%0 Journal Article %J BMC Res Notes %D 2019 %T Community-led data collection using Open Data Kit for surveillance of animal African trypanosomiasis in Shimba hills, Kenya. %A Wamwenje, Sarah A O %A Wangwe, Ibrahim I %A Masila, Nicodemus %A Mirieri, Caroline K %A Wambua, Lillian %A Kulohoma, Benard W %K Adult %K Animals %K Cattle %K Cattle Diseases %K Community-Based Participatory Research %K Data Collection %K Epidemiological Monitoring %K Farmers %K Female %K Humans %K Kenya %K Male %K Mobile Applications %K Pilot Projects %K Proof of Concept Study %K Trypanosomiasis, African %X

OBJECTIVE: In Sub-Saharan Africa, there is an increase in trypanosome non-susceptibility to multiple trypanocides, but limited information on judicious trypanocide use is accessible to smallholder farmers and agricultural stakeholders in disease endemic regions, resulting in widespread multi-drug resistance. Huge economic expenses and the laborious nature of extensive field studies have hindered collection of the requisite large-scale prospective datasets required to inform disease management. We examined the efficacy of community-led data collection strategies using smartphones by smallholder farmers to acquire robust datasets from the trypanosomiasis endemic Shimba hills region in Kenya. We used Open Data Kit, an open-source smartphone application development software, to create a data collection App.

RESULTS: Our study provides proof of concept for the viability of using smartphone Apps to remotely collect reliable large-scale information from smallholder farmers and veterinary health care givers in resource poor settings. We show that these datasets can be reliably collated remotely, analysed, and the findings can inform policies that improve farming practices and economic wellbeing while restricting widespread multi-drug resistance. Moreover, this strategy can be used to monitor and manage other infectious diseases in other rural, resource poor settings.

%B BMC Res Notes %V 12 %P 151 %8 2019 Mar 18 %G eng %N 1 %R 10.1186/s13104-019-4198-z %0 Journal Article %J Sci Rep %D 2018 %T Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach. %A Vandaele, Rémy %A Aceto, Jessica %A Muller, Marc %A Péronnet, Frédérique %A Debat, Vincent %A Wang, Ching-Wei %A Huang, Cheng-Ta %A Jodogne, Sébastien %A Martinive, Philippe %A Geurts, Pierre %A Marée, Raphaël %K Algorithms %K Animals %K Body Weights and Measures %K Drosophila %K Humans %K Image Processing, Computer-Assisted %K Software %K Zebrafish %X

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.

%B Sci Rep %V 8 %P 538 %8 2018 01 11 %G eng %N 1 %R 10.1038/s41598-017-18993-5 %0 Journal Article %J Phys Med Biol %D 2012 %T STIR: software for tomographic image reconstruction release 2. %A Thielemans, Kris %A Tsoumpas, Charalampos %A Mustafovic, Sanida %A Beisel, Tobias %A Aguiar, Pablo %A Dikaios, Nikolaos %A Jacobson, Matthew W %K Algorithms %K Animals %K Computers %K Image Processing, Computer-Assisted %K Mice %K Software %K Tomography %X We present a new version of STIR (Software for Tomographic Image Reconstruction), an open source object-oriented library implemented in C++ for 3D positron emission tomography reconstruction. This library has been designed such that it can be used for many algorithms and scanner geometries, while being portable to various computing platforms. This second release enhances its flexibility and modular design and includes additional features such as Compton scatter simulation, an additional iterative reconstruction algorithm and parametric image reconstruction (both indirect and direct). We discuss the new features in this release and present example results. STIR can be downloaded from http://stir.sourceforge.net. %B Phys Med Biol %V 57 %P 867-83 %8 2012 Feb 21 %G eng %N 4 %R 10.1088/0031-9155/57/4/867 %0 Journal Article %J Bioinformatics %D 2011 %T DDN: a caBIG® analytical tool for differential network analysis. %A Zhang, Bai %A Tian, Ye %A Jin, Lu %A Li, Huai %A Shih, Ie-Ming %A Madhavan, Subha %A Clarke, Robert %A Hoffman, Eric P %A Xuan, Jianhua %A Hilakivi-Clarke, Leena %A Wang, Yue %K Animals %K Computational Biology %K Epigenesis, Genetic %K Female %K Gene Regulatory Networks %K Mammary Glands, Animal %K Rats %K Software %K Systems Biology %X

UNLABELLED: Differential dependency network (DDN) is a caBIG® (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biological conditions. Developed under caBIG®'s In Silico Research Centers of Excellence (ISRCE) Program, DDN enables differential network analysis and provides an alternative way for defining network biomarkers predictive of phenotypes. DDN also serves as a useful systems biology tool for users across biomedical research communities to infer how genetic, epigenetic or environment variables may affect biological networks and clinical phenotypes. Besides the standalone Java application, we have also developed a Cytoscape plug-in, CytoDDN, to integrate network analysis and visualization seamlessly.

AVAILABILITY: The Java and MATLAB source code can be downloaded at the authors' web site http://www.cbil.ece.vt.edu/software.htm.

%B Bioinformatics %V 27 %P 1036-8 %8 2011 Apr 1 %G eng %N 7 %R 10.1093/bioinformatics/btr052 %0 Journal Article %J Acta oncologica (Stockholm, Sweden) %D 2010 %T Adaptive radiotherapy based on contrast enhanced cone beam CT imaging. %A Søvik, Aste %A Rødal, Jan %A Skogmo, Hege K %A Lervåg, Christoffer %A Eilertsen, Karsten %A Malinen, Eirik %K Animals %K Carcinoma %K Cone-Beam Computed Tomography %K Contrast Media %K Dog Diseases %K Dogs %K Female %K Maxillary Neoplasms %K Patient Positioning %K Radiographic Image Enhancement %K Radiotherapy Planning, Computer-Assisted %X Cone beam CT (CBCT) imaging has become an integral part of radiation therapy, with images typically used for offline or online patient setup corrections based on bony anatomy co-registration. Ideally, the co-registration should be based on tumor localization. However, soft tissue contrast in CBCT images may be limited. In the present work, contrast enhanced CBCT (CECBCT) images were used for tumor visualization and treatment adaptation. Material and methods. A spontaneous canine maxillary tumor was subjected to repeated cone beam CT imaging during fractionated radiotherapy (10 fractions in total). At five of the treatment fractions, CECBCT images, employing an iodinated contrast agent, were acquired, as well as pre-contrast CBCT images. The tumor was clearly visible in post-contrast minus pre-contrast subtraction images, and these contrast images were used to delineate gross tumor volumes. IMRT dose plans were subsequently generated. Four different strategies were explored: 1) fully adapted planning based on each CECBCT image series, 2) planning based on images acquired at the first treatment fraction and patient repositioning following bony anatomy co-registration, 3) as for 2), but with patient repositioning based on co-registering contrast images, and 4) a strategy with no patient repositioning or treatment adaptation. The equivalent uniform dose (EUD) and tumor control probability (TCP) calculations to estimate treatment outcome for each strategy. Results. Similar translation vectors were found when bony anatomy and contrast enhancement co-registration were compared. Strategy 1 gave EUDs closest to the prescription dose and the highest TCP. Strategies 2 and 3 gave EUDs and TCPs close to that of strategy 1, with strategy 3 being slightly better than strategy 2. Even greater benefits from strategies 1 and 3 are expected with increasing tumor movement or deformation during treatment. The non-adaptive strategy 4 was clearly inferior to all three adaptive strategies. Conclusion. CECBCT may prove useful for adaptive radiotherapy. %B Acta oncologica (Stockholm, Sweden) %V 49 %P 972-7 %8 2010 Oct %G eng %N 7 %1 http://www.ncbi.nlm.nih.gov/pubmed/20831484?dopt=Abstract %0 Journal Article %J Clin Pharmacol Ther %D 2010 %T How informatics can potentiate precompetitive open-source collaboration to jump-start drug discovery and development. %A Perakslis, E D %A Van Dam, J %A Szalma, S %K Animals %K Cooperative Behavior %K Drug Discovery %K Drug Industry %K Economic Competition %K Humans %K Informatics %K Information Dissemination %B Clin Pharmacol Ther %V 87 %P 614-6 %8 2010 May %G eng %N 5 %R 10.1038/clpt.2010.21 %0 Journal Article %J PLoS One %D 2010 %T JULIDE: a software tool for 3D reconstruction and statistical analysis of autoradiographic mouse brain sections. %A Ribes, Delphine %A Parafita, Julia %A Charrier, Rémi %A Magara, Fulvio %A Magistretti, Pierre J %A Thiran, Jean-Philippe %K Animals %K Autoradiography %K Brain %K Carbon Radioisotopes %K Deoxyglucose %K Image Processing, Computer-Assisted %K Imaging, Three-Dimensional %K Male %K Maze Learning %K Mice %K Mice, Inbred C57BL %K Reproducibility of Results %K Software %X

In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. This software tool has been developed in the open-source ITK software framework and is freely available under a GPL license. The article presents the complete image processing chain from raw data acquisition to 3D statistical group analysis. Results of the group comparison in the context of a study on spatial learning are shown as an illustration of the data that can be obtained with this tool.

%B PLoS One %V 5 %P e14094 %8 2010 %G eng %N 11 %R 10.1371/journal.pone.0014094