A novel FCTF evaluation and prediction model for food efficacy based on association rule mining

IntroductionFood-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.MethodsUsing the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.ResultsThe FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.DiscussionCenturies of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline

A Proposal for Lodging Judgment of Rice Based on Binocular Camera

Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters

TCNet: Transformer Convolution Network for Cutting-Edge Detection of Unharvested Rice Regions

Cutting-edge detection is a critical step in mechanized rice harvesting. Through visual cutting-edge detection, an algorithm can sense in real-time whether the rice harvesting process is along the cutting-edge, reducing loss and improving the efficiency of mechanized harvest. Although convolutional neural network-based models, which have strong local feature acquisition ability, have been widely used in rice production, these models involve large receptive fields only in the deep network. Besides, a self-attention-based Transformer can effectively provide global features to complement the disadvantages of CNNs. Hence, to quickly and accurately complete the task of cutting-edge detection in a complex rice harvesting environment, this article develops a Transformer Convolution Network (TCNet). This cutting-edge detection algorithm combines the Transformer with a CNN. Specifically, the Transformer realizes a patch embedding through a 3 × 3 convolution, and the output is employed as the input of the Transformer module. Additionally, the multi-head attention in the Transformer module undergoes dimensionality reduction to reduce overall network computation. In the Feed-forward network, a 7 × 7 convolution operation is used to realize the position-coding of different patches. Moreover, CNN uses depth-separable convolutions to extract local features from the images. The global features extracted by the Transformer and the local features extracted by the CNN are integrated into the fusion module. The test results demonstrated that TCNet could segment 97.88% of the Intersection over Union and 98.95% of the Accuracy in the unharvested region, and the number of parameters is only 10.796M. Cutting-edge detection is better than common lightweight backbone networks, achieving the detection effect of deep convolutional networks (ResNet-50) with fewer parameters. The proposed TCNet shows the advantages of a Transformer combined with a CNN and provides real-time and reliable reference information for the subsequent operation of rice harvesting

Epidemiology, evolutionary origin, and malaria‐induced positive selection effects of G6PD‐deficient alleles in Chinese populations

Abstract Background Although glucose‐6‐phosphate dehydrogenase (G6PD) deficiency is the most common inherited disorder in the Chinese population, there is scarce evidence regarding the epidemiology, evolutionary origin, and malaria‐induced positive selection effects of G6PD‐deficient alleles in various Chinese ethnic populations. Methods We performed a large population‐based screening (n = 15,690) to examine the impact of selection on human nucleotide diversity and to infer the evolutionary history of the most common deficiency alleles in Chinese populations. Results The frequencies of G6PD deficiency ranged from 0% to 11.6% in 12 Chinese ethnic populations. A frequency map based on geographic information showed that G6PD deficiency was highly correlated with historical malaria prevalence in China and was affected by altitude and latitude. The five most frequently occurring G6PD gene variants were NM_001042351.3:c.1376G>T, NM_001042351.3:c.1388G>A, NM_001042351.3:c.95A>G, NM_001042351.3:c.1311T>C, and NM_001042351.3:c.1024C>T, which were distributed with ethnic features. A pathogenic but rarely reported variant site (NM_001042351.3:c.448G>A) was identified in this study. Bioinformatic analysis revealed a strong and recent positive selection targeting the NM_001042351.3:c.1376G>T allele that originated in the past 3125 to 3750 years and another selection targeting the NM_001042351.3:c.1388G>A allele that originated in the past 5000 to 6000 years. Additionally, both alleles originated from a single ancestor. Conclusion These results indicate that malaria has had a major impact on the Chinese genome since the introduction of rice agriculture