Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

The accuracy of pathological data for the prediction of insignificant prostate cancer

Introduction: The widespread screening programs prompted a decrease in prostate cancer stage at diagnosis, and active surveillance is an option for patients who may harbor clinically insignificant prostate cancer (IPC). Pathologists include the possibility of an IPC in their reports based on the Gleason score and tumor volume. This study determined the accuracy of pathological data in the identification of IPC in radical prostatectomy (RP) specimens. Materials and Methods: Of 592 radical prostatectomy specimens examined in our laboratory from 2001 to 2010, 20 patients harbored IPC and exhibited biopsy findings suggestive of IPC. These biopsy features served as the criteria to define patients with potentially insignificant tumor in this population. The results of the prostate biopsies and surgical specimens of the 592 patients were compared. Results: The twenty patients who had IPC in both biopsy and RP were considered real positive cases. All patients were divided into groups based on their diagnoses following RP: true positives (n = 20), false positives (n = 149), true negatives (n = 421), false negatives (n = 2). The accuracy of the pathological data alone for the prediction of IPC was 91.4%, the sensitivity was 91% and the specificity was 74%. Conclusion: The identification of IPC using pathological data exclusively is accurate, and pathologists should suggest this in their reports to aid surgeons, urologists and radiotherapists to decide the best treatment for their patients.

Comparison of screening methods for conductive hearing loss identification in children: low-cost proposal

Objective To evaluate the effectiveness of two screening methods (imitanciometry screening and questionnaire) to identify children at risk for conductive hearing loss, comparing this data with complete audiologic evaluation. Methods Of 507 children aged between three and six, 111 completed all procedures. The observational methods used were: imitanciometry screening, a questionnaire to identify risk factors for hearing loss and complete audiologic evaluation. Results obtained in the first two instruments were compared with results from complete audiologic evaluation (gold standard). From these comparisons, sensitivity and specificity, accuracy, positive and negative predictive values, and odds ratio were determined for the two screening methods and for the combination of both methods. Results The two methods applied in series (questionnaire and after imitanciometry screening) showed a greater odds ratio and better correlation between sensitivity and proportion of false-positives (ROC curve). Conclusion Combining the two tests in series improved screening accuracy. This combination was the best tool for identifying children at risk for conductive hearing loss.

Transiting exoplanets from the CoRoT space mission XIX. CoRoT-23b: a dense hot Jupiter on an eccentric orbit

We report the detection of CoRoT-23b, a hot Jupiter transiting in front of its host star with a period of 3.6314 +/- 0.0001 days. This planet was discovered thanks to photometric data secured with the CoRoT satellite, combined with spectroscopic radial velocity (RV) measurements. A photometric search for possible background eclipsing binaries conducted at CFHT and OGS concluded with a very low risk of false positives. The usual techniques of combining RV and transit data simultaneously were used to derive stellar and planetary parameters. The planet has a mass of M-p = 2.8 +/- 0.3 M-Jup, a radius of R-pl = 1.05 +/- 0.13 R-Jup, a density of approximate to 3 gcm(-3). RV data also clearly reveal a nonzero eccentricity of e = 0.16 +/- 0.02. The planet orbits a mature G0 main sequence star of V = 15.5 mag, with a mass M-star = 1.14 +/- 0.08 M-circle dot, a radius R-star = 1. 61 +/- 0.18 R-circle dot and quasi-solar abundances. The age of the system is evaluated to be 7 Gyr, not far from the transition to subgiant, in agreement with the rather large stellar radius. The two features of a significant eccentricity of the orbit and of a fairly high density are fairly uncommon for a hot Jupiter. The high density is, however, consistent with a model of contraction of a planet at this mass, given the age of the system. On the other hand, at such an age, circularization is expected to be completed. In fact, we show that for this planetary mass and orbital distance, any initial eccentricity should not totally vanish after 7 Gyr, as long as the tidal quality factor Q(p) is more than a few 10(5), a value that is the lower bound of the usually expected range. Even if CoRoT-23b features a density and an eccentricity that are atypical of a hot Jupiter, it is thus not an enigmatic object.

Detection of gait perturbations based on proprioceptive information. Application to Limit Cycle Walkers

Walking on irregular surfaces and in the presence of unexpected events is a challenging problem for bipedal machines. Up to date, their ability to cope with gait disturbances is far less successful than humans': Neither trajectory controlled robots, nor dynamic walking machines (Limit CycleWalkers) are able to handle them satisfactorily. On the contrary, humans reject gait perturbations naturally and efficiently relying on their sensory organs that, if needed, elicit a recovery action. A similar approach may be envisioned for bipedal robots and exoskeletons: An algorithm continuously observes the state of the walker and, if an unexpected event happens, triggers an adequate reaction. This paper presents a monitoring algorithm that provides immediate detection of any type of perturbation based solely on a phase representation of the normal walking of the robot. The proposed method was evaluated in a Limit Cycle Walker prototype that suffered push and trip perturbations at different moments of the gait cycle, providing 100% successful detections for the current experimental apparatus and adequately tuned parameters, with no false positives when the robot is walking unperturbed.

