Schmitt, Armin

[["dc.bibliographiccitation.firstpage","581"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","AgriEngineering"],["dc.bibliographiccitation.lastpage","595"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Wutke, Martin"],["dc.contributor.author","Traulsen, Imke"],["dc.contributor.author","Gültas, Mehmet"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.date.accessioned","2021-05-17T12:19:04Z"],["dc.date.accessioned","2021-10-27T13:20:03Z"],["dc.date.available","2021-05-17T12:19:04Z"],["dc.date.available","2021-10-27T13:20:03Z"],["dc.date.issued","2020"],["dc.description.abstract","The activity level of pigs is an important stress indicator which can be associated to tail-biting, a major issue for animal welfare of domestic pigs in conventional housing systems. Although the consideration of the animal activity could be essential to detect tail-biting before an outbreak occurs, it is often manually assessed and therefore labor intense, cost intensive and impracticable on a commercial scale. Recent advances of semi- and unsupervised convolutional neural networks (CNNs) have made them to the state of art technology for detecting anomalous behavior patterns in a variety of complex scene environments. In this study we apply such a CNN for anomaly detection to identify varying levels of activity in a multi-pen problem setup. By applying a two-stage approach we first trained the CNN to detect anomalies in the form of extreme activity behavior. Second, we trained a classifier to categorize the detected anomaly scores by learning the potential activity range of each pen. We evaluated our framework by analyzing 82 manually rated videos and achieved a success rate of 91%. Furthermore, we compared our model with a motion history image (MHI) approach and a binary image approach using two benchmark data sets, i.e., the well established pedestrian data sets published by the University of California, San Diego (UCSD) and our pig data set. The results show the effectiveness of our framework, which can be applied without the need of a labor intense manual annotation process and can be utilized for the assessment of the pig activity in a variety of applications like early warning systems to detect changes in the state of health."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.3390/agriengineering2040039"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17775"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91934"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2624-7402"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","630"],["dc.title","Investigation of Pig Activity Based on Video Data and Semi-Supervised Neural Networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0216475"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Steuernagel, Lukas"],["dc.contributor.author","Meckbach, Cornelia"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Zeidler, Sebastian"],["dc.contributor.author","Schmitt, Armin O."],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2019-07-09T11:51:30Z"],["dc.date.available","2019-07-09T11:51:30Z"],["dc.date.issued","2019"],["dc.description.abstract","Transcription factors (TFs) are a special class of DNA-binding proteins that orchestrate gene transcription by recruiting other TFs, co-activators or co-repressors. Their combinatorial interplay in higher organisms maintains homeostasis and governs cell identity by finely controlling and regulating tissue-specific gene expression. Despite the rich literature on the importance of cooperative TFs for deciphering the mechanisms of individual regulatory programs that control tissue specificity in several organisms such as human, mouse, or Drosophila melanogaster, to date, there is still need for a comprehensive study to detect specific TF cooperations in regulatory processes of cattle tissues. To address the needs of knowledge about specific combinatorial gene regulation in cattle tissues, we made use of three publicly available RNA-seq datasets and obtained tissue-specific gene (TSG) sets for ten tissues (heart, lung, liver, kidney, duodenum, muscle tissue, adipose tissue, colon, spleen and testis). By analyzing these TSG-sets, tissue-specific TF cooperations of each tissue have been identified. The results reveal that similar to the combinatorial regulatory events of model organisms, TFs change their partners depending on their biological functions in different tissues. Particularly with regard to preferential partner choice of the transcription factors STAT3 and NR2C2, this phenomenon has been highlighted with their five different specific cooperation partners in multiple tissues. The information about cooperative TFs could be promising: i) to understand the molecular mechanisms of regulating processes; and ii) to extend the existing knowledge on the importance of single TFs in cattle tissues."],["dc.identifier.doi","10.1371/journal.pone.0216475"],["dc.identifier.pmid","31095599"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16139"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59959"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","630"],["dc.title","Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","106051"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Data in Brief"],["dc.bibliographiccitation.lastpage","4"],["dc.bibliographiccitation.volume","32"],["dc.contributor.author","Ramzan, Faisal"],["dc.contributor.author","Khan, Muhammad Sajjad"],["dc.contributor.author","Bhatti, Shaukat Ali"],["dc.contributor.author","Gültas, Mehmet"],["dc.contributor.author","Schmitt, Armin O."],["dc.date.accessioned","2020-08-26T10:01:52Z"],["dc.date.accessioned","2021-10-27T13:11:26Z"],["dc.date.available","2020-08-26T10:01:52Z"],["dc.date.available","2021-10-27T13:11:26Z"],["dc.date.issued","2020"],["dc.description.abstract","This article presents raw data from a survey conducted to identify the selection criteria of breeders raising either of four strains of Beetal goats, namely Beetal Faisalabadi, Beetal Makhi-Cheeni, Beetal Nuqri, and Beetal Rahim Yar Khan. After a pre-survey, a questionnaire was developed and a survey was conducted at four sites of the Punjab province of Pakistan: Faisalabad/Sahiwal, Bahawalpur/Bahawalnagar, Rajanpur, and Rahim Yar Khan. Each of these sites was the home tract of one strain. During the survey breeders (n = 162) were asked to rank the traits of their selection criteria based on the relative importance of those traits. Furthermore, the prevailing production system was also characterized by the breeders. For the interpretation of the results of this survey the readers are referred to Ref. [1]. The raw data set provided in this article can be extended in the future to include more strains of Beetal goats as well as other goat breeds. The selection criteria of breeders can change over time. This data set can also be used in future studies to investigate the temporal changes in the relative importance of different traits for the breeders. The factors potentially influencing those changes can also be investigated. This data set can further be utilized to design community based breeding plans tailored to the needs of the goat farming community."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.1016/j.dib.2020.106051"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17522"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91596"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.issn","2352-3409"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","Selection criteria; Beetal goats; Strains; Participatory approach; Survey questionnaire; Ranking"],["dc.subject.ddc","630"],["dc.title","Survey data to identify the selection criteria used by breeders of four strains of Pakistani beetal goats"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]