Raul Cruz-Cano

Lower prevalence of antibiotic-resistant Salmonella on large-scale U.S. conventional poultry farms that transitioned to organic practices

As a result of the widespread use of antibiotics in large-scale U.S. poultry production, a significant proportion of Salmonella strains recovered from conventional poultry farms and retail poultry products express antibiotic resistance. We evaluated whether large-scale poultry farms that transitioned from conventional to organic practices and discontinued antibiotic use were characterized by differences in the prevalence of antibiotic-resistant Salmonella compared to farms that maintained conventional practices. We collected poultry litter, water and feed samples from 10 newly organic and 10 conventional poultry houses. Samples were analyzed for Salmonella using standard enrichment methods. Isolates were confirmed using standard biochemical tests and the Vitek®2 Compact System. Antimicrobial susceptibility testing was performed by Sensititre® microbroth dilution. Data were analyzed using Fishers exact test and generalized linear mixed models. We detected Salmonella in both conventional and newly organic poultry houses. Salmonella Kentucky was the predominant serovar identified, followed by S. Orion, S. Enteritidis, S. Gostrup and S. Infantis. Among S. Kentucky isolates (n=41), percent resistance was statistically significantly lower among isolates recovered from newly organic versus conventional poultry houses for: amoxicillin-clavulanate (p=0.049), ampicillin (p=0.042), cefoxitin (p=0.042), ceftiofur (p=0.043) and ceftriaxone (p=0.042). Percent multidrug resistance (resistance to ≥3 antimicrobial classes) was also statistically significantly lower among S. Kentucky isolates recovered from newly organic poultry houses (6%) compared to those recovered from conventional houses (44%) (p=0.015). To our knowledge, these are the first U.S. data to show immediate, on-farm changes in the prevalence of antibiotic-resistant Salmonella when antibiotics are voluntarily withdrawn from large-scale poultry facilities in the United States.

Fast regularized canonical correlation analysis

Canonical correlation analysis is a popular statistical method for the study of the correlations between two sets of variables. Finding the canonical correlations between these datasets requires the inversion of their corresponding sample correlation matrices. When the number of variables is large compared to the number of experimental units it is impossible to calculate the inverse of these matrices directly and therefore it is necessary to add a multiple of the identity matrix to them. This procedure is known as regularization. In this paper we present an alternative method to the existing regularization algorithm. The proposed method is based on the estimates of the correlation matrices which minimize the mean squared error risk function. The solution of this optimization problem can be found analytically and consists of a small set of computationally inexpensive equations. We also present material which shows that the proposed method is more stable and provides more accurate results than the standard regularized canonical correlation method. Finally, the application of our original method to NCI-60 microRNA cancer data proves that it can deliver useful insights in study cases which involve hundreds of variables.

Impact of Rurality, Broiler Operations, and Community Socioeconomic Factors on the Risk of Campylobacteriosis in Maryland

OBJECTIVES We evaluated the combined impact of community-level environmental and socioeconomic factors on the risk of campylobacteriosis. METHODS We obtained Campylobacter case data (2002-2010; n = 3694) from the Maryland Foodborne Diseases Active Surveillance Network. We obtained community-level socioeconomic and environmental data from the 2000 US Census and the 2007 US Census of Agriculture. We linked data by zip code. We derived incidence rate ratios by Poisson regressions. We mapped a subset of zip code-level characteristics. RESULTS In zip codes that were 100% rural, incidence rate ratios (IRRs) of campylobacteriosis were 6 times (IRR = 6.18; 95% confidence interval [CI] = 3.19, 11.97) greater than those in urban zip codes. In zip codes with broiler chicken operations, incidence rates were 1.45 times greater than those in zip codes without broilers (IRR = 1.45; 95% CI = 1.34, 1.58). We also observed higher rates in zip codes whose populations were predominantly White and had high median incomes. CONCLUSIONS The community and environment in which one lives may significantly influence the risk of campylobacteriosis.

Multi-Mode Radar Target Detection and Recognition Using Neural Networks

In typical radar systems, the process of recognizing a target requires human involvement. This human element makes radar systems not fully reliable due to unstable performance that varies between operators. This paper describes an intelligent radar system which addresses this problem in a border surveillance environment. The proposed radar system is capable of automatically detecting and then classifying different targets using an artificial neural network trained with the Levenberg-Marquardt algorithm. The training and test sets presented to the neural network are composed by high-resolution Inverse Synthetic Aperture Radar pictures obtained by the radars detection module. Simulation results show that the intelligent radar system can reliably detect and distinguish the different objectives. Moreover, the radar system can outperform human operators and another radar system that deals with similar objectives. These results indicate that future intelligent systems can potentially replace human radar operators in this critical security setting.

