With the development of the Internet of Things and smart devices, there is an increasing demand for intelligent control and remote monitoring. In this paper, Arduino microcontroller is used to build an intelligent security cart integrating remote control, video monitoring and automatic obstacle avoidance, which not only reflects the diversified applications of Internet of Things technology, but also provides a new solution for intelligent security. The article gives the overall composition and control system design of the trolley, and does experimental tests on the trolley control and obstacle avoidance and other functions. The experimental results show that the intelligent security trolley can realize reliable remote control and intelligent obstacle avoidance functions, providing users with convenient and safe monitoring services.

The real-time simulation of large-scale agricultural operations will offer farmers data-driven and physically consistent decision support, facilitated by predictive digital twins. To construct a predictive digital twin, the initial step involves 3D reconstruction of plant geometry. In this paper, a high-resolution, accurate 3D reconstruction of tomato plants, Tomato-NeRF, is proposed, which is specially used for three-dimensional reconstruction of tomato plants. Our approach used a modular design to integrate ideas from their research paper into Tomato-NeRF. By using hash encoding to map coordinates to trainable feature vectors, we balance quality, memory usage, and performance in NeRF training. The proposal sampler targets key regions for rendering, and customized loss functions are designed to optimize specific tasks. The effectiveness of our approach is demonstrated by the ability to generate high-resolution geometric models from phone camera data. Comparative results show that Tomato-NeRF has significant advantages over Instant-NGP and MipNeRF in the tomato plant reconstruction task. The data acquisition method is simpler and more efficient than other reconstruction methods, providing a practical solution for real-time agricultural simulations.

Genomic analysis has revealed that the 1,637-Mb Gossypium arboreum genome contains approximately 81% transposable elements (TEs), while only 57% of the 735-Mb G. raimondii genome is occupied by TEs. In this study, we investigated whether there were unknown transcripts associated with TE or TE fragments and, if so, how these new transcripts were evolved and regulated. As sequence depths increased from 4 to 100 G, a total of 10,284 novel intergenic transcripts (intergenic genes) were discovered. On average, approximately 84% of these intergenic transcripts possibly overlapped with the long terminal repeat (LTR) insertions in the otherwise untranscribed intergenic regions and were expressed at relatively low levels. Most of these intergenic transcripts possessed no transcription activation markers, while the majority of the regular genic genes possessed at least one such marker. Genes without transcription activation markers formed their+1 and -1 nucleosomes more closely (only (117±1.4)bp apart), while twice as big spaces (approximately (403.5±46.0) bp apart) were detected for genes with the activation markers. The analysis of 183 previously assembled genomes across three different kingdoms demonstrated systematically that intergenic transcript numbers in a given genome correlated positively with its LTR content. Evolutionary analysis revealed that genic genes originated during one of the whole-genome duplication events around 137.7 million years ago (MYA) for all eudicot genomes or 13.7 MYA for the Gossypium family, respectively, while the intergenic transcripts evolved around 1.6 MYA, resultant of the last LTR insertion. The characterization of these low-transcribed intergenic transcripts can facilitate our understanding of the potential biological roles played by LTRs during speciation and diversifications.

BackgroundTransposable elements (TEs) are able to diversify plant gene expression and function, sequentially promote plant variety and evolution. However, there is lack of efficient approach to investigate the evolution behavior and transcription activity of TEs in plants. Here we developed a pipeline Matrix-TE to comprehensively evaluate the super-families, differentiation and transcription activity of LTR/TEs in Indica and Japonica rice, the two considerable important and closely related monocots.ResultsSix LTR/TE super-families were identified by Matrix-TE in both Indica and Japonica rice genomes, in which the OS-type1 and OS-type2 super-families were unclassified. Indica rice specific TE peak P-Gypsy and Japonica rice specific TE peak P-Copia were observed separately. Then the two peaks were analyzed by Gaussian Probability Density Function (GPDF) fit. Significant TE transcription activities were observed in Indica and Japonica rice plants after stress treatments. Particularly, hot, cold and salt stresses induced the high expression level of LTR/TEs in rice plants.ConclusionsWe developed the approach Matrix-TE on the basis of BLASTN and GPDF algorithms, and applied it to comprehensively and quantitatively investigate LTR/TE types and contents in the close subspecies Indica and Japonica rice genomes. The individual TE burst events P-Copia and P-Gypsy were observed in Japonica and Indica rice, separately. RNA-seq and RT-PCR methods indicated that LTR/TE transcripts were induced by hot, cold and high salt stress conditions. The optimized Matrix-TE approach and procedures probably could be used in other plant species with big genomes like wheat and maize.

