DeepWL: Robust EPID based Winston-Lutz analysis using deep learning,synthetic image generation and optical path-tracing

Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localises the radiation isocentre of the linac.In the current work we demonstrate a novel method of analysing EPID based Winston-Lutz QA images using a deep learning model trained only on synthetic image data. In addition, we propose a novel method of generating the synthetic WL images and associated ‘ground-truth’ masks using an optical path-tracing engine to ‘fake’ mega-voltage EPID images.The model called DeepWL was trained on 1500 synthetic WL images using data augmentation techniques for 180 epochs. The model was built using Keras with a TensorFlow backend on an Intel Core i5-6500T CPU and trained in approximately 15 h. DeepWL was shown to produce ball bearing and multi-leaf collimator field segmentations with a mean dice coefficient of 0.964 and 0.994 respectively on previously unseen synthetic testing data. When DeepWL was applied to WL data measured on an EPID, the predicted mean displacements were shown to be statistically similar to the Canny Edge detection method. However, the DeepWL predictions for the ball bearing locations were shown to correlate better with manual annotations compared with the Canny edge detection algorithm.DeepWL was demonstrated to analyse Winston-Lutz images with an accuracy suitable for routine linac quality assurance with some statistical evidence that it may outperform Canny Edge detection methods in terms of segmentation robustness and the resultant displacement predictions.     

AnimalFinder: A semi-automated system for animal detection in time-lapse camera trap images

Although the use of camera traps in wildlife management is well established, technologies to automate image processing have been much slower in development, despite their potential to drastically reduce personnel time and cost required to review photos. We developed AnimalFinder in MATLAB® to identify animal presence in time-lapse camera trap images by comparing individual photos to all images contained within the subset of images (i.e. photos from the same survey and site), with some manual processing required to remove false positives and collect other relevant data (species, sex, etc.). We tested AnimalFinder on a set of camera trap images and compared the presence/absence results with manual-only review with white-tailed deer (Odocoileus virginianus), wild pigs (Sus scrofa), and raccoons (Procyon lotor). We compared abundance estimates, model rankings, and coefficient estimates of detection and abundance for white-tailed deer using N-mixture models. AnimalFinder performance varied depending on a threshold value that affects program sensitivity to frequently occurring pixels in a series of images. Higher threshold values led to fewer false negatives (missed deer images) but increased manual processing time, but even at the highest threshold value, the program reduced the images requiring manual review by ~ 40% and correctly identified > 90% of deer, raccoon, and wild pig images. Estimates of white-tailed deer were similar between AnimalFinder and the manual-only method (~ 1–2 deer difference, depending on the model), as were model rankings and coefficient estimates. Our results show that the program significantly reduced data processing time and may increase efficiency of camera trapping surveys.     

Alien Plant Monitoring with Ultralight Airborne Imaging Spectroscopy

Effective management of invasive plants requires a precise determination of their distribution. Remote sensing techniques constitute a promising alternative to field surveys and hyperspectral sensors (also known as imaging spectrometers, with a large number of spectral bands and high spectral resolution) are especially suitable when very similar categories are to be distinguished (e.g. plant species). A main priority in the development of this technology is to lower its cost and simplify its use, so that its demonstrated aptitude for many environmental applications can be truly realized. With this aim, we have developed a system for hyperspectral imaging (200 spectral bands in the 380–1000 nm range and circa 3 nm spectral resolution) operated on board ultralight aircraft (namely a gyrocopter), which allows a drastic reduction of the running costs and operational complexity of image acquisition, and also increases the spatial resolution of the images (circa 5–8 pixels/m² at circa 65 km/h and 300 m height). The detection system proved useful for the species tested (Acacia melanoxylon, Oxalis pes-caprae, and Carpobrotus aff. edulis and acinaciformis), with user’s and producer’s accuracy always exceeding 90%. The detection accuracy reported corresponds to patches down to 0.125 m² (50% of pixels 0.5×0.5 m in size), a very small size for many plant species, making it very effective for initial stages of invasive plant spread. In addition, its low operating costs, similar to those of a 4WD ground vehicle, facilitate frequent image acquisition. Acquired images constitute a permanent record of the status of the study area, with great amount of information that can be analyzed in the future for other purposes, thus greatly facilitating the monitoring of natural areas at detailed spatial and temporal scales for improved management.     

