An epidemiological study of burglary offenders: trends and predictors of self-reported arrests for burglary in the United States, 2002-2013

Burglary is serious property crime with a relatively high incidence and has been shown to be variously associated with other forms of criminal behavior. Unfortunately, an epidemiological understanding of burglary and its correlates is largely missing from the literature. Using public-use data collected between 2002 and 2013 as part of the National Survey on Drug Use and Health (NSDUH), the current study compared those who self-reported burglary arrest in the prior 12 months with and without criminal history. The unadjusted prevalence estimates of self-reported burglary arrest were statistically different for those with a prior arrest history (4.7%) compared with those without an arrest history (0.02%) which is a 235-fold difference. Those with an arrest history were more likely to report lower educational attainment, to have lower income, to have moved more than 3 times in the past 5 years, and to use alcohol, tobacco, illicit drugs, and engage in binge drinking. Moreover, those with prior arrest histories were younger and more likely to be male. There is considerable heterogeneity among burglars with criminal history indicating substantially greater behavioral risk

ProtocadherinX/Y, a Candidate Gene-Pair for Schizophrenia and Schizoaffective Disorder: A DHPLC Investigation of Gonomic Sequence

Protocadherin X and Protocadherin Y (PCDHX and PCDHY) are cell-surface adhesion molecules expressed predominantly in the brain. The PCDHX/Y gene-pair was generated by an X-Y translocation approximately 3 million years ago (MYA) that gave rise to the Homo sapiens-specific region of Xq21.3 and Yp11.2 homology. Genes within this region are expected to code for sexually dimorphic human characteristics, including, for example, cerebral asymmetry a dimension of variation that has been suggested is relevant to psychosis. We examined differences in patients with schizophrenic or schizoaffective psychosis in the genomic sequence of PCDHX and PCDHY in coding and adjacent intronic sequences using denaturing high performance liquid chromatography (DHPLC). Three coding variants were detected in PCDHX and two in PCDHY. However, neither the coding variants nor the intronic polymorphisms could be related to psychosis within families. Low sequence variation suggests selective pressure against sequence change in modern humans in contrast to the structural chromosomal and sequence changes including fixed X-Y differences that occurred in this region earlier in hominid evolution. Our findings exclude sequence variation in PCDHX/Y as relevant to the aetiology of psychosis. However, we note the unusual status of this region with respect to X-inactivation. Further investigation of the epigenetic control of PCDHX/Y in relation to psychosis is warran

Quick assessment of the economic value of olive mill waste water

Background: Olive biophenols are emerging as a valued class of natural products finding practical application in the food, pharmaceutical, beverage, cosmetic and nutraceutical industries due to their powerful biological activity which includes antioxidant and antimicrobial properties. Olive mill waste water (OMWW), a by-product in olive oil manufacturing, is rich in biophenols such as hydroxytyrosol and tyrosol. The amount of biophenols depends on the cultivar, the geographical area of cultivation, and the seasonal conditions. The goal of this study was to develop a straightforward method to assess the economic value of OMWW via quantification of hydroxytyrosol and tyrosol. Results: The amount of hydroxytyrosol and tyrosol phenolic compounds in the OMWW from four different cultivars grown in four different regions of Sicily was analyzed using liquid-liquid and solid-liquid analytical protocols developed ad hoc. Results showed significant differences amongst the different cultivars and their geographical origin. In all samples, the concentration of hydroxytyrosol was generally from 2 to 10 times higher than that of tyrosol. In general, the liquid-liquid extraction protocol gave higher amounts of extracted biophenols. The cultivar Cerasuola had the highest amount of both hydroxytyrosol and tyrosol. The cultivar Nocellara Etnea had the lowest content of both biophenols. Conclusions: A quick method to assess the economic value of olive mill waste water via quantification of hydroxytyrosol and tyrosol in olive phenolic enriched extracts is now available

Behavior of tumors under nonstationary theraphy

We present a model for the interaction dynamics of lymphocytes-tumor cells population. This model reproduces all known states for the tumor. Futherly,we develop it taking into account periodical immunotheraphy treatment with cytokines alone. A detailed analysis for the evolution of tumor cells as a function of frecuency and theraphy burden applied for the periodical treatment is carried out. Certain threshold values for the frecuency and applied doses are derived from this analysis. So it seems possible to control and reduce the growth of the tumor. Also, constant values for cytokines doses seems to be a succesful treatment.Comment: 6 pages, 7 figure

Can we use Hare’s psychopathy model within forensic and non-forensic populations? An empirical investigation

Although psychopathy construct (SRP-SF) was assessed among various samples, prior research did not investigate whether the model proposed by Hare and colleagues can be used to capture psychopathy scores derived from forensic and non-forensic populations. The main objective of the current study was to test dimensionality, construct validity, and factorial invariance of the SRP-SF within prison (N = 730) and student (N = 2,506) samples. Our results indicate that the SRP-SF measure cannot be used in the same way within forensic and non-forensic samples, which may be due to the inclusion of criminal/antisocial traits as an integral part of psychopathy

Comparative assessment of performance and genome dependence among phylogenetic profiling methods