LTR-retrotransposable elements are major components of diploid (Gossypium arboreum) and tetraploid (Gossypium hirsutum) cotton genomes that have undergone dramatic increases in copy number during the course of evolution. However, little is known about the biological functions of LTR-retrotransposable elements in cotton. Here, we show that a copia-like LTR-retrotransposable element has maintained considerable activity in both G. arboreum and G. hirsutum. We identified two functional domains of the retrotransposon and analyzed their expression levels in various cotton tissues, including leaves, ovules, and germinating seeds. ChIP-qPCR (chromatin immunoprecipitation followed by quantitative PCR), using a copia-specific antibody, established that copia-like proteins primarily bind to the first exons of several protein-coding genes in cotton cells. This finding suggests that retrotransposons play a novel, important role in regulating the transcriptional activities of protein-coding genes with various biological activities.

Zinc finger proteins (ZFPs) containing only a single zinc finger domain play important roles in the regulation of plant growth and development, as well as in biotic and abiotic stress responses. To date, the evolutionary history and functions of the ZFP gene family have not been identified in cotton. In this paper, we identified 29 ZFP genes in Gossypium hirsutum. This gene family was divided into seven subfamilies, 22 of which were distributed over 17 chromosomes. Bioinformatic analysis revealed that 20 GhZFP genes originated from whole genome duplications and two originated from dispersed duplication events, indicating that whole genome duplication is the main force in the expansion of the GhZFP gene family. Most GhZFP8 subfamily genes, except for GhZFP8–3, were highly expressed during fiber cell growth, and were induced by brassinosteroids in vitro. Furthermore, we found that a large number of GhZFP genes contained gibberellic acid responsive elements, auxin responsive elements, and E-box elements in their promoter regions. Exogenous application of these hormones significantly stimulated the expression of these genes. Our findings reveal that GhZFP8 genes are involved in cotton fiber development and widely induced by auxin, gibberellin and BR, which provides a foundation for the identification of more downstream genes with potential roles in phytohormone stimuli, and a basis for breeding better cotton varieties in the future.

Due to the economic value of natural textile fiber, cotton has attracted much research attention, which has led to the publication of two diploid genomes and two tetraploid genomes. These big data facilitate functional genomic study in cotton, and allow researchers to investigate cotton genome structure, gene expression, and protein function on the global scale using high-throughput methods. In this review, we summarized recent studies of cotton genomes. Population genomic analyses revealed the domestication history of cultivated upland cotton and the roles of transposable elements in cotton genome evolution. Alternative splicing of cotton transcriptomes was evaluated genome-widely. Several important gene families like MYC, NAC, Sus and GhPLDα1 were systematically identified and classified based on genetic structure and biological function. High-throughput proteomics also unraveled the key functional proteins correlated with fiber development. Functional genomic studies have provided unprecedented insights into global-scale methods for cotton research.

OBJECTIVES:To describe the natural history and clinical features of sporadic amyotrophic lateral sclerosis (ALS) in Chinese patients, and to report data on the prognostic factors for survival.METHODS:All patients referred to our ALS centre between 2003 and 2012 were followed up every 3 months. Survival and tracheotomy were predefined as primary outcome measures. Group differences were analysed using parametric and non-parametric tests as appropriate. Survival was analysed using the Kaplan-Meier method and Cox regression analysis.RESULTS:Of the 1624 patients with ALS, 75.1% had limb-onset, 14.0% had bulbar-onset, 7.8% had flail-arm syndrome (FAS), 2.6% had progressive muscular atrophy and 0.5% had primary lateral sclerosis. The male:female ratio was 1.7:1, and the mean age at onset was 49.8 years. The median diagnostic delay was 14 months, and the median survival time after symptom onset was 71 months. Male gender, older age at symptom onset, lower body mass index, shorter diagnostic delay, bulbar-onset ALS phenotype, higher Airlie House category at presentation, rural place of residence, use of traditional Chinese medicine and a history of contact with pesticides were associated with poorer survival, whereas female gender or an FAS phenotype may have a better prognosis.CONCLUSIONS:The clinical characteristics and outcomes of Chinese patients with sporadic ALS were different compared with patients from other countries. Compared with other studies, the age at onset of Chinese patients was earlier, the percentage of bulbar-onset ALS was lower and the prognosis was better. This study substantially advances the understanding of the clinical features and epidemiology of this rare disease.

Color Channel Comparison Method is an effective method to transform color images into gray ones. This method can enhance pests’ characteristics and remove the background to some extent. However, some interfering background cannot be removed. In order to solve this problem, an improvement on Color Channel Comparison Method is realized in this paper. Comparisons between gray brightness and a threshold value determine whether the pixel is the interfering background. And the threshold value is determined according to the brightness of the image. Empirical results show that the interfering background in black or white pests’ photo is effectively cleared, and black or white pests can be more effectively separated from the colored background by using the improved method. The improved color channel comparison method can effectively solve the interfering background problem of Color Channel Comparison Method.