Functional Analyses of LONELY GUY Cytokinin-Activating Enzymes Reveal the Importance of the Direct Activation Pathway in Arabidopsis

Cytokinins play crucial roles in diverse aspects of plant growth and development. Spatiotemporal distribution of bioactive cytokinins is finely regulated by metabolic enzymes. LONELY GUY (LOG) was previously identified as a cytokinin-activating enzyme that works in the direct activation pathway in rice (Oryza sativa) shoot meristems. In this work, nine Arabidopsis thaliana LOG genes (At LOG1 to LOG9) were predicted as homologs of rice LOG. Seven At LOGs, which are localized in the cytosol and nuclei, had enzymatic activities equivalent to that of rice LOG. Conditional overexpression of At LOGs in transgenic Arabidopsis reduced the content of N⁶-(Δ²-isopentenyl)adenine (iP) riboside 5′-phosphates and increased the levels of iP and the glucosides. Multiple mutants of At LOGs showed a lower sensitivity to iP riboside in terms of lateral root formation and altered root and shoot morphology. Analyses of At LOG promoter:β-glucuronidase fusion genes revealed differential expression of LOGs in various tissues during plant development. Ectopic overexpression showed pleiotropic phenotypes, such as promotion of cell division in embryos and leaf vascular tissues, reduced apical dominance, and a delay of leaf senescence. Our results strongly suggest that the direct activation pathway via LOGs plays a pivotal role in regulating cytokinin activity during normal growth and development in Arabidopsis.     

MtreeRing: An R package with graphical user interface for automatic measurement of tree ring widths using image processing techniques

Accurate measurements of ring-width series are essential for dendrochronological analyses. We present an R package MtreeRing for ring-width measurements on scanned digital images. A morphological alternate sequential filter is used for noise reduction in the original image. Ring boundaries are determined by the steepest negative slopes in the light reflectance of latewood-earlywood transitions. To automatically identify tree rings, the package provides three alternative methods (watershed-based segmentation, Canny edge detector, and a linear detection algorithm), each with advantages and disadvantages and suited to different wood anatomical features. The user can also manually mark tree rings on species with complex anatomical structures. The arcs of inner-rings and angles of successive inclined ring boundaries are used to correct ring-width series. Differences in ring-width measurements between MtreeRing and WinDENDRO in a given coniferous species (Larix gmelinii) were assessed, and no significant difference between programs was found. Furthermore, the package provides an R-based web application which was developed using the Shiny framework. This beginner-friendly application allows viewing and interacting with tree ring images. It requires no programming experience and can run on either a local computer or a remote server.     

Computer-Assisted Photo Identification Outperforms Visible Implant Elastomers in an Endangered Salamander,Eurycea tonkawae

Despite recognition that nearly one-third of the 6300 amphibian species are threatened with extinction, our understanding of the general ecology and population status of many amphibians is relatively poor. A widely-used method for monitoring amphibians involves injecting captured individuals with unique combinations of colored visible implant elastomer (VIE). We compared VIE identification to a less-invasive method – computer-assisted photographic identification (photoID) – in endangered Jollyville Plateau salamanders (Eurycea tonkawae), a species with a known range limited to eight stream drainages in central Texas. We based photoID on the unique pigmentation patterns on the dorsal head region of 1215 individual salamanders using identification software Wild-ID. We compared the performance of photoID methods to VIEs using both ‘high-quality’ and ‘low-quality’ images, which were taken using two different camera types and technologies. For high-quality images, the photoID method had a false rejection rate of 0.76% compared to 1.90% for VIEs. Using a comparable dataset of lower-quality images, the false rejection rate was much higher (15.9%). Photo matching scores were negatively correlated with time between captures, suggesting that evolving natural marks could increase misidentification rates in longer term capture-recapture studies. Our study demonstrates the utility of large-scale capture-recapture using photo identification methods for Eurycea and other species with stable natural marks that can be reliably photographed.     

