Serological research of Salmonella on Belgian pig farms

Risk factors for Salmonellosis in pigs were investigated in a cross-sectional study on 144 Belgian farrow-to-finish herds belonging to one slaughterhouse co-operation. Herd data were collected using a questionnaire. The blood samples were serologically analyzed. Variables significantly related to the Salmonella prevalence in the univariate analyses were subsequently analysed in a multivariate model. Furthermore, the clustering of Salmonella infection within the herd, section and pen was studied. The average within-herd seroprevalence was: 73.4% when using OD 10%. In the multivariate analyses the structure of the feed seems to be the most important factor of the model with five factors. Feeding pigs meal instead of granulated or crumb is a protecting factor for Salmonella. Other risk factors in the multivariate model are natural ventilation, less then 3 days emptiness after wet cleansing, not dry cleansing of sows’ pen before wet cleansing and the absence of dogs in the pig houses

Start Time and Duration Distribution Estimation in Semi-Structured Processes

Semi-structured processes are business workflows, where the execution of the workflow is not completely controlled by a workflow engine, i.e., an implementation of a formal workflow model. Examples are workflows where actors potentially have interaction with customers reporting the result of the interaction in a process aware information system. Building a performance model for resource management in these processes is difficult since the required information is only partially recorded. In this paper we propose a systematic approach for the creation of an event log that is suitable for available process mining tools. This event log is created by an incrementally cleansing of data. The proposed approach is evaluated in an experiment

Pileup Mitigation with Machine Learning (PUMML)

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.Comment: 20 pages, 8 figures, 2 tables. Updated to JHEP versio

ESTpass: a web-based server for processing and annotating expressed sequence tag (EST) sequences

We present a web-based server, called ESTpass, for processing and annotating sequence data from expressed sequence tag (EST) projects. ESTpass accepts a FASTA-formatted EST file and its quality file as inputs, and it then executes a back-end EST analysis pipeline consisting of three consecutive steps. The first is cleansing the input EST sequences. The second is clustering and assembling the cleansed EST sequences using d2_cluster and CAP3 programs and producing putative transcripts. From the CAP3 output, ESTpass detects chimeric EST sequences which are confirmed through comparison with the nr database. The last step is annotating the putative transcript sequences using RefSeq, InterPro, GO and KEGG gene databases according to user-specified options. The major advantages of ESTpass are the integration of cleansing and annotating processes, rigorous chimeric EST detection, exhaustive annotation, and email reporting to inform the user about the progress and to send the analysis results. The ESTpass results include three reports (summary, cleansing and annotation) and download function, as well as graphic statistics. They can be retrieved and downloaded using a standard web browser. The server is available at http://estpass.kobic.re.kr/