Improved color channel comparison method (ICCCM) is an effective method to transform color images into gray-scale ones. Based on the ICCCM, black or white insects could be effectively extracted and recognized from the real color images with bright background. However it is difficult to use the ICCCM to extract and recognize the black insects from the real color image with dark background. In this paper, the ICCCM is modified to transform the color images into the gray ones, extracting and recognizing the black insects on the dark background. The ICCCM is modified as follows: (1) A threshold of the gray image is an average brightness value of red (R), green (G) and blue (B) in all the image pixels. (2) The bright pixels and the color pixels have the highest brightness value 255 in the gray image. (3) A pixel brightness value of the dark area in the gray image equals to a minimum of R, G and B in the pixel. (4) After deleted all the pixels with a brightness value of 255, a threshold of the binary image is determined by Otsu's theory. The modified ICCCM more effectively extracts and recognizes the black insects from the real color images with dark background compared with the ICCCM.

Ancylis sativa Liu is a kind of insects to cause severe damage to jujube trees. This paper studies the recognition of Ancylis sativa Liu from catchers via using computer vision technique. The image of a Ancylis sativa catcher generally shows not only Ancylis sativa but also other objects. Consequently it leads to that the image has a complicated background and an uncertain interesting region. Ancylis sativa Liu are able successfully to be recognized from the image as follows. Firstly, the region of interest is extracted from the image by enhancing the contrast of Ancylis sativa Liu to the background. Secondly, the outline of Ancylis sativa Liu shape is drawn by identifying the vertexes of the object figure. The recognition of Ancylis sativa Liu is effective and practical. It can be expected to be applied to the detections of similar kinds of jujube insects.

The mixture of Gaussian processes (MGP) is an important probabilistic model which is often applied to the regression and classification of temporal data. But the existing EM algorithms for its parameter learning encounters a hard difficulty on how to compute the expectations of those assignment variables (as the hidden ones). In this paper, we utilize the leave-one-out cross-validation probability decomposition for the conditional probability and develop an efficient EM algorithm for the MGP model in which the expectations of the assignment variables can be solved directly in the E-step. In the M-step, a conjugate gradient method under a standard Wolfe-Powell line search is implemented to learn the parameters. Furthermore, the proposed EM algorithm can be carried out in a hard cutting way such that each data point is assigned to the GP expert with the highest posterior in the E-step and then the parameters of each GP expert can be learned with these assigned data points in the M-step. Therefore, it has a potential advantage of handling large datasets in comparison with those soft cutting methods. The experimental results demonstrate that our proposed EM algorithm is effective and efficient.

Mixture of experts (ME) is a modular neural network architecture for supervised classification. The double-loop expectation-maximization (EM) algorithm has been developed for learning the parameters of the ME architecture, and the iteratively reweighted least squares (IRLS) algorithm and the Newton-Raphson algorithm are two popular schemes for learning the parameters in the inner loop or gating network. In this letter, we investigate asymptotic convergence properties of the EM algorithm for ME using either the IRLS or Newton-Raphson approach. With the help of an overlap measure for the ME model, we obtain an upper bound of the asymptotic convergence rate of the EM algorithm in each case. Moreover, we find that for the Newton approach as a specific Newton-Raphson approach to learning the parameters in the inner loop, the upper bound of asymptotic convergence rate of the EM algorithm locally around the true solution Θ* is [Formula: see text], where ϵ>0 is an arbitrarily small number, o(x) means that it is a higher-order infinitesimal as x → 0, and e(Θ*) is a measure of the average overlap of the ME model. That is, as the average overlap of the true ME model with large sample tends to zero, the EM algorithm with the Newton approach to learning the parameters in the inner loop tends to be asymptotically superlinear. Finally, we substantiate our theoretical results by simulation experiments.

The mixture of experts (ME) architecture is a powerful neural network model for supervised learning, which contains a number of ‘‘expert’’networks plus a gating network. The expectation-maximization (EM) algorithm can be used to learn the parameters of the ME architecture. In fact, there have already existed several methods to implement the EM algorithm, such as the IRLS algorithm, the ECM algorithm, and an approximation to the Newton-Raphson algorithm. The differences among these implementations rely on how to train the gating network, which results in a double-loop training procedure, i.e., there is an inner loop training procedure within the general or outer loop training procedure. In this paper, we propose a least mean square regression method to learn or compute the parameters for the gating network directly, which leads to a single loop (i.e., there is no inner loop training) EM algorithm for the ME architecture. It is demonstrated by the simulation experiments that our proposed EM algorithm outperforms the existing ones on both speed and classification accuracy.

This paper presents a new method that utilizes the technologies of image processing and computer vision. Firstly, the projected areas of the pig’s image captured directly from top view are computed. Secondly, the heights are obtained from side view. Then the pig’s weight is estimated by the projected areas and heights. By comparing with the real weight, the mean relative error is 3.2%. The experiment indicates that this hands-off method has great significance in scientific management of the pig’s production which does not require large labor and material resources, and also avoid the loss in production resulted from stress.