Functional conservation between mammalian MGRN1 and plant LOG2 ubiquitin ligases

Plant LOSS OF GDU 2 (LOG2) and Mammalian Mahogunin Ring Finger 1 (MGRN1) proteins are RING-type E3 ligases sharing similarity N-terminal to the RING domain. Deletion of this region disrupts the interaction of LOG2 with the plant membrane protein GLUTAMINE DUMPER1 (GDU1). Phylogenetic analysis identified two clades of LOG2/MGRN1-like proteins in vertebrates and plants. The ability of MGRN1 to functionally replace LOG2 was tested. MGRN1 ubiquitylates GDU1 in vitro and can partially substitute for LOG2 in the plant, partially restoring amino acid resistance to a GDU1-myc over-expression, log2-2 background. Altogether, these results suggest a conserved function for the N-terminal domain in evolution.     

Count Bayesian models for genetic analysis of in vitro embryo production traits in Guzerá cattle

Four models for in vitro embryo production traits in Guzerá cattle were compared: Gaussian (untransformed variable – LIN and transformed in logarithmic scale – LOG), Poisson (POI) and zero-inflated Poisson (ZIP). Data consisted of 5716 ovum pick-up and in vitro fertilization records performed in 1205 cows from distinct regions of Brazil. Analyzed count traits were the number of viable oocytes (N_OV), number of grade I oocytes (N_GI), number of degenerated oocytes (N_DG), number of cleaved embryos (N_CLV) and number of viable produced embryos (N_EMB). Heritability varied from 0.17 (LIN) to 0.25 (POI) for N_OV; 0.08 (LOG) to 0.18 (ZIP) for N_GI; 0.12 (LIN) to 0.20 (POI) for N_DG; 0.13 (LIN) to 0.19 (POI) for N_CLV; 0.10 (LIN) to 0.20 (POI) for N_EMB depending on the considered model. The estimated repeatability varied from 0.53 (LOG) to 0.63 (POI) for N_OV; 0.22 (LOG) to 0.39 (ZIP) for N_GI; 0.29 (LIN) to 0.42 (ZIP) for N_DG; 0.42 (LIN) to 0.59 (POI) for N_CLV; 0.36 (LIN) to 0.51 (POI) for N_EMB. The goodness of fit, measured by deviance information criterion and mean squared residuals, suggested superiority of POI and ZIP over Gaussian models. Estimated breeding values (EBV) obtained by different models were highly correlated, varying from 0.92 for N_OV (between LIN-POI) and 0.99 for N_GI (between POI-ZIP). The number of coincident animals on the 10% top EBV showed lower similarities. We recommend POI and ZIP models as the most adequate for genetic analysis of in vitro embryo production traits in Guzerá cattle.     

Detecting Changes in Retinal Function: Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS)

Visual fields measured with standard automated perimetry are a benchmark test for determining retinal function in ocular pathologies such as glaucoma. Their monitoring over time is crucial in detecting change in disease course and, therefore, in prompting clinical intervention and defining endpoints in clinical trials of new therapies. However, conventional change detection methods do not take into account non-stationary measurement variability or spatial correlation present in these measures. An inferential statistical model, denoted ‘Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement’ (ANSWERS), was proposed. In contrast to commonly used ordinary linear regression models, which assume normally distributed errors, ANSWERS incorporates non-stationary variability modelled as a mixture of Weibull distributions. Spatial correlation of measurements was also included into the model using a Bayesian framework. It was evaluated using a large dataset of visual field measurements acquired from electronic health records, and was compared with other widely used methods for detecting deterioration in retinal function. ANSWERS was able to detect deterioration significantly earlier than conventional methods, at matched false positive rates. Statistical sensitivity in detecting deterioration was also significantly better, especially in short time series. Furthermore, the spatial correlation utilised in ANSWERS was shown to improve the ability to detect deterioration, compared to equivalent models without spatial correlation, especially in short follow-up series. ANSWERS is a new efficient method for detecting changes in retinal function. It allows for better detection of change, more efficient endpoints and can potentially shorten the time in clinical trials for new therapies.     

