Utilization of Probabilistic Models in Short Read Assembly from Second-Generation Sequencing

With the advent of cheaper and faster DNA sequencing technologies, assembly methods have greatly changed. Instead of outputting reads that are thousands of base pairs long, new sequencers parallelize the task by producing read lengths between 35 and 400 base pairs. Reconstructing an organism’s genome from these millions of reads is a computationally expensive task. Our algorithm solves this problem by organizing and indexing the reads using n-grams, which are short, fixed-length DNA sequences of length n. These n-grams are used to efficiently locate putative read joins, thereby eliminating the need to perform an exhaustive search over all possible read pairs. Our goal was develop a novel n-gram method for the assembly of genomes from next-generation sequencers. Specifically, a probabilistic, iterative approach was utilized to determine the most likely reads to join through development of a new metric that models the probability of any two arbitrary reads being joined together. Tests were run using simulated short read data based on randomly created genomes ranging in lengths from 10,000 to 100,000 nucleotides with 16 to 20x coverage. We were able to successfully re-assemble entire genomes up to 100,000 nucleotides in length.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Markers Models in Survival Analysis and Applications to Issues Associated with AIDS

Jewell and Kalbfleisch (1992) consider the use of marker processes for applications related to estimation of the survival distribution of time to failure. Marker processes were assumed to be stochastic processes that, at a given point in time, provide information about the current hazard and consequently on the remaining time to failure. Particular attention was paid to calculations based on a simple additive model for the relationship between the hazard function at time t and the history of the marker process up until time t. Specific applications to the analysis of AIDS data included the use of markers as surrogate responses for onset of AIDS with censored data and as predictors of the time elapsed since infection in prevalent individuals. Here we review recent work on the use of marker data to tackle these kinds of problems with AIDS data. The Poisson marker process with an additive model, introduced in Jewell and Kalbfleisch (1992) may be a useful "test" example for comparison of various procedures.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

Human experimental pain models in drug development: translational pain research

Human experimental pain models require standardized stimulation and quantitative assessment of the evoked responses. This approach can be applied to healthy volunteers and pain patients before and after pharmacological interventions. Standardized stimuli of different modalities (ie, mechanical, chemical, thermal or electrical) can be applied to the skin, muscles and viscera for a differentiated and comprehensive assessment of various pain pathways and mechanisms. Using a multi-modal, multi-tissue approach, new and existing analgesic drugs can be profiled by their modulation of specific biomarkers. It has been shown that biomarkers, for example, those related to the central integration of repetitive nociceptive stimuli, can predict efficacy of a given drug in neuropathic pain conditions. Human experimental pain models can bridge animal and clinical pain research, and act as translational research providing new possibilities for designing successful clinical trials. Proof-of-concept studies provide cheap, fast and reliable information on dose-efficacy relationships and how pain sensed in the skin, muscles and viscera are inhibited.

Pathogenesis of bacterial meningitis: contributions by experimental models in rabbits

Rabbits models of bacterial meningitis have contributed substantially to our understanding of the disease, although the technical characteristics of these models only allow the study of specific aspects of the disease. Bacterial multiplication in the subarachnoidal space is not substantially influenced by host defense mechanisms, mainly because of the lack of sufficient amounts of specific antibodies and functional complement in infected CSF. The multiplying bacteria induce profound changes in the blood-brain barrier, an influx of serum proteins into the CSF and the invasion of polymorphonuclear leukocytes at the site of the infection. The presence of polymorphonuclear leukocytes in CSF not only appears to be of limited value in combating the infection, but also seems to produce deleterious effects on the central nervous system. Components of the leukocytes, such as unsaturated fatty acids, arachidonic metabolites and free oxygen radicals, may contribute to the profound hydrodynamic, structural and metabolic changes that are currently under study in experimental models of the disease. A better understanding of the pathophysiology of bacterial meningitis may allow us to design more effective therapeutic strategies and improve the outcome of this disease.

[Neither Descartes nor Freud? current pain models in psychosomatic medicine]

Models explaining chronic pain based on the mere presence or absence of peripheral somatic findings or which view pain of psychological origin when there is no somatic explanation, have their shortcomings. Current scientific knowledge calls for distinct pain concepts, which integrate neurobiological and neuropsychological aspects of pain processing.

