Effects of dietary yeast beta-1,3-1,6-glucan on growth performance, intestinal morphology and chosen immunity parameters changes in Haidong chicks

Objective: This study investigated the effects of 1,3-1,6 beta-glucan added to the diet of Haidong chicks reared under hypoxic conditions, to ascertain the growth performances, immunity and intestinal morphology changes. Methods: A total of 750 chicks were divided into five groups and fed diets containing 0.5g/kg, 1.0g/kg and 2.0g/kg 1,3-1,6 beta-glucan from yeast (G1, G2, G3, respectively), 0.2g/kg Taylor rhizomorph (T) and a control feed (C). Results: The body weight and body weight gain was higher in chicks fed 1,3-1,6 beta-glucan and Taylor rhizomorph than in control group. Feed conversion ratio significantly differed for G2 and G3 groups in comparison to control group. The relative weight of bursa was higher in G1, G2 and G3 groups. The white blood cells and lymphocytes were significantly increased in groups fed 1,3-1,6 beta-glucan. The IgG of serum peak appeared in the G3 group. The villous height of the duodenum was higher in 1,3-1,6 beta-glucan feed groups. In the jejunum, the villous height was higher in G2 and G3 groups and crypt depth for all the groups fed β-glucan. At ileum level the villous height and crept depth was higher for groups G1, G2 and G3. Conclusion: The growth performance of Haidong chicks is improved when 10 and 20 g/kg 1,3-1,6 beta-glucan is included in the diet; hence, it is suggested to diet include 1,3-1,6 beta-glucan in poultry diet to reduce and replace the use of antibiotics

Comprehensive evaluation of extracellular small RNA isolation methods from serum in high throughput sequencing

Supplementary Tables S1-S9. (XLSX 576 kb

quantwiz: a scalable parallel software package for label-free protein quantification

IEEE Beijing Section; Hunan University; Liverpool Hope University; Peking University; National Natural Science Foundation of ChinaIn the context of the prosperous development of Proteomics in life science, protein quantification, especially these based on Mass Spectrometry (short for MS) method, becomes an essential part of research. In our previous work, we developed a new software package called QuantWiz for high performance Liquid Chromatography (short for LC)-MS-based label-free protein quantification. We solved those problems of portability, applicability and longtime running existed in other software for protein quantification based on MS method. In this paper, we first compared the LC-MS-based label-free protein quantification accuracy of QuantWiz with the well-known Census software package. Then we designed and implemented a distributed memory version parallel algorithm for QuantWiz. Finally, we performed scalability testing of our new parallel algorithm and showed that our new parallel algorithm can scale up to 512 processes on Dawning 5000A

Explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers’ confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification

A novel reconstruction algorithm based on density clustering for cosmic-ray muon scattering inspection

As a relatively new radiation imaging method, the cosmic-ray muon scattering imaging technology can be used to prevent nuclear smuggling and is of considerable significance to nuclear safety. Proposed in this paper is a new reconstruction algorithm based on density clustering, aiming to improve inspection quality with better performance. Firstly, this new algorithm is introduced in detail. Then in order to eliminate the inequity of the density threshold caused by the heterogeneity of the muon flux in different positions, a new flux correction method is proposed. Finally, three groups of simulation experiments are carried out with the help of Geant4 toolkit to optimize the algorithm parameters, verify the correction method and test the inspection quality under shielded condition, and compare this algorithm with another common inspection algorithm under different conditions. The results show that this algorithm can effectively identify and locate nuclear material with low misjudging and missing rates even when there is shielding and momentum precision is low, and the threshold correcting method is universally effective for density clustering algorithms

Systematic Assessment of Survey Scan and MS2-Based Abundance Strategies for Label-Free Quantitative Proteomics Using High-Resolution MS Data

Survey-scan-based label-free method have shown no compelling benefit over fragment ion (MS2)-based approaches when low-resolution mass spectrometry (MS) was used, the growing prevalence of high-resolution analyzers may have changed the game. This necessitates an updated, comparative investigation of these approaches for data acquired by high-resolution MS. Here, we compared survey scan-based (ion current, IC) and MS2-based abundance features including spectral-count (SpC) and MS2 total-ion-current (MS2-TIC), for quantitative analysis using various high-resolution LC/MS data sets. Key discoveries include: (i) study with seven different biological data sets revealed only IC achieved high reproducibility for lower-abundance proteins; (ii) evaluation with 5-replicate analyses of a yeast sample showed IC provided much higher quantitative precision and lower missing data; (iii) IC, SpC, and MS2-TIC all showed good quantitative linearity (<i>R</i>² > 0.99) over a >1000-fold concentration range; (iv) both MS2-TIC and IC showed good linear response to various protein loading amounts but not SpC; (v) quantification using a well-characterized CPTAC data set showed that IC exhibited markedly higher quantitative accuracy, higher sensitivity, and lower false-positives/false-negatives than both SpC and MS2-TIC. Therefore, IC achieved an overall superior performance than the MS2-based strategies in terms of reproducibility, missing data, quantitative dynamic range, quantitative accuracy, and biomarker discovery