Adaptive linear feature-reuse network for rapid forest fire smoke detection model

Current mainstream smoke detection methods have some problems, such as missing report, large deviation of detection box and slow detection speed. Therefore, we propose an Adaptive Linear Feature-Reuse Network for Rapid Forest Fire Smoke Detection (ALFRNet). Firstly, we designed Double Linear Feature-Reuse Module(DLFR Module) to reduce information loss in the process of the acquisition of smoke images；and Hybrid Attention-Guided Module (HAG Module) was proposed to reduce the interference caused by blurred image and to emphasize the expression of smoke characteristics. At the convolutional layer of the proposed network, a novel Adaptive Depthwise Convolution Module (ADC Module) which can effectively solve the problem of difficulty in recognition caused by too small smoke targets in images was used. Besides, we adopt Cluster NMS (CNMS) in purpose of avoiding large deviation of the detection box. It can adapt to smoke target with fuzzy edge and improve the performance of detection. Finally, we build a smoke detection system based on the Internet of things. The experimental results show that our method achieves 87.26% mAP₅₀ at 43 FPS on NVIDIA TITAN Xp. Compared with other mainstream methods, it has the better performance of quicker speed, higher accuracy and more accurate position of detection box.     

From microscopy data to in silico environments for in vivo-oriented simulations

In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction method of realistic simulation spaces based on our TEM images. Interactive raytracing visualization of this space allows the perception of the overall 3D structure, which is not directly accessible from 2D TEM images. Simulation results show that the diffusion in such generated structures strongly depends on image post-processing. Frayed structures corresponding to noisy images hinder the diffusion much stronger than smooth surfaces from denoised images. This means that the correct identification of noise or structure is significant to reconstruct appropriate reaction environment in silico in order to estimate realistic behaviors of reactants in vivo. Static structures lead to anomalous diffusion due to the partial confinement. In contrast, mobile crowding agents do not lead to anomalous diffusion at moderate crowding levels. By varying the mobility of these non-reactive obstacles (NRO), we estimated the relationship between NRO diffusion coefficient (D_nro) and the anomaly in the tracer diffusion (α). For D_nro=21.96 to 44.49 μm²/s, the simulation results match the anomaly obtained from FCS measurements. This range of the diffusion coefficient from simulations is compatible with the range of the diffusion coefficient of structural proteins in the cytoplasm. In addition, we investigated the relationship between the radius of NRO and anomalous diffusion coefficient of tracers by the comparison between different simulations. The radius of NRO has to be 58 nm when the polymer moves with the same diffusion speed as a reactant, which is close to the radius of functional protein complexes in a cell.     

冷冻电镜单颗粒三维重构密度掩模的自动生成方法研究

冷冻电镜单颗粒三维重构技术是用来解析生物大分子三维结构的常用方法.然而目前在单颗粒三维重构过程中,溶剂平滑操作还存在一定缺陷:没有一款主流的单颗粒三维重构程序能够自动寻找掩模(mask)三维密度图,使得三维重构过程难免受到噪音统计学模型计算偏差的干扰.为解决这一问题,本研究借鉴X射线晶体学中解析优化相位所广泛采用的溶剂平滑方法,采用高斯滤波、坎尼边缘检测、最小误差阈值处理等方法处理重构所得三维密度图,优化溶剂平滑操作,发展在单颗粒三维重构过程中自动寻找mask三维密度图的方法.运用三维密度图傅里叶壳层相关系数(fourier shell correlation,FSC)曲线图、模拟颗粒数据重构角度误差散点图等指标评估此方法的效果.结果表明,自动寻找mask密度图的方法能够较好地找到涵盖分子结构信号区域的mask密度图,较为明显提高三维重构所得密度图分辨率.     

