Chicken Toll-like Receptor 3 Recognizes Its Cognate Ligand When Ectopically Expressed in Human Cells

Recognition of pathogens by toll-like receptors (TLRs) causes activation of signaling cascades that trigger cytokine secretion and, ultimately, innate immunity. Genes encoding proteins with substantial homology to mammalian TLR1, TLR2, TLR3, TLR4, TLR5, and TLR7 are present in the chicken genome, whereas orthologs of TLR8, TLR9, and TLR10 seem to be defective or missing. Except for chicken TLR2 (ChTLR2), which was previously shown to recognize lipopeptides and lipopolysaccharides (LPS), the ligand specificity of ChTLRs had not been determined. We found that polyI:C, LPS, R848, S-28463, and ODN2006, which are specifically recognized by TLR3, TLR4, TLR7/8, and TLR9 in mammals, induced substantial amounts of type I interferon (IFN) and interleukin-6 (IL-6) in freshly prepared chicken splenocytes. To determine the ligand specificity of ChTLR3 and ChTLR7, we used a standard reporter assay frequently employed for analysis of mammalian TLRs. Neither S-28463 nor any other TLR ligand induced reporter activity in human 293 cells expressing ChTLR7. However, human 293 cells expressing ChTLR3 strongly and specifically responded to polyI:C, demonstrating that this chicken receptor represents a true ortholog of mammalian TLR3

DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming

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Rapid interactome profiling by massive sequencing

We have developed a high-throughput protein expression and interaction analysis platform that combines cDNA phage display library selection and massive gene sequencing using the 454 platform. A phage display library of open reading frame (ORF) fragments was created from mRNA derived from different tissues. This was used to study the interaction network of the enzyme transglutaminase 2 (TG2), a multifunctional enzyme involved in the regulation of cell growth, differentiation and apoptosis, associated with many different pathologies. After two rounds of panning with TG2 we assayed the frequency of ORFs within the selected phage population using 454 sequencing. Ranking and analysis of more than 120 000 sequences allowed us to identify several potential interactors, which were subsequently confirmed in functional assays. Within the identified clones, three had been previously described as interacting proteins (fibronectin, SMOC1 and GSTO2), while all the others were new. When compared with standard systems, such as microtiter enzyme-linked immunosorbant assay, the method described here is dramatically faster and yields far more information about the interaction under study, allowing better characterization of complex systems. For example, in the case of fibronectin, it was possible to identify the specific domains involved in the interaction

Using text mining and machine learning for detection of child abuse

Abuse in any form is a grave threat to a child's health. Public health institutions in the Netherlands try to identify and prevent different kinds of abuse, and building a decision support system can help such institutions achieve this goal. Such decision support relies on the analysis of relevant child health data. A significant part of the medical data that the institutions have on children is unstructured, and in the form of free text notes. In this research, we employ machine learning and text mining techniques to detect patterns of possible child abuse in the data. The resulting model achieves a high score in classifying cases of possible abuse. We then describe our implementation of the decision support API at a municipality in the Netherlands

Identifying child abuse through text mining and machine learning

In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on children is unstructured, found in the form of free text notes. We explore whether these consultation data contain meaningful patterns to determine abuse. Then we train machine learning models on cases of abuse as determined by over 500 child specialists from a municipality in The Netherlands. The resulting model achieves a high score in classifying cases of possible abuse. We methodologically evaluate and compare the performance of the classifiers. We then describe our implementation of the decision support API at a municipality in the Netherlands

DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming

The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%

Predicting Perceived Training Load in Professional Football

Sport injuries are most often caused by overstraining. Injuries not only have an impact on the quality of life of athletes but can also incur high costs to sports clubs, due to the players’ absence.  The main goal is to have a tool, which can advise trainers to optimise training per individual athlete in order to reach peak performace and reduce injuries

Additional file 1: of Review of biomarkers in systemic juvenile idiopathic arthritis: helpful tools or just playing tricks?

Full summary of included studies. (DOCX 153 kb