Comparing SNP Panels and Statistical Methods for Estimating Genomic Breed Composition of Individual Animals in Ten Cattle Breeds

Background: SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively. Finally, the downstream implication of GBC on genomic prediction accuracies was investigated and discussed in a Santa Gertrudis cattle population. Results: There were 15,708 common SNPs across five currently-available commercial bovine SNP chips. From this set, four subsets (1,000, 3,000, 5,000, and 10,000 SNPs) were selected by maximizing average Euclidean distance (AED) of SNP allelic frequencies among the ten cattle breeds. For 198 animals presented as Akaushi, estimated GBC of the Akaushi breed (GBCA) based on the admixture model agreed very well among the five SNP panels, identifying 166 animals with GBCA = 1. Using the same SNP panels, the linear regression approach reported fewer animals with GBCA = 1. Nevertheless, estimated GBCA using both models were highly correlated (r = 0.953 to 0.992). In the genomic prediction of a Santa Gertrudis population (and crosses), the results showed that the predictability of molecular breeding values using SNP effects obtained from 1,225 animals with no less than 0.90 GBC of Santa Gertrudis (GBCSG) decreased on crossbred animals with lower GBCSG. Conclusions: Of the two statistical models used to compute GBC, the admixture model gave more consistent results among the five selected SNP panels than the linear regression model. The availability of these common SNP panels facilitates identification and estimation of breed compositions using currently-available bovine SNP chips. In view of utility, the 1 K panel is the most cost effective and it is convenient to be included as add-on content in future development of bovine SNP chips, whereas the 10 K and 16 K SNP panels can be more resourceful if used independently for imputation to intermediate or high-density genotypes

Wave energy technology in China

This paper traces the research stages of China&#39;s study of wave energy technology, summarizing the findings and deficiencies of each stage from oscillating water column, through onshore oscillating buoy to floating Duck. It also highlights the major innovations in China&#39;s new floating Duck device.</p

Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data

Shelterbelts (or windbreaks) can effectively improve the microclimate and soil conditions of adjacent farmland and thus increase crop yield. However, the individual contribution of these two factors to yield changes is still unclear since the short-term effect from the microclimate and the accumulated effect from the soil jointly affect crop yield. The latter (soil effect) is supposed to remain after shelterbelt-cutting, thus inducing a post-cutting legacy effect on yield, which can be used to decompose the shelterbelt-induced yield increase. Here, we develop an innovative framework to investigate the legacy effect of post-cutting shelterbelt on corn yield by combining Google Earth and Sentinel-2 data in Northeastern China. Using this framework, for the first time, we decompose the shelterbelt-induced yield increase effect into microclimate and soil effects by comparing the yield profiles before and after shelterbelt-cutting. We find that on average, the intensity of the legacy effect, namely the crop yield increment of post-cutting shelterbelts, is 0.98 +/- 0.03%. The legacy effect varies depending on the shelterbelt-farmland relative location and shelterbelt density. The leeward side of the shelterbelt-adjacent farmland has a more remarkable legacy effect compared to the windward side. Shelterbelts with medium-high density have the largest legacy effect (1.94 +/- 0.05%). Overall, the legacy effect accounts for 47% of the yield increment of the shelterbelt before cutting, implying that the soil effect is almost equally important for increasing crop yield compared to the microclimate effect. Our findings deepen the understanding of the mechanism of shelterbelt-induced yield increase effects and can help to guide shelterbelt management

Field-Effect Sensors Using Biomaterials for Chemical Sensing

After millions of years of evolution, biological chemical sensing systems (i.e., olfactory and taste systems) have become very powerful natural systems which show extreme high performances in detecting and discriminating various chemical substances. Creating field-effect sensors using biomaterials that are able to detect specific target chemical substances with high sensitivity would have broad applications in many areas, ranging from biomedicine and environments to the food industry, but this has proved extremely challenging. Over decades of intense research, field-effect sensors using biomaterials for chemical sensing have achieved significant progress and have shown promising prospects and potential applications. This review will summarize the most recent advances in the development of field-effect sensors using biomaterials for chemical sensing with an emphasis on those using functional biomaterials as sensing elements such as olfactory and taste cells and receptors. Firstly, unique principles and approaches for the development of these field-effect sensors using biomaterials will be introduced. Then, the major types of field-effect sensors using biomaterials will be presented, which includes field-effect transistor (FET), light-addressable potentiometric sensor (LAPS), and capacitive electrolyte–insulator–semiconductor (EIS) sensors. Finally, the current limitations, main challenges and future trends of field-effect sensors using biomaterials for chemical sensing will be proposed and discussed

Comparing SNP Panels and Statistical Methods for Estimating Genomic Breed Composition of Individual Animals in Ten Cattle Breeds

Background: SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively. Finally, the downstream implication of GBC on genomic prediction accuracies was investigated and discussed in a Santa Gertrudis cattle population. Results: There were 15,708 common SNPs across five currently-available commercial bovine SNP chips. From this set, four subsets (1,000, 3,000, 5,000, and 10,000 SNPs) were selected by maximizing average Euclidean distance (AED) of SNP allelic frequencies among the ten cattle breeds. For 198 animals presented as Akaushi, estimated GBC of the Akaushi breed (GBCA) based on the admixture model agreed very well among the five SNP panels, identifying 166 animals with GBCA = 1. Using the same SNP panels, the linear regression approach reported fewer animals with GBCA = 1. Nevertheless, estimated GBCA using both models were highly correlated (r = 0.953 to 0.992). In the genomic prediction of a Santa Gertrudis population (and crosses), the results showed that the predictability of molecular breeding values using SNP effects obtained from 1,225 animals with no less than 0.90 GBC of Santa Gertrudis (GBCSG) decreased on crossbred animals with lower GBCSG. Conclusions: Of the two statistical models used to compute GBC, the admixture model gave more consistent results among the five selected SNP panels than the linear regression model. The availability of these common SNP panels facilitates identification and estimation of breed compositions using currently-available bovine SNP chips. In view of utility, the 1 K panel is the most cost effective and it is convenient to be included as add-on content in future development of bovine SNP chips, whereas the 10 K and 16 K SNP panels can be more resourceful if used independently for imputation to intermediate or high-density genotypes

analyticalmethodofradiationbyawaveenergydevicewithdualrectangularfloatingbodies

The system with one floating rectangular body on the free surface and one submerged rectangular body has been applied to a wave energy conversion device in water of finite depth. the radiation problem by this device on a plane incident wave is solved by the use of an eigenfunction expansion method, and a new analytical expression for the radiation velocity potential is obtained. the wave excitation force is calculated via the known incident wave potential and the radiation potential with a theorem of haskind employed. to verify the correctness of this method, an example is computed respectively through the bound element method and analytical method. results show that two numerical methods. are in good agreement, which shows that the present method is applicable. in addition, the trends of hydrodynamic coefficients and wave force are analyzed under different conditions by use of the present analytical method

Iodine Immobilized UiO-66-NH₂ Metal-Organic Framework as an Effective Antibacterial Additive for Poly(ε-caprolactone)

Iodine has been widely used as an effective disinfectant with broad-spectrum antimicrobial potency. However, the application of iodine in an antibacterial polymer remains challenging due to its volatile nature and poor solubility. Herein, iodine immobilized UiO-66-NH2 metal-organic framework (MOF) (UiO66@I2) with a high loading capacity was synthesized and used as an effective antibacterial additive for poly(ε-caprolactone) (PCL). An orthogonal design approach was used to achieve the optimal experiments’ conditions in iodine adsorption. UiO66@I2 nanoparticles were added to the PCL matrix under ultrasonic vibration and evaporated the solvent to get a polymer membrane. The composites were characterized by SEM, XRD, FTIR, and static contact angle analysis. UiO-66-NH2 nanoparticles have a high iodine loading capacity, up to 18 wt.%. The concentration of iodine is the most important factor in iodine adsorption. Adding 0.5 wt.% or 1.0 wt.% (equivalent iodine content) of UiO66@I2 to the PCL matrix had no influence on the structure of PCL but reduces the static water angle. The PCL composites showed strong antibacterial activities against Staphylococcus aureus and Escherichia coli. In contrast, the same content of free iodine/PCL composites had no antibacterial activity. The difference in the antibacterial performance was due to the different iodine contents in the polymer composites. It was found that MOF nanoparticles could retain most of the iodine during the sample preparation and storage, while there was few iodine left in the free iodine/PCL composites. This study offers a common and simple way to immobilize iodine and prepare antibacterial polymers with low antiseptic content that would reduce the influence of an additive on polymers’ physical properties

Attributing the impacts of ecological engineering and climate change on carbon uptake in Northeastern China

Context: In the past decades, several ecological engineering (eco-engineering) programs have been conducted in China, leading to a significant increase in regional carbon sink. However, the contribution of different eco-engineering programs to carbon uptake is still not clear, as the location of different programs is difficult to identify, and their impacts are concurrent with climate change. Objectives: We aim to detect the location of eco-engineering programs and attribute the impacts of eco-engineering and climate change on vegetation dynamics and carbon uptake in Northeastern China during 2000–2020. Methods: We developed a new framework to detect the location of eco-engineering programs by combining a temporal pattern analysis method and Markov model, and to attribute the impacts of eco-engineering and climate change on vegetation greenness and carbon uptake by combining a neighbor contrast method within a sliding window and trend analysis on the normalized difference vegetation index (NDVI) and gross primary production (GPP). Results: We identified four main forestry eco-engineering programs: croplands to forest (CtoF), grasslands to forest (GtoF), savannas to forest (StoF), and natural forest conservation (NFC) programs, whose areas accounted for 2.11%, 1.89%, 3.41%, and 1.72% of the total study area, respectively. Both eco-engineering and climate change contributed to the increase in greenness and carbon uptake. Compared to climate change effect, eco-engineering increased NDVI and GPP by 121% and 21.43% on average, respectively. Specifically, the eco-engineering-induced increases in GPP were 54.1%, 9.46%, 8.13%, and 24.20% for CtoF, GtoF, StoF, and NFC, respectively. Conclusions: These findings highlight the important and direct contribution of eco-engineering on vegetation greening with positive effects on carbon sequestration at a fine scale, providing an important implication for eco-engineering planning and management towards a carbon-neutral future

Lichen-Derived Actinomycetota: Novel Taxa and Bioactive Metabolites

Actinomycetes are essential sources of numerous bioactive secondary metabolites with diverse chemical and bioactive properties. Lichen ecosystems have piqued the interest of the research community due to their distinct characteristics. Lichen is a symbiont of fungi and algae or cyanobacteria. This review focuses on the novel taxa and diverse bioactive secondary metabolites identified between 1995 and 2022 from cultivable actinomycetota associated with lichens. A total of 25 novel actinomycetota species were reported following studies of lichens. The chemical structures and biological activities of 114 compounds derived from the lichen-associated actinomycetota are also summarized. These secondary metabolites were classified into aromatic amides and amines, diketopiperazines, furanones, indole, isoflavonoids, linear esters and macrolides, peptides, phenolic derivatives, pyridine derivatives, pyrrole derivatives, quinones, and sterols. Their biological activities included anti-inflammatory, antimicrobial, anticancer, cytotoxic, and enzyme-inhibitory actions. In addition, the biosynthetic pathways of several potent bioactive compounds are summarized. Thus, lichen actinomycetes demonstrate exceptional abilities in the discovery of new drug candidates