Effect of Angle on Flow-Induced Vibrations of Pinniped Vibrissae

Two types of vibrissal surface structures, undulated and smooth, exist among pinnipeds. Most Phocidae have vibrissae with undulated surfaces, while Otariidae, Odobenidae, and a few phocid species possess vibrissae with smooth surfaces. Variations in cross-sectional profile and orientation of the vibrissae also exist between pinniped species. These factors may influence the way that the vibrissae behave when exposed to water flow. This study investigated the effect that vibrissal surface structure and orientation have on flow-induced vibrations of pinniped vibrissae. Laser vibrometry was used to record vibrations along the whisker shaft from the undulated vibrissae of harbor seals (Phoca vitulina) and northern elephant seals (Mirounga angustirostris) and the smooth vibrissae of California sea lions (Zalophus californianus). Vibrations along the whisker shaft were measured in a flume tank, at three orientations (0°, 45°, 90°) to the water flow. The results show that vibration frequency and velocity ranges were similar for both undulated and smooth vibrissae. Angle of orientation, rather than surface structure, had the greatest effect on flow-induced vibrations. Vibration velocity was up to 60 times higher when the wide, flat aspect of the whisker faced into the flow (90°), compared to when the thin edge faced into the flow (0°). Vibration frequency was also dependent on angle of orientation. Peak frequencies were measured up to 270 Hz and were highest at the 0° orientation for all whiskers. Furthermore, CT scanning was used to quantify the three-dimensional structure of pinniped vibrissae that may influence flow interactions. The CT data provide evidence that all vibrissae are flattened in cross-section to some extent and that differences exist in the orientation of this profile with respect to the major curvature of the hair shaft. These data support the hypothesis that a compressed cross-sectional profile may play a key role in reducing self-noise of the vibrissae.     

Detection method for Convallaria keiskei colonies in Hokkaido,Japan, by combining CNN and FCM using UAV-based remote sensing data

The Convallaria keiskei, a plant species indigenous to Japan, is on the verge of extinction. In the past, they have been manually protected and managed. Unmanned aerial vehicles (UAVs), which are already being applied in various fields, such as agriculture, surveying, and logistics, can be applied to automate this task. Image processing and machine learning techniques applied on images obtained from UAVs can automate Convallaria keiskei classification, help to estimate the increase in colony numbers, and reduce the detection cost. In a previous study, a flower number estimation method that combines image processing and a convolutional neural network (CNN) was proposed. However, leaf regions similar to flower regions were misidentified as flower regions, and the accuracy was reduced. Therefore, in this study, a method was investigated to reduce the number of false positives by excluding areas similar to the flower regions. Specifically, a novel detection method combining image processing, CNN, and fuzzy c-means is proposed. To validate the proposed method, it was compared with the previous method as well as the method in which k-means was used instead of fuzzy c-means. All results were evaluated using flower distribution maps marked by field workers. The proposed method improved the F-measure by up to 22.0% compared with the previous method. Application of the proposed method to orthorectified images facilitates the understanding of flower populations over a wide range of areas, which can contribute to the conservation and management of the species.     

Clinico-Pathological Discrepancies in the Diagnosis of Causes of Maternal Death in Sub-Saharan Africa: Retrospective Analysis

Background

Maternal mortality is a major public-health problem in developing countries. Extreme differences in maternal mortality rates between developed and developing countries indicate that most of these deaths are preventable. Most information on the causes of maternal death in these areas is based on clinical records and verbal autopsies. Clinical diagnostic errors may play a significant role in this problem and might also have major implications for the evaluation of current estimations of causes of maternal death.

