Analysis of genetic diversity and structure of endangered Dengchuan cattle population using a single-nucleotide polymorphism chip.

This study aimed to evaluate the genetic diversity and structure within the Dengchuan cattle population and effectively protect and utilize their germplasm resources. Herein, the single-nucleotide polymorphisms (SNPs) of 100 Dengchuan cattle (46 bulls and 54 cows) were determined using the GGP Bovine 100K SNP Beadchip. The results showed that among the Dengchuan cattle, a total of 101,220 SNPs were detected, and there were 83,534 SNPs that passed quality control, of which 85.7% were polymorphic. The average genetic distance based on identity-by-state (IBS) within the conservation population of Dengchuan cattle was 0.26 ± 0.02. A total of 3,999 genome-length runs of homozygosity (ROHs) were detected in the Dengchuan cattle, with ROH lengths primarily concentrated in the range of 1-5 Mb, accounting for 87.02% of the total. The average inbreeding coefficient based on ROHs was 4.6%, within the conservation population of Dengchuan cattle, whereas it was 4.9% for bulls, and the Wright inbreeding coefficient (FIS) value was 2.4%, demonstrating a low level of inbreeding within the Dengchuan cattle population. Based on neighbor-joining tree analysis, the Dengchuan cattle could be divided into 16 families. In summary, the conservation population of Dengchuan cattle displays relatively abundant diversity and a moderate genetic relationship. Inbreeding was observed among a few individuals, but the overall inbreeding level of the population remained low. It is important to maintain this low level of inbreeding when introducing purebred bloodlines to expand the core group. This approach will ensure the long-term conservation of Dengchuan cattle germplasm resources and prevent loss of genetic diversity.

Early Identification of Root Damages Caused by Western Corn Rootworms Using a Minimally Invasive Root Phenotyping Robot-MISIRoot.

Western corn rootworm (WCR) is one of the most devastating corn rootworm species in North America because of its ability to cause severe production loss and grain quality damage. To control the loss, it is important to identify the infection of WCR at an early stage. Because the root system is the earliest feeding source of the WCR at the larvae stage, assessing the direct damage in the root system is crucial to achieving early detection. Most of the current methods still necessitate uprooting the entire plant, which could cause permanent destruction and a loss of the original root's structural information. To measure the root damages caused by WCR non-destructively, this study utilized MISIRoot, a minimally invasive and in situ automatic plant root phenotyping robot to collect not only high-resolution images but also 3D positions of the roots without uprooting. To identify roots in the images and to study how the damages were distributed in different types of roots, a deep convolution neural network model was trained to differentiate the relatively thick and thin roots. In addition, a color camera was used to capture the above-ground morphological features, such as the leaf color, plant height, and side-view leaf area. To check if the plant shoot had any visible symptoms in the inoculated group compared to the control group, several vegetation indices were calculated based on the RGB color. Additionally, the shoot morphological features were fed into a PLS-DA model to differentiate the two groups. Results showed that none of the above-ground features or models output a statistically significant difference between the two groups at the 95% confidence level. On the contrary, many of the root structural features measured using MISIRoot could successfully differentiate the two groups with the smallest t-test p-value of 1.5791 × 10-6. The promising outcomes were solid proof of the effectiveness of MISIRoot as a potential solution for identifying WCR infestations before the plant shoot showed significant symptoms.

Development of a Target-to-Sensor Mode Multispectral Imaging Device for High-Throughput and High-Precision Touch-Based Leaf-Scale Soybean Phenotyping.

Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239).

Effects of Chitosan on the Secretion of Cytokines and Expression of Inducible Nitric Oxide Synthase mRNA in Peritoneal Macrophages of Broiler Chicken

An in vitro experiment was conducted to study the effects of chitosan on the secretion of cytokines and expression of inducible nitric oxide synthase mRNA in peritoneal macrophages of broiler chicken. In the experiment, peritoneal macrophages were incubated for 24 h in culture medium supplemented with 0 (control), 40, 80, 160 and 320 µg/mL chitosan. The results showed that chitosan tended to increase quadratically the levels of interleukin-1 (P = 0.093) and interleukin-2 (P = 0.106) in the culture fluid of peritoneal macrophage. Chitosan also significantly enhanced inducible nitric oxide synthase mRNA expression of peritoneal macrophage in a quadratic dose-dependent manner (P < 0.05) and tended to promote quadratically the secretion of nitric oxide (P = 0.053) and inducible nitric oxide synthase (P = 0.157) in peritoneal macrophages. This result implied that one of the mechanisms by which chitosan modulated immune functions in chickens might be chitosan activating expression of inducible nitric oxide synthase and then improving the secretion of nitric oxide.