BACKGROUND: The rapidly increasing speed with which genome sequence data can be generated will be accompanied by an exponential increase in the number of sequenced eukaryotes. With the increasing number of sequenced eukaryotic genomes comes a need for bioinformatic techniques to aid in functional annotation. Ideally, genome context based techniques such as proximity, fusion, and phylogenetic profiling, which have been so successful in prokaryotes, could be utilized in eukaryotes. Here we explore the application of phylogenetic profiling, a method that exploits the evolutionary co-occurrence of genes in the assignment of functional linkages, to eukaryotic genomes. RESULTS: In order to evaluate the performance of phylogenetic profiling in eukaryotes, we assessed the relative performance of commonly used profile construction techniques and genome compositions in predicting functional linkages in both prokaryotic and eukaryotic organisms. When predicting linkages in E. coli with a prokaryotic profile, the use of continuous values constructed from transformed BLAST bit-scores performed better than profiles composed of discretized E-values; the use of discretized E-values resulted in more accurate linkages when using S. cerevisiae as the query organism. Extending this analysis by incorporating several eukaryotic genomes in profiles containing a majority of prokaryotes resulted in similar overall accuracy, but with a surprising reduction in pathway diversity among the most significant linkages. Furthermore, the application of phylogenetic profiling using profiles composed of only eukaryotes resulted in the loss of the strong correlation between common KEGG pathway membership and profile similarity score. Profile construction methods, orthology definitions, ontology and domain complexity were explored as possible sources of the poor performance of eukaryotic profiles, but with no improvement in results. CONCLUSION: Given the current set of completely sequenced eukaryotic organisms, phylogenetic profiling using profiles generated from any of the commonly used techniques was found to yield extremely poor results. These findings imply genome-specific requirements for constructing functionally relevant phylogenetic profiles, and suggest that differences in the evolutionary history between different kingdoms might generally limit the usefulness of phylogenetic profiling in eukaryotes

Towards the identification of essential genes using targeted genome sequencing and comparative analysis

BACKGROUND: The identification of genes essential for survival is of theoretical importance in the understanding of the minimal requirements for cellular life, and of practical importance in the identification of potential drug targets in novel pathogens. With the great time and expense required for experimental studies aimed at constructing a catalog of essential genes in a given organism, a computational approach which could identify essential genes with high accuracy would be of great value. RESULTS: We gathered numerous features which could be generated automatically from genome sequence data and assessed their relationship to essentiality, and subsequently utilized machine learning to construct an integrated classifier of essential genes in both S. cerevisiae and E. coli. When looking at single features, phyletic retention, a measure of the number of organisms an ortholog is present in, was the most predictive of essentiality. Furthermore, during construction of our phyletic retention feature we for the first time explored the evolutionary relationship among the set of organisms in which the presence of a gene is most predictive of essentiality. We found that in both E. coli and S. cerevisiae the optimal sets always contain host-associated organisms with small genomes which are closely related to the reference. Using five optimally selected organisms, we were able to improve predictive accuracy as compared to using all available sequenced organisms. We hypothesize the predictive power of these genomes is a consequence of the process of reductive evolution, by which many parasites and symbionts evolved their gene content. In addition, essentiality is measured in rich media, a condition which resembles the environments of these organisms in their hosts where many nutrients are provided. Finally, we demonstrate that integration of our most highly predictive features using a probabilistic classifier resulted in accuracies surpassing any individual feature. CONCLUSION: Using features obtainable directly from sequence data, we were able to construct a classifier which can predict essential genes with high accuracy. Furthermore, our analysis of the set of genomes in which the presence of a gene is most predictive of essentiality may suggest ways in which targeted sequencing can be used in the identification of essential genes. In summary, the methods presented here can aid in the reduction of time and money invested in essential gene identification by targeting those genes for experimentation which are predicted as being essential with a high probability

ELISA: Structure-Function Inferences based on statistically significant and evolutionarily inspired observations

The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, ) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind. Availability: The database is available at

High-precision high-coverage functional inference from integrated data sources

<p>Abstract</p> <p>Background</p> <p>Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of knowledge about protein function. The result is a weighted functional linkage network (FLN) in which linked neighbors share at least one function with high probability. Precision is, however, low. Aiming to provide precise functional annotation for as many proteins as possible, we explore and propose a two-step framework for functional annotation (1) construction of a high-coverage and reliable FLN via machine learning techniques (2) development of a decision rule for the constructed FLN to optimize functional annotation.</p> <p>Results</p> <p>We first apply this framework to <it>Saccharomyces cerevisiae</it>. In the first step, we demonstrate that four commonly used machine learning methods, Linear SVM, Linear Discriminant Analysis, Naïve Bayes, and Neural Network, all combine heterogeneous data to produce reliable and high-coverage FLNs, in which the linkage weight more accurately estimates functional coupling of linked proteins than use individual data sources alone. In the second step, empirical tuning of an adjustable decision rule on the constructed FLN reveals that basing annotation on maximum edge weight results in the most precise annotation at high coverages. In particular at low coverage all rules evaluated perform comparably. At coverage above approximately 50%, however, they diverge rapidly. At full coverage, the maximum weight decision rule still has a precision of approximately 70%, whereas for other methods, precision ranges from a high of slightly more than 30%, down to 3%. In addition, a scoring scheme to estimate the precisions of individual predictions is also provided. Finally, tests of the robustness of the framework indicate that our framework can be successfully applied to less studied organisms.</p> <p>Conclusion</p> <p>We provide a general two-step function-annotation framework, and show that high coverage, high precision annotations can be achieved by constructing a high-coverage and reliable FLN via data integration followed by applying a maximum weight decision rule.</p