Methods and Findings

A retrospective analysis of clinico-pathologic correlation was carried out, using necropsy as the gold standard for diagnosis. All maternal autopsies (n = 139) during the period from October 2002 to December 2004 at the Maputo Central Hospital, Mozambique were included and major diagnostic discrepancies were analyzed (i.e., those involving the cause of death). Major diagnostic errors were detected in 56 (40.3%) maternal deaths. A high rate of false negative diagnoses was observed for infectious diseases, which showed sensitivities under 50%: HIV/AIDS-related conditions (33.3%), pyogenic bronchopneumonia (35.3%), pyogenic meningitis (40.0%), and puerperal septicemia (50.0%). Eclampsia, was the main source of false positive diagnoses, showing a low predictive positive value (42.9%).

Conclusions

Clinico-pathological discrepancies may have a significant impact on maternal mortality in sub-Saharan Africa and question the validity of reports based on clinical data or verbal autopsies. Increasing clinical awareness of the impact of obstetric and nonobstetric infections with their inclusion in the differential diagnosis, together with a thorough evaluation of cases clinically thought to be eclampsia, could have a significant impact on the reduction of maternal mortality.     

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome.     

How to Capitalize on the Retest Effect in Future Trials on Huntington’s Disease

The retest effect—improvement of performance on second exposure to a task—may impede the detection of cognitive decline in clinical trials for neurodegenerative diseases. We assessed the impact of the retest effect in Huntington’s disease trials, and investigated its possible neutralization. We enrolled 54 patients in the Multicentric Intracerebral Grafting in Huntington’s Disease (MIG-HD) trial and 39 in the placebo arm of the Riluzole trial in Huntington’s Disease (RIL-HD). All were assessed with the Unified Huntington’s Disease Rating Scale (UHDRS) plus additional cognitive tasks at baseline (A₁), shortly after baseline (A₂) and one year later (A₃). We used paired t-tests to analyze the retest effect between A₁ and A₂. For each task of the MIG-HD study, we used a stepwise algorithm to design models predictive of patient performance at A₃, which we applied to the RIL-HD trial for external validation. We observed a retest effect in most cognitive tasks. A decline in performance at one year was detected in 3 of the 15 cognitive tasks with A₁ as the baseline, and 9 of the 15 cognitive tasks with A₂ as the baseline. We also included the retest effect in performance modeling and showed that it facilitated performance prediction one year later for 14 of the 15 cognitive tasks. The retest effect may mask cognitive decline in patients with neurodegenerative diseases. The dual baseline can improve clinical trial design, and better prediction should homogenize patient groups, resulting in smaller numbers of participants being required.     

Objective Assessment of Nuclear and Cortical Cataracts through Scheimpflug Images: Agreement with the LOCS III Scale

PurposeTo assess nuclear and cortical opacities through the objective analysis of Scheimpflug images, and to check the correlation with the Lens Opacity Classification System III (LOCS III).MethodsNuclear and cortical opacities were graded according to the LOCS III rules after pupil dilation. The maximum and average pixel intensity values along an elliptical mask within the lens nucleus were taken to analyse nuclear cataracts. A new metric based on the percentage of opaque pixels within a region of interest was used to analyse cortical cataracts. The percentage of opaque pixels was also calculated for half, third and quarter areas from the region of interest’s periphery.ResultsThe maximum and average intensity values along the nucleus were directly proportional to the LOCS III grade: The larger the LOCS III value, the larger maximum and average intensity ones. These metrics showed a positive and significant correlation with the LOCS grade: The larger the LOCS grade, the higher was percentage of opaque pixels along the cortex within the same mask’s size. This metric showed a significant correlation to the LOCS grade.ConclusionThe metrics used to assess nuclear opacities showed good correlation with the LOCS III. The percentage of opaque pixels showed to be a useful metric to measure objectively the severity of the cortical opacity. These metrics could be implemented in an algorithm to detect and grade lens opacities automatically and objectively.