Isoflavone pharmacokinetics and metabolism after consumption of a standardized soy and soy-almond bread in men with asymptomatic prostate cancer

Epidemiologic associations suggest that populations consuming substantial amounts of dietary soy exhibit a lower risk of prostate cancer. A 20-week randomized, phase II, crossover trial was conducted in 32 men with asymptomatic prostate cancer. The crossover involved 8 weeks each of soy bread (SB) and soy–almond bread (SAB). The primary objective was to investigate isoflavone bioavailability and metabolite profile. Secondary objectives include safety, compliance, and assessment of biomarkers linked to prostate carcinogenesis. Two distinct SBs were formulated to deliver approximately 60 mg aglycone equivalents of isoflavones per day. The isoflavones were present as aglycones (∼78% as aglycones) in the SAB whereas in the standard SB predominantly as glucosides (18% total isoflavones as aglycones). Compliance to SB (97% ± 4%) and SAB (92% ± 18%) was excellent; toxicity was rare and limited to grade 1 gastrointestinal complaints. Pharmacokinetic studies between SB and SAB showed modest differences. Peak serum concentration time (Tmax) was significantly faster with SAB meal compared with SB in some isoflavonoids, and AUC0 to 24 h of dihydrodaidzein and O-desmethylangolensin was significantly greater after an SB meal. An exploratory cluster analysis was used to identify four isoflavone-metabolizing phenotypes. Insulin-like growth factor–binding protein increased significantly by 41% (P = 0.024) with soy intervention. Findings from this study provide the necessary framework to study isoflavone-metabolizing phenotypes as a strategy for identification of individuals that might benefit or show resistance to cancer preventive strategies using dietary soy. A standardized SB used for future large-scale randomized clinical trials to affect human prostate carcinogenesis is feasible. Cancer Prev Res; 8(11); 1045–54. ©2015 AACR.

Logic minimization and rule extraction for identification of functional sites in molecular sequences

BackgroundLogic minimization is the application of algebraic axioms to a binary dataset with the purpose of reducing the number of digital variables and/or rules needed to express it. Although logic minimization techniques have been applied to bioinformatics datasets before, they have not been used in classification and rule discovery problems. In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sites in proteins. TFBS are important in various developmental processes and glycosylation is a posttranslational modification critical to protein functions.MethodsIn the present study, we first transformed the original biological dataset into a suitable binary form. Logic minimization was then applied to generate sets of simple rules to describe the transformed dataset. These rules were used to predict TFBS and O-glycosylation sites. The TFBS dataset is obtained from the TRANSFAC database, while the glycosylation dataset was compiled using information from OGLYCBASE and the Swiss-Prot Database.We performed the same predictions using two standard classification techniques, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), and used their sensitivities and positive predictive values as benchmarks for the performance of our proposed algorithm. SVM were also used to reduce the number of variables included in the logic minimization approach.ResultsFor both TFBS and O-glycosylation sites, the prediction performance of the proposed logic minimization method was generally comparable and, in some cases, superior to the standard ANN and SVM classification methods with the advantage of providing intelligible rules to describe the datasets. In TFBS prediction, logic minimization produced a very small set of simple rules. In glycosylation site prediction, the rules produced were also interpretable and the most popular rules generated appeared to correlate well with recently reported hydrophilic/hydrophobic enhancement values of amino acids around possible O-glycosylation sites. Experiments with Self-Organizing Neural Networks corroborate the practical worth of the logic minimization method for these case studies.ConclusionsThe proposed logic minimization algorithm provides sets of rules that can be used to predict TFBS and O-glycosylation sites with sensitivity and positive predictive value comparable to those from ANN and SVM. Moreover, the logic minimization method has the additional capability of generating interpretable rules that allow biological scientists to correlate the predictions with other experimental results and to form new hypotheses for further investigation. Additional experiments with alternative rule-extraction techniques demonstrate that the logic minimization method is able to produce accurate rules from datasets with large numbers of variables and limited numbers of positive examples.

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications.

Computational Prediction of Replication Origins in Herpesviruses

Computational methods for replication origin prediction in individual herpesvirus genomes have been previously devised based on the locations of high concentrations of palindromes. In order to make use of similarities in genome composition and organization of related herpesviruses, an artificial neural network approach is explored. We implement feed-forward artificial neural networks trained by 17 input variables comprising the positions of known replication origins relative to the genome lengths and the dinucleotide scores. The overall prediction accuracy of the neural network approach for our data set is better than that of the palindrome based approach. Furthermore, suitable combinations of the prediction results given by the two approaches substantially increase the prediction accuracy achieved by either method applied individually.

Mentholation affects the cigarette microbiota by selecting for bacteria resistant to harsh environmental conditions and selecting against potential bacterial pathogens

BackgroundThere is a paucity of data regarding the microbial constituents of tobacco products and their impacts on public health. Moreover, there has been no comparative characterization performed on the bacterial microbiota associated with the addition of menthol, an additive that has been used by tobacco manufacturers for nearly a century. To address this knowledge gap, we conducted bacterial community profiling on tobacco from user- and custom-mentholated/non-mentholated cigarette pairs, as well as a commercially-mentholated product. Total genomic DNA was extracted using a multi-step enzymatic and mechanical lysis protocol followed by PCR amplification of the V3-V4 hypervariable regions of the 16S rRNA gene from five cigarette products (18 cigarettes per product for a total of 90 samples): Camel Crush, user-mentholated Camel Crush, Camel Kings, custom-mentholated Camel Kings, and Newport Menthols. Sequencing was performed on the Illumina MiSeq platform and sequences were processed using the Quantitative Insights Into Microbial Ecology (QIIME) software package.ResultsIn all products, Pseudomonas was the most abundant genera and included Pseudomonas oryzihabitans and Pseudomonas putida, regardless of mentholation status. However, further comparative analysis of the five products revealed significant differences in the bacterial compositions across products. Bacterial community richness was higher among non-mentholated products compared to those that were mentholated, particularly those that were custom-mentholated. In addition, mentholation appeared to be correlated with a reduction in potential human bacterial pathogens and an increase in bacterial species resistant to harsh environmental conditions.ConclusionsTaken together, these data provide preliminary evidence that the mentholation of commercially available cigarettes can impact the bacterial community of these products.

Presence of animal feeding operations and community socioeconomic factors impact salmonellosis incidence rates: An ecological analysis using data from the Foodborne Diseases Active Surveillance Network (FoodNet), 2004-2010.

Nontyphoidal Salmonella spp. are a leading cause of foodborne illness. Risk factors for salmonellosis include the consumption of contaminated chicken, eggs, pork and beef. Agricultural, environmental and socioeconomic factors also have been associated with rates of Salmonella infection. However, to our knowledge, these factors have not been modeled together at the community-level to improve our understanding of whether rates of salmonellosis are variable across communities defined by differing factors. To address this knowledge gap, we obtained data on culture-confirmed Salmonella Typhimurium, S. Enteritidis, S. Newport and S. Javiana cases (2004-2010; n=14,297) from the Foodborne Diseases Active Surveillance Network (FoodNet), and socioeconomic, environmental and agricultural data from the 2010 Census of Population and Housing, the 2011 American Community Survey, and the 2007 U.S. Census of Agriculture. We linked data by zip code and derived incidence rate ratios using negative binomial regressions. Multiple community-level factors were associated with salmonellosis rates; however, our findings varied by state. For example, in Georgia (Incidence Rate Ratio (IRR)=1.01; 95% Confidence Interval (CI)=1.005-1.015) Maryland (IRR=1.01; 95% CI=1.003-1.015) and Tennessee (IRR=1.01; 95% CI=1.002-1.012), zip codes characterized by greater rurality had higher rates of S. Newport infections. The presence of broiler chicken operations, dairy operations and cattle operations in a zip code also was associated with significantly higher rates of infection with at least one serotype in states that are leading producers of these animal products. For instance, in Georgia and Tennessee, rates of S. Enteritidis infection were 48% (IRR=1.48; 95% CI=1.12-1.95) and 46% (IRR=1.46; 95% CI=1.17-1.81) higher in zip codes with broiler chicken operations compared to those without these operations. In Maryland, New Mexico and Tennessee, higher poverty levels in zip codes were associated with higher rates of infection with one or more Salmonella serotypes. In Georgia and Tennessee, zip codes with higher percentages of the population composed of African Americans had significantly higher rates of infection with one or more Salmonella serotypes. In summary, our findings show that community-level agricultural, environmental and socioeconomic factors may be important with regard to rates of infection with Salmonella Typhimurium, Enteritidis, Newport and Javiana.