Dan Tulpan

University of Guelph

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Christine F. Baes

University of Guelph

Flávio S. Schenkel

University of Guelph

F. Miglior

University of Guelph

Paola Lecca

Free University of Bozen-Bolzano

Saeed Shadpour

University of Guelph

J.L. Ellis

University of Guelph

Hinayah Rojas de Oliveira

Purdue University West Lafayette

Graham Plastow

University of Alberta

Jihao You

University of Guelph

J. Lassen

Aarhus University

Cooperative Institutions

University of Guelph

University of British Columbia

National Research Council Canada

Agriculture and Agri-Food Canada

National Academies of Sciences, Engineering, and Medicine

University of Alberta

Université Laval

Simon Fraser University

National Engineering Research Center for Information Technology in Agriculture

Universidade de São Paulo

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Non-intrusive Patient Monitoring of Alzheimer's Disease Subjects Using Wireless Sensor Networks

Marco Avvenuti Christopher Baker Janet Light Dan Tulpan Alessio Vecchio

On aging there is a decrease in the cognitive functions of the brain which can result in behavioral anomalies such as wandering and susceptibility to fall, typical of patients with Alzheimer's disease. In order to learn how to manage patients with cognitive impairment it is necessary to non-intrusively monitor brain activity in conjunction with body movements. To facilitate the translation of insights derived through wireless monitoring into robust strategies for crisis prevention and management, we provide a preliminary assessment of a patient monitoring infrastructure, and we discuss related issues and challenges.

Remote patient monitoring

10.1109/congress.2009.25

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Citations (35)

5 Hands-on 3: Introduction to developing Python computational pipelines for predictive machine learning modelling of livestock data

Journal of Animal Science (2024)

Dan Tulpan

Abstract This hands-on workshop aims to offer examples of computational pipelines that apply traditional predictive machine learning modelling techniques to animal science datasets with the purpose of solving classification and/or regression problems. The objective of this workshop is to provide the attendants with fully functional and customizable computational pipelines developed in Python. The workshop is structured in a hands-on format and includes a combination of basic notions about machine learning and relevant algorithms, evaluation measures, evaluation strategies and Python code examples. To avoid technical problems related to installing a Python environment on personal computers, we recommend the registrants to acquire access on the Repl.it platform (https://replit.com/) by creating a free account prior to attending the workshop. Detailed information will be provided before the beginning of the workshop at the following URL: http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2024/

Python

10.1093/jas/skae234.076

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Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

Zenodo (CERN European Organization for Nuclear Research) (2022)

Saeed Shadpour T.C.S. Chud Dagnachew Hailemariam Hinayah Rojas de Oliveira Graham Plastow

Supplementary Tables

10.5281/zenodo.6632059

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Enrichment of Triticum aestivum gene annotations using ortholog cliques and gene ontologies in other plants

BMC Genomics (2015)

Dan Tulpan Serge Léger Alain Tchagang Youlian Pan

While the gargantuan multi-nation effort of sequencing T. aestivum gets close to completion, the annotation process for the vast number of wheat genes and proteins is in its infancy. Previous experimental studies carried out on model plant organisms such as A. thaliana and O. sativa provide a plethora of gene annotations that can be used as potential starting points for wheat gene annotations, proven that solid cross-species gene-to-gene and protein-to-protein correspondences are provided. DNA and protein sequences and corresponding annotations for T. aestivum and 9 other plant species were collected from Ensembl Plants release 22 and curated. Cliques of predicted 1-to-1 orthologs were identified and an annotation enrichment model was defined based on existing gene-GO term associations and phylogenetic relationships among wheat and 9 other plant species. A total of 13 cliques of size 10 were identified, which represent putative functionally equivalent genes and proteins in the 10 plant species. Eighty-five new and more specific GO terms were associated with wheat genes in the 13 cliques of size 10, which represent a 65% increase compared with the previously 130 known GO terms. Similar expression patterns for 4 genes from Arabidopsis, barley, maize and rice in cliques of size 10 provide experimental evidence to support our model. Overall, based on clique size equal or larger than 3, our model enriched the existing gene-GO term associations for 7,838 (8%) wheat genes, of which 2,139 had no previous annotation. Our novel comparative genomics approach enriches existing T. aestivum gene annotations based on cliques of predicted 1-to-1 orthologs, phylogenetic relationships and existing gene ontologies from 9 other plant species.

Gene Annotation

Ensembl

Comparative Genomics

Gene prediction

Functional Genomics

10.1186/s12864-015-1496-2

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The Plant Orthology Browser: An Orthology and Gene‐Order Visualizer for Plant Comparative Genomics

The Plant Genome (2017)

Dan Tulpan Serge Léger

Worldwide genome sequencing efforts for plants with medium and large genomes require identification and visualization of orthologous genes, while their syntenic conservation becomes the pinnacle of any comparative and functional genomics study. Using gene models for 20 fully sequenced plant genomes, including model organisms and staple crops such as Aegilops tauschii Coss., Arabidopsis thaliana (L.) Heynh., Brachypodium distachyon (L.) Beauv., turnip ( Brassica rapa L.), barley ( Hordeum vulgare L.), rice ( Oryza sativa L.), sorghum [ Sorghum bicolor (L.) Moench], wheat ( Triticum aestivum L.), red wild einkorn ( Triticum urartu Tumanian ex Gandilyan), and maize ( Zea mays L.), we computationally predicted 1,021,611 orthologs using stringent sequence similarity criteria. For each pair of plant species, we determined sets of conserved synteny blocks using strand orientation and physical mapping. Gene ontology (GO) annotations are added for each gene. Plant Orthology Browser (POB) includes three interconnected modules: (i) a gene‐order visualization module implementing an interactive environment for exploration of gene order between any pair of chromosomes in two plant species, (ii) a synteny visualization module providing unique interactive dot plot representations of orthologous genes between a pair of chromosomes in two distinct plant species, and (iii) a search module that interconnects all modules via free‐text search capability with online as‐you‐type suggestions and highlighting that allows exploration of the underlining information without constraint of interface‐dependent search fields. The POB is a web‐based orthology and annotation visualization tool, which currently supports 20 completely sequenced plant species with considerably large genomes and offers intuitive and highly interactive pairwise comparison and visualization of genomic traits via gene orthology.

Synteny

Comparative Genomics

Orthologous Gene

Genome browser

Gene Annotation

Brachypodium distachyon

Brachypodium

10.3835/plantgenome2016.08.0078

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Effects of clipping of flight feathers on resource use in Gallus gallus domesticus

Royal Society Open Science (2022)

Renée Garant Bret W. Tobalske Neila BenSassi Nienke van Staaveren Dan Tulpan

Ground-dwelling species of birds, such as domestic chickens (Gallus gallus domesticus), experience difficulties sustaining flight due to high wing loading. This limited flight ability may be exacerbated by loss of flight feathers that is prevalent among egg-laying chickens. Despite this, chickens housed in aviary style systems need to use flight to access essential resources stacked in vertical tiers. To understand the impact of flight feather loss on chickens' ability to access elevated resources, we clipped primary and secondary flight feathers for two hen strains (brown-feathered and white-feathered, n = 120), and recorded the time hens spent at elevated resources (feeders, nest-boxes). Results showed that flight feather clipping significantly reduced the percentage of time that hens spent at elevated resources compared to ground resources. When clipping both primary and secondary flight feathers, all hens exhibited greater than or equal to 38% reduction in time spent at elevated resources. When clipping only primary flight feathers, brown-feathered hens saw a greater than 50% reduction in time spent at elevated nest-boxes. Additionally, brown-feathered hens scarcely used the elevated feeder regardless of treatment. Clipping of flight feathers altered the amount of time hens spent at elevated resources, highlighting that distribution and accessibility of resources is an important consideration in commercial housing.

Clipping (morphology)

Gallus gallus domesticus

Flight feather

10.1098/rsos.211561

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Session details: Theme: Artificial intelligence and agents: BIO - Bioinformatics track

Juan M. Corchado Paola Lecca Dan Tulpan

No abstract available.

Theme (computing)

10.1145/3462403

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Use of rumination time in health risk assessment of prepartum dairy cows.

Journal of Dairy Science (2024)

M.G.S. Santos N. Antonacci C. Van Dorp B. Mion Dan Tulpan

The objectives of this observational cohort study were to evaluate the associations of rumination time (RT) in the last week of pregnancy with transition cow metabolism, inflammation, health, and subsequent milk production, reproduction, and culling. Pregnant nulliparous (n = 199) and parous (n = 337) cows were enrolled 21 d before expected calving. Rumination time and physical activity were monitored automatically by sensors from d -21 to 15 relative to calving. Blood samples were collected on d -14, -5, 4, 8, and 12 ± 1 relative to calving. Diagnoses of clinical health problems were performed by researchers from calving to 15 DIM. In classification 1, cows were ranked based on average daily RT in the last week of pregnancy and classified into terciles as short RT (SRT), moderate RT (MRT), or long RT (LRT) for association analyses. In classification 2, RT deviation from the parity average was used in a receiver operating characteristic curve to identify the best threshold to predict postpartum clinical disease. Cows were then classified as above the threshold (AT) or below the threshold (BT). Compared with cows with LRT, cows with SRT had greater serum concentrations of nonesterified fatty acids (0.47 vs. 0.40 ± 0.01 mmol/L), BHB (0.58 vs. 0.52 ± 0.01 mmol/L), and haptoglobin (0.22 vs. 0.18 ± 0.008 g/L) throughout the transition period, and reduced concentrations of glucose, cholesterol, albumin, and magnesium in a time-dependent manner. Parous cows with SRT had higher odds of postpartum clinical disease (adjusted odds ratio [AOR] = 3.7; 95% CI: 2.1-6.4), lower odds of pregnancy by 210 DIM (AOR = 0.34; 95% CI = 0.15-0.75), and lower milk production (46.9 vs. 48.6 ± 0.5 kg/d) than parous cows with LRT. Deviation in prepartum RT had good predictive value for clinical disease in parous cows (area under the curve = 0.65; 95% CI = 0.60-0.71) but not in nulliparous (area under the curve = 0.51; 95% CI = 0.42-0.59). Separation of parous cows according to the identified threshold (≤-53 min from the parity average) resulted in differences in serum concentrations of nonesterified fatty acids (AT = 0.31 ± 0.006, BT = 0.38 ± 0.014 mmol/L), BHB (AT = 0.49 ± 0.008, BT = 0.53 ± 0.015 mmol/L), and globulin (AT = 32.3 ± 0.3, BT = 34.8 ± 0.5 g/L) throughout the transition period, as well as in serum cholesterol, urea, magnesium, albumin, and haptoglobin in a time-dependent manner. Below threshold parous cows had higher odds of clinical disease (AOR = 3.7; 95% CI = 2.1-6.4), reduced hazard of pregnancy (adjusted hazard ratio = 0.64, 95% CI: 0.47-0.89), greater hazard of culling (adjusted hazard ratio = 2.1, 95% CI: 1.2-3.6), and lower milk production (46.3 ± 0.7 vs. 48.5 ± 0.3 kg/d). External validation using data from 153 parous cows from a different herd and the established threshold in RT deviation (≤-53 min) resulted in similar predictive value, with the odds of postpartum disease 2.4 times greater in BT than AT (37.5% vs. 20.1%). In conclusion, RT in the week preceding calving was a reasonable predictor of postpartum health and future milk production, reproduction, and culling in parous cows but not in nulliparous cows.

NEFA

Haptoglobin

Odds

10.3168/jds.2023-24610

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Stochastic Local Search Algorithms for DNA Word Design

Lecture notes in computer science (2003)

Dan Tulpan Holger H. Hoos Anne Condon

10.1007/3-540-36440-4_20

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A review of traditional and machine learning methods applied to animal breeding

Animal Health Research Reviews (2019)

Shadi Nayeri Mehdi Sargolzaei Dan Tulpan

Abstract The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

Mainstream

10.1017/s1466252319000148

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Citations (62)