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The software KaryoType, an improved and completely renewed version of the previously existing NucType, was developed for plant chromosomes. The primary function of the software is to allow efficient chromosome measurements and karyotype analysis from microphotographs. Karyotype characterization usually includes chromosome number, size, arm ratio, centromeric index, relative lengths and karyotype formula. Moreover, KaryoType is also capable of measuring karyotype asymmetry indices such as CVCL, CVCI and MCA, and can recognize chromosome homologues based on chromosome length and arm ratio automatically or manually. This program runs on Windows 7 and above and Mac OS X and is freely available at the website of the University of Sichuan (http://mnh.scu.edu.cn/soft/blog/karyotype/).

Combining state-of-the art digital imaging technology with different kinds of marine exploration techniques such as modern AUV (autonomous underwater vehicle), ROV (remote operating vehicle) or other monitoring platforms enables marine imaging on new spatial and/or temporal scales. A comprehensive interpretation of such image collections requires the detection, classification and quantification of objects of interest in the images usually performed by domain experts. However, the data volume and the rich content of the images makes the support by software tools inevitable. We define some requirements for marine image annotation and present our new online tool Biigle 2.0. It is developed with a special focus on annotating benthic fauna in marine image collections with tools customized to increase efficiency and effectiveness in the manual annotation process. The software architecture of the system is described and the special features of Biigle 2.0 are illustrated with different use-cases and future developments are discussed.

Purpose To investigate whether an increased magnitude of quantitative rotatory knee laxity is associated with a greater level of generalized joint laxity in ACL-injured and contralateral knees. Methods A total of 103 patients were enrolled across four international centers to undergo anatomic ACL reconstruction. Rotatory knee laxity was evaluated preoperatively, both in the awake state and under anesthesia, using the standardized pivot shift test. Two devices were used to quantify rotatory knee laxity; an inertial sensor, measuring the joint acceleration, and an image analysis system, measuring the lateral compartment translation of the tibia. The presence of generalized joint laxity was determined using the Beighton Hypermobility Score. The correlation between the level of generalized joint laxity and the magnitude of rotatory knee laxity was calculated for both the involved knee and the non-involved knee. Further, patients were dichotomized into low (0–4) or high (5–9) Beighton Score groups. Alpha was set at < 0.05. Results Ninety-six patients had complete datasets, 83 and 13 in the low and high Beighton Score groups respectively. In anesthetized patients, there was a significant correlation between the degree of Beighton Score and quantitative pivot shift when analyzing the non-involved knee using the image analysis system ( r  = 0.235, p  < 0.05). When analyzing the same knee, multivariate analysis adjusted for meniscal injury, age and gender revealed an increased odds ratio for patients with increased lateral compartment translation to be part of the high Beighton Score group (OR 1.86, 95% CI 1.10–3.17, p  < 0.05). No other correlation was significant. When analyzing the dichotomized subgroups, no significant correlations could be established. Conclusion The findings in this study suggest that there is a weak correlation between generalized joint laxity and the contralateral healthy knee, indicating increased rotatory knee laxity in these patients. Generalized joint laxity does not appear to correlate with rotatory knee laxity in ACL-injured knees. Level of evidence Prospective cohort study; level of evidence, 2.

Objective To evaluate the changes of skin surface parameters in a patient with vitiligo using Antera 3D ® Skin Image Analysis System, and to observe the clinical efficacy of the treatment of facial vitiligo. Methods A patient with facial vitiligo was treated with intradermal injection of psoralen solution into the lesion combined with 308 nm excimer laser therapy. Vitiligo lesion images were captured using the Antera 3D ® camera before treatment and every 2 weeks after the start of the treatment. The associated software analysis system was used to measure the parameters of melanin level, ultra-low melanin concentration, and L* value as objective indexes for the evaluation of vitiligo treatment efficacy. Results After 10 treatments, the melanin level of the patient’s skin lesions increased from 0.480 to 0.580, while the ultra-low melanin concentration and L* value decreased from 9.360 to 64.10 to 3.032 and 58.10, respectively. Conclusion The Antera 3D ® skin image analysis system provides objective data, which are conducive to improving patients’ treatment compliance and confidence, as well as facilitating the development of treatment plans.

Sports image analysis system based on computer vision image processing technology is a hot project in urgent need of society at present. Due to various objective and subjective factors, the application environment of intelligent module in sports image analysis system is limited, and there is still a certain gap from the social requirements. In this paper, the hardware selection of the system is focused on the applicability and cost. Based on the idea of the inherent hardware of the organizers of the grass-roots sports meeting, the system combining camera, video capture card and PC is selected, which has a high ratio of information to price. A fuzzy enhancement system of 3D digital image based on data mining is designed. Analyze the motion information in video stream to a certain extent, extract the physical characteristics and behavioral characteristics of moving objects, and use the fusion of the two to complete the dynamic target analysis in video surveillance images. The sports image analysis system based on 3D image technology designed in this paper has good universality and good application and popularization value.

Modern science and technology is highly differentiated and concentrated, and has also entered a new stage of multidisciplinary comprehensive utilization. Especially, VR technology has played a great role in competitive sports training. Intelligent video analysis system is a key point of modern video development. With the development of computer technology and the gradual progress of video technology, intelligent video analysis system has broad development space. In this paper, a key algorithm SISA (Sports Image Segmentation Algorithm) is designed. The algorithm improves and optimizes the semantic segmentation algorithm by using hole convolution, pyramid pooling and trainable conditional random field structure, and uses key node detection algorithm to enhance the segmentation effect of small objects. The common methods to realize virtual human animation are manual marking, motion capture and automatic recognition. In order to reduce the workload of manual marking, avoid the high cost of motion capture and make up for the precision error of automatic recognition, it can be practically applied to the guidance of physical training.

On June 24, 2017, a catastrophic landslide destroyed the village of Xinmo (Maoxian County, Sichuan, China). A 2.87 × 106 m3 rock mass in source area collapsed and entrained the surface soil layer along the run-out path. This disaster took eighty-three people’s lives and destroyed more than 103 houses. It is worth noting that rock fragmentation and grinding could expand the spreading area of danger zone in a landslide event. The Xinmo landslide provided a rare opportunity to infer the dynamic fragmentation and grinding of rock masses from the particle size and shape distribution in the entrainment and deposition area. A field investigation combined with an automatic image analysis system was conducted to study the characteristics of particle size and shape distribution along the debris channel. The image analysis of these field data showed that the median size (D50) of particles ranged from 0.41 to 27.71 m in the landslide area. Particle fractal dimension (D) obtained from the Number-size distribution ranged from 1.77 to 2.97 over the entire study area. Moreover, the evolution of D50 and D along the run-out path confirmed that the degree of cumulative rock fragmentation increased as the travel distance increased. Additionally, the particle roundness (R) ranged from 0.51 to 0.88 along the run-out path, which peaked twice during the motion of granular flow, once was in the entrainment area, and another was in the end of the deposition area. Rock scraping occurred in the entrainment area could increase the degree of rock grinding, and reshape coarse stones into smooth particles of large R values (larger roundness of particles could lead to longer spreading distance in a landslide event, due to the lower internal friction among particles). Based on analysis above, the rock scraping phenomena occurred between the source materials and entrainment materials were confirmed to influence the translation and spread of granular flows in landslides.

Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories—area, boundary, and color—and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture.

Background Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila . Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data. Results We have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system. Conclusion Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy.