Individual Pig Identification Using Back Surface Point Clouds in 3D Vision.

The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig's back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig's back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.

One-step synthesis of triethanolamine-capped Pt nanoparticle for colorimetric and electrochemiluminescent immunoassay of SARS-CoV spike proteins.

Platinum nanoparticles (PtNPs) have been attracted worldwide attention due to their versatile application potentials, especially in the catalyst and sensing fields. Herein, a facile synthetic method of triethanolamine (TEOA)-capped PtNPs (TEOA@PtNP) for electrochemiluminescent (ECL) and colorimetric immunoassay of SARS-CoV spike proteins (SARS-CoV S-protein, a target detection model) is developed. Monodisperse PtNPs with an average diameter of 2.2 nm are prepared by a one-step hydrothermal synthesis method using TEOA as a green reductant and stabilizer. TEOA@PtNPs can be used as a nanocarrier to combine with antigen by the high-affinity antibody, which leads to a remarkable inhibition of electron transfer efficiency and mass transfer processes. On the basis of its peroxidase-like activity and easy-biolabeling property, the TEOA@PtNP can be used to establish a colorimetric immunosensor of SARS-CoV S-protein thought catalyzing the reaction of H2O2 and 3,3',5,5'-tetramethylbenzidine (TMB). Especially, the Ru(bpy)3 2+ ECL reaction is well-achieved with the TEOA@PtNPs due to their great conductivity and loading abundant TEOA co-reactants, resulting in an enhancing ECL signal in immunoassay of SARS-CoV S-protein. As a consequence, two proposed methods could achieve sensitive detection of SARS-CoV S-protein in wide ranges, the colorimetric and ECL detection limits were as low as 8.9 fg /mL and 4.2 fg /mL (S/N = 3), respectively. We believe that the proposed colorimetric and ECL immunosesors with high sensitivity, good reproducibility, and good stability will be a promising candidate for a broad spectrum of applications.

Purification and characterization of a novel wild-type α-amylase from Antarctic sea ice bacterium Pseudoalteromonas sp. M175.

A novel wild-type α-amylase named wtAmy175 from Pseudoalteromonas sp. M175 strain was purified through ammonium sulphate precipitation, DEAE cellulose, and Sephadex G-75 sequentially (25.83-fold, 7.67%-yield) for biochemical characterization. SDS-PAGE and zymographic activity staining of purified enzyme showed a single band with a predicted molecular mass of about 61 kDa. The optimum temperature and pH for enzyme activity were 30 °C and 7.5, respectively. Additionally, the enzyme exhibited high activity and remarkable stability in 0-10 mM SDS. The values of Km and Vmax for soluble starch as substrate were 2.47 mg/ml and 0.103 mg/ml/min, respectively. Analysis of hydrolysis products of soluble starch and maltooligosaccharides showed that wtAmy175 cleaved the interior and the terminal α-1,4-glycosidic linkage in starch, and had transglycosylation activity. The result of fluorescence spectroscopy showed that wtAmy175 had strong binding affinity with soluble starch. In brief, this study discovered the first wild-type α-amylase so far with several distinctive properties of cold activity, SDS-resistance, and the mixed activity of α-amylase and α-glucosidase, suggesting that wtAmy175 possess high adaptive capability to endure harsh industrial conditions and would be an excellent candidate in detergent and textile industries.

Molecular Cloning and Characterization of a Novel α-Amylase from Antarctic Sea Ice Bacterium Pseudoalteromonas sp. M175 and Its Primary Application in Detergent.

A novel cold-adapted and salt-tolerant α-amylase gene (amy175) from Antarctic sea ice bacterium Pseudoalteromonas sp. M175 was successfully cloned and expressed. The open reading frame (ORF) of amy175 had 1722 bp encoding a protein of 573 amino acids residues. Multiple alignments indicated Amy175 had seven highly conserved sequences and the putative catalytic triad (Asp244, Glu286, and Asp372). It was the first identified member of GH13_36 subfamily which contained QPDLN in the CSR V. The recombinant enzyme (Amy175) was purified to homogeneity with a molecular mass of about 62 kDa on SDS-PAGE. It had a mixed enzyme specificity of α-amylase and α-glucosidase. Amy175 displayed highest activity at pH 8.0 and 25°C and exhibited extreme salt-resistance with the maximum activity at 1 M NaCl. Amy175 was strongly stimulated by Mg2+, Ni2+, K+, 1 mM Ca2+, 1 mM Ba2+, 1 mM Pb2+, 1 mM sodium dodecyl sulphate (SDS), and 10% dimethyl sulfoxide (DMSO) but was significantly inhibited by Cu2+, Mn2+, Hg2+, 10 mM ß-mercaptoethanol (ß-ME), and 10% Tween 80. Amy175 demonstrated excellent resistance towards all the tested commercial detergents, and wash performance analysis displayed that the addition of Amy175 improved the stain removal efficiency. This study demonstrated that Amy175 would be proposed as a novel α-amylase source for industrial application in the future.

Fuzzy comprehensive evaluation of multiple environmental factors for swine building assessment and control.

In confined swine buildings, temperature, humidity, and air quality are all important for animal health and productivity. However, the current swine building environmental control is only based on temperature; and evaluation and control methods based on multiple environmental factors are needed. In this paper, fuzzy comprehensive evaluation (FCE) theory was adopted for multi-factor assessment of environmental quality in two commercial swine buildings using real measurement data. An assessment index system and membership functions were established; and predetermined weights were given using analytic hierarchy process (AHP) combined with knowledge of experts. The results show that multi-factors such as temperature, humidity, and concentrations of ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) can be successfully integrated in FCE for swine building environment assessment. The FCE method has a high correlation coefficient of 0.737 compared with the method of single-factor evaluation (SFE). The FCE method can significantly increase the sensitivity and perform an effective and integrative assessment. It can be used as part of environmental controlling and warning systems for swine building environment management to improve swine production and welfare.

A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system.

Ammonia (NH3) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could provide a reasonable way for pig industries and environment regulatory to determine environment control strategies and give an effective method to evaluate the air quality. The adaptive neuro fuzzy inference system (ANFIS) simulates human's vague thinking manner to solve the ambiguity and nonlinear problems which are difficult to be processed by conventional mathematics. Five kinds of membership functions were used to build a well fitted ANFIS prediction model. It was shown that the prediction model with "Gbell" membership function had the best capabilities among those five kinds of membership functions, and it had the best performances compared with backpropagation (BP) neuro network model and multiple linear regression model (MLRM) both in wintertime and summertime, the smallest value of mean square error (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the largest coefficients of determination (R2) are 0.6351 and 0.6483, repectively. The ANFIS prediction model could be served as a beneficial strategy for the environment control system that has input parameters with highly fluctuating, complexity, and non-linear relationship.