Antitumor effect of SIRT1 inhibition in human HCC tumor models in vitro and in vivo

Sirtuins (SIRT1-7) are a highly conserved family of NAD(+)-dependent enzymes that control the activity of histone and nonhistone regulatory proteins. SIRT1 is purposed to promote longevity and to suppress the initiation of some cancers. Nevertheless, SIRT1 is reported to function as a tumor suppressor as well as an oncogenic protein. Our data show that compared with normal liver or surrounding tumor tissue, SIRT1 is strongly overexpressed in human hepatocellular carcinoma (HCC). In addition, human HCC cell lines (Hep3B, HepG2, HuH7, HLE, HLF, HepKK1, skHep1) were screened for the expression of the sirtuin family members and only SIRT1 was consistently overexpressed compared with normal hepatocytes. To determine its effect on HCC growth, SIRT1 activity was inhibited either with lentiviruses expressing short hairpin RNAs or with the small molecule inhibitor, cambinol. Knockdown or inhibition of SIRT1 activity had a cytostatic effect, characterized by an altered morphology, impaired proliferation, an increased expression of differentiation markers, and cellular senescence. In an orthotopic xenograft model, knockdown of SIRT1 resulted in 50% fewer animals developing tumors and cambinol treatment resulted in an overall lower tumor burden. Taken together, our data show that inhibition of SIRT1 in HCC cells impairs their proliferation in vitro and tumor formation in vivo. These data suggest that SIRT1 expression positively influences the growth of HCC and support further studies aimed to block its activity alone or in combination as a novel treatment strategy.

Replications of fundamental research models in ultra high dilutions 1994 and 2015 - update on a bibliometric study

INTRODUCTION This paper focuses exclusively on experimental models with ultra high dilutions (i.e. beyond 10(-23)) that have been submitted to replication scrutiny. It updates previous surveys, considers suggestions made by the research community and compares the state of replication in 1994 with that in 2015. METHODS Following literature research, biochemical, immunological, botanical, cell biological and zoological studies on ultra high dilutions (potencies) were included. Reports were grouped into initial studies, laboratory-internal, multicentre and external replications. Repetition could yield either comparable, or zero, or opposite results. The null-hypothesis was that test and control groups would not be distinguishable (zero effect). RESULTS A total of 126 studies were found. From these, 28 were initial studies. When all 98 replicative studies were considered, 70.4% (i.e. 69) reported a result comparable to that of the initial study, 20.4% (20) zero effect and 9.2% (9) an opposite result. Both for the studies until 1994 and the studies 1995-2015 the null-hypothesis (dominance of zero results) should be rejected. Furthermore, the odds of finding a comparable result are generally higher than of finding an opposite result. Although this is true for all three types of replication studies, the fraction of comparable studies diminishes from laboratory-internal (total 82.9%) to multicentre (total 75%) to external (total 48.3%), while the fraction of opposite results was 4.9%, 10.7% and 13.8%. Furthermore, it became obvious that the probability of an external replication producing comparable results is bigger for models that had already been further scrutinized by the initial researchers. CONCLUSIONS We found 28 experimental models which underwent replication. In total, 24 models were replicated with comparable results, 12 models with zero effect, and 6 models with opposite results. Five models were externally reproduced with comparable results. We encourage further replications of studies in order to learn more about the model systems used.

The wine hedonic price models in the "Old and New World": : state of the art

The basic hedonic hypothesis is that goods are valued for their utility-bearing characteristics and not for the good itself. Each attribute can be evaluated by consumers when making a purchasing decision and an implicit price can be identified for each of them. Thus, the observed price of a certain good can be analyzed as the sum of the implicit prices paid for each quality attribute. Literature has reported hedonic models estimates in the case of wines, which are excellent examples of differentiated goods worldwide.The impact of different wine attributes (intrinsic or extrinsic) on consumers’ willingness to pay has been analyzed with dissimilar results. Wines coming from "New World" producers seem to be appreciated for different attributes than wines produced in the "Old World". Moreover, "Old and New World" consumers seem to value differently the wine’s characteristics. To our knowledge, no cross country analysis has been done dealing with "New World" wines in "Old World" countries, leaving an important gap in understanding underlying attributes influencing buying decisions.

Distributed models in P-Systems architectures to reduce computation time

Membrane systems are computational equivalent to Turing machines. However, their distributed and massively parallel nature obtains polynomial solutions opposite to traditional non-polynomial ones. At this point, it is very important to develop dedicated hardware and software implementations exploiting those two membrane systems features. Dealing with distributed implementations of P systems, the bottleneck communication problem has arisen. When the number of membranes grows up, the network gets congested. The purpose of distributed architectures is to reach a compromise between the massively parallel character of the system and the needed evolution step time to transit from one configuration of the system to the next one, solving the bottleneck communication problem. The goal of this paper is twofold. Firstly, to survey in a systematic and uniform way the main results regarding the way membranes can be placed on processors in order to get a software/hardware simulation of P-Systems in a distributed environment. Secondly, we improve some results about the membrane dissolution problem, prove that it is connected, and discuss the possibility of simulating this property in the distributed model. All this yields an improvement in the system parallelism implementation since it gets an increment of the parallelism of the external communication among processors. Proposed ideas improve previous architectures to tackle the communication bottleneck problem, such as reduction of the total time of an evolution step, increase of the number of membranes that could run on a processor and reduction of the number of processors.