Systematic Assessment of Survey Scan and MS2-Based Abundance Strategies for Label-Free Quantitative Proteomics Using High-Resolution MS Data

Survey-scan-based label-free method have shown no compelling benefit over fragment ion (MS2)-based approaches when low-resolution mass spectrometry (MS) was used, the growing prevalence of high-resolution analyzers may have changed the game. This necessitates an updated, comparative investigation of these approaches for data acquired by high-resolution MS. Here, we compared survey scan-based (ion current, IC) and MS2-based abundance features including spectral-count (SpC) and MS2 total-ion-current (MS2-TIC), for quantitative analysis using various high-resolution LC/MS data sets. Key discoveries include: (i) study with seven different biological data sets revealed only IC achieved high reproducibility for lower-abundance proteins; (ii) evaluation with 5-replicate analyses of a yeast sample showed IC provided much higher quantitative precision and lower missing data; (iii) IC, SpC, and MS2-TIC all showed good quantitative linearity (<i>R</i>² > 0.99) over a >1000-fold concentration range; (iv) both MS2-TIC and IC showed good linear response to various protein loading amounts but not SpC; (v) quantification using a well-characterized CPTAC data set showed that IC exhibited markedly higher quantitative accuracy, higher sensitivity, and lower false-positives/false-negatives than both SpC and MS2-TIC. Therefore, IC achieved an overall superior performance than the MS2-based strategies in terms of reproducibility, missing data, quantitative dynamic range, quantitative accuracy, and biomarker discovery

Systematic Assessment of Survey Scan and MS2-Based Abundance Strategies for Label-Free Quantitative Proteomics Using High-Resolution MS Data

Survey-scan-based label-free method have shown no compelling benefit over fragment ion (MS2)-based approaches when low-resolution mass spectrometry (MS) was used, the growing prevalence of high-resolution analyzers may have changed the game. This necessitates an updated, comparative investigation of these approaches for data acquired by high-resolution MS. Here, we compared survey scan-based (ion current, IC) and MS2-based abundance features including spectral-count (SpC) and MS2 total-ion-current (MS2-TIC), for quantitative analysis using various high-resolution LC/MS data sets. Key discoveries include: (i) study with seven different biological data sets revealed only IC achieved high reproducibility for lower-abundance proteins; (ii) evaluation with 5-replicate analyses of a yeast sample showed IC provided much higher quantitative precision and lower missing data; (iii) IC, SpC, and MS2-TIC all showed good quantitative linearity (<i>R</i>² > 0.99) over a >1000-fold concentration range; (iv) both MS2-TIC and IC showed good linear response to various protein loading amounts but not SpC; (v) quantification using a well-characterized CPTAC data set showed that IC exhibited markedly higher quantitative accuracy, higher sensitivity, and lower false-positives/false-negatives than both SpC and MS2-TIC. Therefore, IC achieved an overall superior performance than the MS2-based strategies in terms of reproducibility, missing data, quantitative dynamic range, quantitative accuracy, and biomarker discovery

Efficiency calculation of the nMCP with 10B doping based on mathematical models

The nMCP (Neutron sensitive microchannel plate) combined with advanced readout electronics is widely used in energy selective neutron imaging because of its good spatial and timing resolution. Neutron detection efficiency is a crucial parameter for the nMCP. In this paper, a mathematical model based on the oblique cylindrical channel and elliptical pore was established to calculate the neutron absorption probability, the escape probability of charged particles and overall detection efficiency of nMCP and analyze the effects of neutron incident position, pore diameter, wall thickness and bias angle. It was shown that when the doping concentration of the nMCP was 10 mol%, the thickness of nMCP was 0.6 mm, the detection efficiency could reach maximum value, about 24% for thermal neutrons if the pore diameter was 6 μm, the wall thickness was 2 μm and the bias angle was 3 or 6°. The calculated results are of great significance for evaluating the detection efficiency of the nMCP. In a subsequent companion paper, the mathematical model would be extended to the case of the spatial resolution and detection efficiency optimization of the coating nMCP