 true suspects. The two stage algorithm resulted in a zero rate of false positive detection, based on the artificial suspect analytes while producing a rate of false negative detection of 0.12. For the conventional approaches, the rates of false positive detection varied between 0.06 for UNIFI and 0.15 for TASQ. The rates of false negative detection for these methods ranged between 0.07 for TASQ and 0.09 for UNIFI. The effect of background signal complexity on the two stage algorithm was evaluated through the generation of a synthetic signal. We further discuss the boundaries of applicability of the two stage algorithm. The importance of background knowledge and experience in evaluating the reliability of results during the suspect screening was evaluated.

Neurophysiology of juvenile myoclonic epilepsy: EEG-based network and graph analysis of the interictal and immediate preictal states.

INTRODUCTION: The neuronal mechanisms of enduring seizure propensity and seizure precipitation in juvenile myoclonic epilepsy (JME) are not known. We investigated these issues, within the framework of the "network concept" of epilepsy. METHODS: Design1: 19, unmedicated JME patients were compared with nineteen, age-, and sex-matched normal control persons (NC). A total of 120s, artifact-free, paroxysm-free, eyes-closed, resting state EEG background activity was analyzed for each person. Design2: interictal and immediate preictal periods of the JME patients were compared in order to explore interictal-preictal network differences. For both comparison designs, statistically significant differences of EEG functional connectivity (EEGfC), nodal and global graph parameters were evaluated. MAIN RESULTS: Design1: maximum abnormalities were: increased delta, theta, alpha1 EEGfC and decreased alpha2 and beta EEGfC in the JME group as compared to the NC group, mainly among cortical areas that are involved in sensory-motor integration. Nodal degree and efficiency of three, medial, basal frontal nodes were greater in JME than in NC, in the alpha1 band. Design2: preictal delta EEGfC showed further increase in the above-mentioned areas, as compared to the interictal state. DISCUSSION: Increased EEGfC indicates a hypercoupled state among the specified cortical areas. This interictal abnormality further increases in the preictal state. Nodal graph statistics indicates abnormal neuronal dynamics in the cortical area that is the ictal onset zone in JME. SIGNIFICANCE: Interictal and preictal neuronal dysfunction has been described in terms of network dynamics and topography in JME patients. Forthcoming investigations of seizure precipitation and therapeutic drug effects are encouraged on this basis.

Glyphosate effects on the gene expression of the apical bud in soybean (Glycine max).

Glyphosate is a broad spectrum, non-selective herbicide which has been widely used for weed control. Much work has focused on elucidating the high accumulation of glyphosate in shoot apical bud (shoot apex). However, to date little is known about the molecular mechanisms of the sensitivity of shoot apical bud to glyphosate. Global gene expression profiling of the soybean apical bud response to glyphosate treatment was performed in this study. The results revealed that the glyphosate inhibited tryptophan biosynthesis of the shikimic acid pathway in the soybean apical bud, which was the target site of glyphosate. Glyphosate inhibited the expression of most of the target herbicide site genes. The promoter sequence analysis of key target genes revealed that light responsive elements were important regulators in glyphosate induction. These results will facilitate further studies of cloning genes and molecular mechanisms of glyphosate on soybean shoot apical bud.

Faces in the mist: illusory face and letter detection.

We report three behavioral experiments on the spatial characteristics evoking illusory face and letter detection. False detections made to pure noise images were analyzed using a modified reverse correlation method in which hundreds of observers rated a modest number of noise images (480) during a single session. This method was originally developed for brain imaging research, and has been used in a number of fMRI publications, but this is the first report of the behavioral classification images. In Experiment 1 illusory face detection occurred in response to scattered dark patches throughout the images, with a bias to the left visual field. This occurred despite the use of a fixation cross and expectations that faces would be centered. In contrast, illusory letter detection (Experiment 2) occurred in response to centrally positioned dark patches. Experiment 3 included an oval in all displays to spatially constrain illusory face detection. With the addition of this oval the classification image revealed an eyes/nose/mouth pattern. These results suggest that face detection is triggered by a minimal face-like pattern even when these features are not centered in visual focus.