First Insight into the Variation of the Milk Serum Proteome within and between Individual Cows

Milk contains all nutrients needed for development of calves. One important group of components responsible for this are the milk proteins. Variation due to feed or animal health, has been studied for the most abundant milk proteins. The aim of this study was to determine the variation between and within cows for their milk serum proteome. Sample Set 1 was collected from Holstein Friesian (HF) cows between November 2011 and March 2012 and prepared using filter aided sample preparation (FASP) followed by LC-MS/MS for protein identification and quantification. The results showed that the milk serum proteome was very constant in mid lactation (four cows at five time points, p > 0.05) between 3 and 6 months in lactation. Sample Set 2 was collected from HF cows in Dec 2012 and analyzed using FASP and dimethyl labeling followed by LC-MS/MS. Significant variation in the milk serum proteome (p < 0.05) between 17 individual cows was found in Sample Set 2. The most variable proteins were immune-related proteins, which may reflect the health status of the individual cow. On the other hand, proteins related to nutrient synthesis and transport were relatively constant, indicating the importance of milk in providing a stable supply of nutrients to the neonate. In conclusion, the milk serum proteome was stable over mid lactation, but differed significantly between individuals, especially in immune-related proteins.

Comment on ‘SARS-CoV-2 suppresses anticoagulant and fibrinolytic gene expression in the lung’

Mast et al. analyzed transcriptome data derived from RNA-sequencing (RNA-seq) of COVID-19 patient bronchoalveolar lavage fluid (BALF) samples, as compared to BALF RNA-seq samples from a study investigating microbiome and inflammatory interactions in obese and asthmatic adults (Mast et al., 2021). Based on their analysis of these data, Mast et al. concluded that mRNA expression of key regulators of the extrinsic coagulation cascade and fibrinolysis were significantly reduced in COVID-19 patients. Notably, they reported that the expression of the extrinsic coagulation cascade master regulator Tissue Factor (F3) remained unchanged, while there was an 8-fold upregulation of its cognate inhibitor Tissue Factor Pathway Inhibitor (TFPI). From this they conclude that “pulmonary fibrin deposition does not stem from enhanced local [tissue factor] production and that counterintuitively, COVID-19 may dampen [tissue factor]-dependent mechanisms in the lungs”. They also reported decreased Activated Protein C (aPC) mediated anticoagulant activity and major increases in fibrinogen expression and other key regulators of clot formation. Many of these results are contradictory to findings in most of the field, particularly the findings regarding extrinsic coagulation cascade mediated coagulopathies. Here, we present a complete re-analysis of the data sets analyzed by Mast et al. This re-analysis demonstrates that the two data sets utilized were not comparable between one another, and that the COVID-19 sample set was not suitable for the transcriptomic analysis Mast et al. performed. We also identified other significant flaws in the design of their retrospective analysis, such as poor-quality control and filtering standards. Given the issues with the datasets and analysis, their conclusions are not supported.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

Pokémon GO as a Cognitive and Societal Development Tool for Personalised Learning

The present research develops and tests a theoretical gamification model (GM) that explores the use of mobile learning (ML) and massively multiplayer online (MMO) games to strengthen group prospection of teams and improve retention. The GM used Pokémon GO to enable higher education students to engage in activities and challenges with a view to observe the impacts on health and wellbeing through collection of quantitative and qualitative data. The data that was collected involved a sample set (N = 50) of participants within the general educational sector. The model constructs were measured throughout the first academic semester, from September 2018 to February 2019. There is significant evidence to show that the use of ML in the classroom is beneficial depending on the influences from and engagement with participants. The contributions from these findings should provide the basics for further research into different studies involving MMOs and ML or gamification studies.

Pokémon GO as a Cognitive and Societal Development Tool for Personalised Learning

The present research develops and tests a theoretical gamification model (GM) that explores the use of mobile learning (ML) and massively multiplayer online (MMO) games to strengthen group prospection of teams and improve retention. The GM used Pokémon GO to enable higher education students to engage in activities and challenges with a view to observe the impacts on health and wellbeing through collection of quantitative and qualitative data. The data that was collected involved a sample set (N = 50) of participants within the general educational sector. The model constructs were measured throughout the first academic semester, from September 2018 to February 2019. There is significant evidence to show that the use of ML in the classroom is beneficial depending on the influences from and engagement with participants. The contributions from these findings should provide the basics for further research into different studies involving MMOs and ML or gamification studies.

The Mixed-Body Model: A Method for Predicting Large Deflections in Stepped Cantilever Beams

This paper presents a method for predicting endpoint coordinates, stress, and force to deflect stepped cantilever beams under large deflections. This method, the Mixed-Body Model or MBM, combines small deflection theory and the Pseudo-Rigid-Body Model for large deflections. To analyze the efficacy of the model, the MBM is compared to a model that assumes the first step in the beam to be rigid, to finite element analysis, and to the numerical boundary value solution over a large sample set of loading conditions, geometries, and material properties. The model was also compared to physical prototypes. In all cases, the MBM agrees well with expected values. Optimization of the MBM parameters yielded increased agreement, leading to average errors of &lt;0.01 to 3%. The model provides a simple, quick solution with minimal error that can be particularly helpful in design.

Image analysis of real-time classification of cherry fruit from colour features

An image analysis algorithm for the classification of cherries in real time by processing their digitalized colour images was developed, and tested. A set of five digitalized images of colour pattern, corresponding to five colour classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the coloured area of the fruit. A histogram analysis was carried out for the RGB and HSV colour spaces, where the Red and Hue components showed differences between each of the specified colour patterns of the exporting reference system. This information led to the development of a hybrid Bayesian classification algorithm based on the components R and H. Its accuracy was tested with a set of cherry samples within the colour range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm showed a 100% effectiveness in classifying a sample set of cherries into the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, thus ensuring an affordable commercial deployment.

Unique SARS-CoV-2 variant found in public sequence data of Antarctic soil samples collected in 2018-2019

The COVID-19 pandemic has been going on for two years now and although many hypotheses have been put forward, its origin remain obscure. We investigated whether the huge public sequencing data archives’ samples collected earlier than the earliest known cases of the pandemic might contain traces of SARS-CoV-2. Here we report the bioinformatic analysis of a metagenome sample set collected from soil on King George Island, Antarctica between 2018-12-24 and 2019-01-13. It contains sequence fragments matching the SARS-CoV-2 reference genome with altogether more than half million nucleotides, covering the complete genome on average 17×. Preliminary phylogeny analysis places the sample close to the known earliest cases. The high sequence coverage rules out chance alignments from other species but possible laboratory contamination cannot be excluded. The sequence harbours a unique combination of mutations, unseen in other samples, so whatever its origin, it can add important piece of information to the puzzle of the ongoing pandemic.

Performance of Cleanroom Garment Fabrics When Processed in an Industrial Laundry, Comparing Irradiation and Autoclave Sterilisation

EU GMP Annex 1 requires that “reusable garments should be replaced based at a set frequency determined by qualification, or if damage is identified.” [1] In the UK, most cleanroom garments supplied to the pharmaceutical and healthcare sectors are washed and sterilised by gamma irradiation. This study compares cleanroom garment fabric performance across the lifespan of multiple fabrics. Previous research has shown that cleanroom garment fabrics terminally sterilised by gamma irradiation remain suitable for use for up to 50 processes, however, these studies often focus on a limited number of samples. This study uses a large sample set, analysing the performance up to 100 processes and compares the performance effects of gamma irradiation vs autoclaving, as an alternative sterilisation method. Multiple market leading cleanroom garment fabrics were washed and dried using a standard industrial cleanroom laundry process and sterilised by either gamma irradiation or autoclave. They were tested for particle barrier efficiency, abrasion resistance, pore size, and tensile strength as new, then at set process counts throughout their life, 10, 20, 30, 50, 70 and 100 processes. A process is equal to one wash/dry/sterilisation cycle. The results show that not all cleanroom garment fabrics deteriorate equally and that some market leading fabrics may not provide adequate performance throughout life, even if they are suitable when new. They also show that autoclaving is comparable with irradiation in durability and performance over a fabric’s life, in some cases performing better than irradiation above process counts of 50.

The Effect of Service Quality and Customer Relationship Management Towards Customer Loyalty

This study aims to determine the effect of service quality and customer relationship management on customer loyalty at GTT Café Mojokerto. The population used is all consumers of GTT Café Mojokerto who have visited more than once, while the sample set by the researcher is 124 consumers who have visited at least twice. Data collection techniques in this study were taken by distributing questionnaires to customers who were there and measured based on a Likert scale. The variables used are service quality and customer relationship management as independent variables and customer loyalty as the dependent variable. Furthermore, the researcher processed the data using the SPSS 25 program to obtain significant results in the study. Based on the results of the research analysis, it is obtained the hypothesis that service quality has an effect on customer loyalty and customer relationship management has an effect on customer loyalty and the two variables simultaneously influence customer loyalty so that it can be said that all independent variables influence the dependent variable partially or simultaneously.