A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice

The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics research. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and subsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday

Investigating the Bio-corrosion Inhibition Effect of Enzymes in Circulating Cooling Water System

To avoid the secondary pollution that inorganic corrosion inhibitor may cause in circulating cooling water, the bio-corrosion inhibition effect of lysozyme, catalase, lipase, and laccase, as the biologic inhibitors without the above problem, and the increased bio-corrosion inhibition effect of Ca+2 and Mg+2 are studied. An enzyme with corrosion properties was selected by rotary coupon test. The inhibition rate test and the inhibition rate of ultraviolet test of lysozyme as well as lipase’s SEM analysis and elemental analysis were used to explore its inhibition mechanism. Aiming to decrease enzyme’s usage cost, the rotary coupon test was performed to study the effect of different ion mass concentrations on enzyme activity; its inhibition effect was first analyzed, and the complex formulation of enzyme with ion and polyaspartate was then investigated. The experiment showed that among all single enzymatic reagents, lysozyme and lipase had the best corrosion inhibition effect, and when Ca+2 mass concentration range was 107.75-182.57 mg/L, enzyme activity, microbial resistance, and corrosion inhibition properties were improved; a sample compound showed the best corrosion inhibition effect when 10 mg/L, 50 mg/L, and 50 mg/L of lysozyme, lipase, and poly-aspartic acid respectively were used. When the corrosion speed was controlled at 0.005 mm/a, the inhibition efficiency was above 95%

CodeExp: Explanatory Code Document Generation

Developing models that can automatically generate detailed code explanation can greatly benefit software maintenance and programming education. However, existing code-to-text generation models often produce only high-level summaries of code that do not capture implementation-level choices essential for these scenarios. To fill in this gap, we propose the code explanation generation task. We first conducted a human study to identify the criteria for high-quality explanatory docstring for code. Based on that, we collected and refined a large-scale code docstring corpus and formulated automatic evaluation metrics that best match human assessments. Finally, we present a multi-stage fine-tuning strategy and baseline models for the task. Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones. We envision our training dataset, human-evaluation protocol, recommended metrics, and fine-tuning strategy can boost future code explanation research. The code and annotated data are available at https://github.com/subercui/CodeExp.Comment: Accepted in Findings of EMNLP 202

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Accurate Inference of Rice Biomass Based on Support Vector Machine

International audienceBiomass is an important phenotypic trait in plant growth analysis. In this study, we established and compared 8 models for measuring aboveground biomass of 402 rice varieties. Partial least squares (PLS) regression and all subsets regression (ASR) were carried out to determine the effective predictors. Then, 6 models were developed based on support vector regression (SVR). The kernel function used in this study was radial basis function (RBF). Three different optimization methods, Genetic Algorithm (GA) K-fold Cross Validation (K-CV), and Particle Swarm Optimization (PSO), were applied to optimize the penalty error C and RBF \upgamma γ. We also compared SVR models with models based on PLS regression and ASR. The result showed the model in combination of ASR, GA optimization and SVR outperformed other models with coefficient of determination (R2) of 0.85 for the 268 varieties in the training set and 0.79 for the 134 varieties in the testing set, respectively. This paper extends the application of SVR and intelligent algorithm in measurement of cereal biomass and has the potential of promoting the accuracy of biomass measurement for different varieties

Rapid Identification of Rice Varieties by Grain Shape and Yield-Related Features Combined with Multi-class SVM

International audienceRice is the major food of approximately half world population and thousands of rice varieties are planted in the world. The identification of rice varieties is of great significance, especially to the breeders. In this study, a feasible method for rapid identification of rice varieties was developed. For each rice variety, rice grains per plant were imaged and analyzed to acquire grain shape features and a weighing device was used to obtain the yield-related parameters. Then, a Support Vector Machine (SVM) classifier was employed to discriminate the rice varieties by these features. The average accuracy for the grain traits extraction is 98.41 %, and the average accuracy for the SVM classifier is 79.74 % by using cross validation. The results demonstrated that this method could yield an accurate identification of rice varieties and could be integrated into new knowledge in developing computer vision systems used in automated rice-evaluated system

Mesoscopic prediction on the effective thermal conductivity of unsaturated clayey soil with double porosity system

International audienceThe effect of water distribution on heat conduction in unsaturated structural clayey soils with double porosity system is investigated in the present study. A dual-porosity model consisting of intra-aggregate and inter-aggregate pores is developed to describe the water distribution within intra-aggregate and inter-aggregate pores. Heat transfer using this model is then numerically simulated to determine the soil effective thermal conductivity. The obtained values are compared with available experimental data. The results show that the model can predict the accurate soil thermal conductivity values at various water contents and densities. On one hand, the model shows a higher soil thermal conductivity when water content and/or dry density are higher. On the other hand, the model shows an effect of water distribution on the soil thermal conductivity; this later is higher when water is preferentially distributed within aggregates than between aggregates. In addition, the model can give a direct visualization of heat transfer mechanisms in unsaturated clayey soils with double porosity system. In fact, heat conduction is dominant in the wet region of the dual-porosity space. This study provides both a useful predictive model of thermal conductivity and a better understanding on the physical mechanism of heat conduction in unsaturated structural clayey soils or other multiphase media with double porosity system

Data on the generation of rabbit infections and RPR titre changes in serum samples from syphilis patients at follow-up

The data presented in this article are related to the research article entitled “Performance of novel infection phase-dependent antigens in syphilis serodiagnosis and treatment efficacy determination”. The rabbit model [1,2] is an appropriate animal model for studying syphilis, a classic sexually transmitted disease (STD). Live Treponema pallidum (T. pallidum, Tp) and inactivated T. pallidum were inoculated in the backs of New Zealand rabbits. RT-PCR was performed to determine whether T. pallidum DNA could be detected in different groups. Sixty paired serum samples from patients at follow-up were tested by RPR and recombinant Tp0971-, Tp0768-, Tp0462- and Tp92-based ELISA. Keywords: Treponema pallidum, Rabbit, RPR, ELIS

Determination of rice panicle numbers during heading by multi-angle imaging

Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping