A sequential naïve Bayes classifier for DNA barcodes.

DNA barcodes are short strands of 255-700 nucleotide bases taken from the cytochrome c oxidase subunit 1 (COI) region of the mitochondrial DNA. It has been proposed that these barcodes may be used as a method of differentiating between biological species. Current methods of species classification utilize distance measures that are heavily dependent on both evolutionary model assumptions as well as a clearly defined "gap" between intra- and interspecies variation. Such distance measures fail to measure classification uncertainty or to indicate how much of the barcode is necessary for classification. We propose a sequential naïve Bayes classifier for species classification to address these limitations. The proposed method is shown to provide accurate species-level classification on real and simulated data. The method proposed here quantifies the uncertainty of each classification and addresses how much of the barcode is necessary.

Predicting cumulative risk of bovine respiratory disease complex (BRDC) using feedlot arrival data and daily morbidity and mortality counts.

Although bovine respiratory disease complex (BRDC) is common in post-weaning cattle, BRDC prediction models are seldom analyzed. The objectives of this study were to assess the ability to predict cumulative cohort-level BRDC morbidity using on-arrival risk factors and to evaluate whether or not adding BRDC risk classification and daily BRDC morbidity and mortality data to the models enhanced their predictive ability. Retrospective cohort-level and individual animal health data were used to create mixed negative binomial regression (MNBR) models for predicting cumulative risk of BRDC morbidity. Logistic regression models were used to illustrate that the percentage of correctly (within |5%| of actual) classified cohorts increased across days, but the effect of day was modified by arrival weight, arrival month, and feedlot. Cattle arriving in April had the highest (77%) number of lots correctly classified at arrival and cattle arriving in December had the lowest (28%). Classification accuracy at arrival varied according to initial weight, ranging from 17% (< 182 kg) to 91% (> 409 kg). Predictive accuracy of the models improved from 64% at arrival to 74% at 8 days on feed (DOF) when risk code was known compared to 56% accuracy at arrival and 69% at 8 DOF when risk classification was not known. The results of this study demonstrate how the predictive ability of models can be improved by utilizing more refined data on the prior history of cohorts, thus making these models more useful to operators of commercial feedlots.

Bien que le complexe des maladies respiratoires bovines (BRDC) est fréquent chez les bovins en période post-sevrage, les modèles prédictifs de BRDC sont peu souvent analysés. Les objectifs de la présente étude étaient d'évaluer la capacité à prédire la morbidité cumulative associée au BRDC d'une cohorte en utilisant les facteurs de risque à l'arrivée et d'évaluer si l'ajout ou non de la classification du risque de BRDC et les données quotidiennes de morbidité et de mortalité aux modèles augmente leur capacité prédictive. Les données de santé provenant d'études rétrospectives de cohortes ainsi que d'animaux individuels ont été utilisées pour créer des modèles mixtes de régression binomiale négative mixte (MNBR) pour prédire le risque cumulatif de morbidité associé au BRDC. Les modèles de régression logistique étaient utilisés pour illustrer que le pourcentage de cohortes correctement classifiées (|5 %| à l'intérieur de la valeur actuelle) augmentait au fil des jours, mais que l'effet du jour était modifié par le poids à l'arrivée, le mois d'arrivée, et le parc d'engraissement. Les bovins arrivant en avril avaient le nombre le plus élevé (77 %) de lots classifiés correctement à l'arrivée et les bovins arrivant en décembre avaient le nombre le plus faible (28 %). La précision de la classification à l'arrivée variait selon le poids initial, allant de 17 % (< 182 kg) à 91 % (> 409 kg). La précision prédictive des modèles s'est améliorée de 64 % à l'arrivée à 74 % au jour 8 d'alimentation (DOF) lorsque le code de risque était connu comparativement à une précision de 56 % à l'arrivée et de 69 % à 8 j DOF lorsque la classification de risque était inconnue. Les résultats de cette étude démontrent comme la capacité prédictive des modèles peut être améliorée en utilisant des données plus précises sur l'historique des cohortes, rendant ainsi ces modèles plus utiles aux opérateurs de parcs d'engraissement commerciaux.(Traduit par Docteur Serge Messier).

Metalloproteinase-disintegrin ADAM12 is associated with a breast tumor-initiating cell phenotype.

Members of the ADAM family of proteases have been associated with mammary tumorigenesis. Gene profiling of human breast tumors identified several intrinsic subtypes of breast cancer, which differ in terms of their basic biology, response to chemotherapy/radiation, preferential sites of metastasis, and overall patient survival. Whether or not the expression of individual ADAM proteases is linked to a particular subtype of breast cancer and whether the functions of these ADAMs are relevant to the cancer subtype have not been investigated. We analyzed several transcriptomic datasets and found that ADAM12L is specifically up-regulated in claudin-low tumors. These tumors are poorly differentiated, exhibit aggressive characteristics, have molecular signatures of epithelial-to-mesenchymal transition (EMT), and are rich in markers of breast tumor-initiating cells (BTICs). Consistently, we find that ADAM12L, but not the alternative splice variant ADAM12S, is a part of stromal, mammosphere, and EMT gene signatures, which are all associated with BTICs. In patients with estrogen receptor-negative tumors, high expression of ADAM12L, but not ADAM12S, is predictive of resistance to neoadjuvant chemotherapy. Using MCF10DCIS.com breast cancer cells, which express the endogenous ADAM12L and efficiently form mammospheres when plated at the density of single cell per well, we show that ADAM12L plays an important role in supporting mammosphere growth. We postulate that ADAM12L may serve as a novel marker and/or a novel therapeutic target in BTICs.