Influence of small-scale inhomogeneities on the cosmological consistency tests

The current cosmological dark sector (dark matter plus dark energy) is challenging our comprehension about the physical processes taking place in the Universe. Recently, some authors tried to falsify the basic underlying assumptions of such dark matterdark energy paradigm. In this Letter, we show that oversimplifications of the measurement process may produce false positives to any consistency test based on the globally homogeneous and isotropic ? cold dark matter (?CDM) model and its expansion history based on distance measurements. In particular, when local inhomogeneity effects due to clumped matter or voids are taken into account, an apparent violation of the basic assumptions (Copernican Principle) seems to be present. Conversely, the amplitude of the deviations also probes the degree of reliability underlying the phenomenological DyerRoeder procedure by confronting its predictions with the accuracy of the weak lensing approach. Finally, a new method is devised to reconstruct the effects of the inhomogeneities in a ?CDM model, and some suggestions of how to distinguish between clumpiness (or void) effects from different cosmologies are discussed.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

The performance of four molecular methods for the laboratory diagnosis of congenital toxoplasmosis in amniotic fluid samples

Introduction Toxoplasmosis may be life-threatening in fetuses and in immune-deficient patients. Conventional laboratory diagnosis of toxoplasmosis is based on the presence of IgM and IgG anti-Toxoplasma gondii antibodies; however, molecular techniques have emerged as alternative tools due to their increased sensitivity. The aim of this study was to compare the performance of 4 PCR-based methods for the laboratory diagnosis of toxoplasmosis. One hundred pregnant women who seroconverted during pregnancy were included in the study. The definition of cases was based on a 12-month follow-up of the infants. Methods Amniotic fluid samples were submitted to DNA extraction and amplification by the following 4 Toxoplasma techniques performed with parasite B1 gene primers: conventional PCR, nested-PCR, multiplex-nested-PCR, and real-time PCR. Seven parameters were analyzed, sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and efficiency (Ef). Results Fifty-nine of the 100 infants had toxoplasmosis; 42 (71.2%) had IgM antibodies at birth but were asymptomatic, and the remaining 17 cases had non-detectable IgM antibodies but high IgG antibody titers that were associated with retinochoroiditis in 8 (13.5%) cases, abnormal cranial ultrasound in 5 (8.5%) cases, and signs/symptoms suggestive of infection in 4 (6.8%) cases. The conventional PCR assay detected 50 cases (9 false-negatives), nested-PCR detected 58 cases (1 false-negative and 4 false-positives), multiplex-nested-PCR detected 57 cases (2 false-negatives), and real-time-PCR detected 58 cases (1 false-negative). Conclusions The real-time PCR assay was the best-performing technique based on the parameters of Se (98.3%), Sp (100%), PPV (100%), NPV (97.6%), PLR (â^ž), NLR (0.017), and Ef (99%).

Useful computer vision techniques for human-robot interaction

[ES]This paper describes some simple but useful computer vision techniques for human-robot interaction. First, an omnidirectional camera setting is described that can detect people in the surroundings of the robot, giving their angular positions and a rough estimate of the distance. The device can be easily built with inexpensive components. Second, we comment on a color-based face detection technique that can alleviate skin-color false positives. Third, a simple head nod and shake detector is described, suitable for detecting affirmative/negative, approval/dissaproval, understanding/disbelief head gestures.

Domain-Specific Program Checking

Lint-like program checkers are popular tools that ensure code quality by verifying compliance with best practices for a particular programming language. The proliferation of internal domain-specific languages and models, however, poses new challenges for such tools. Traditional program checkers produce many false positives and fail to accurately check constraints, best practices, common errors, possible optimizations and portability issues particular to domain-specific languages. We advocate the use of dedicated rules to check domain-specific practices. We demonstrate the implementation of domain-specific rules, the automatic fixing of violations, and their application to two case-studies: (1) Seaside defines several internal DSLs through a creative use of the syntax of the host language; and (2) Magritte adds meta-descriptions to existing code by means of special methods. Our empirical validation demonstrates that domain-specific program checking significantly improves code quality when compared with general purpose program checking.

Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker

A marker that is strongly associated with outcome (or disease) is often assumed to be effective for classifying individuals according to their current or future outcome. However, for this to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiological studies. An illustration of the relationship between odds ratios and receiver operating characteristic (ROC) curves shows, for example, that a marker with an odds ratio as high as 3 is in fact a very poor classification tool. If a marker identifies 10 percent of controls as positive (false positives) and has an odds ratio of 3, then it will only correctly identify 25 percent of cases as positive (true positives). Moreover, the authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker’s ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. The serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.

REMOVING TECHNICAL VARIABILITY IN RNA-SEQ DATA USING CONDITIONAL QUANTILE NORMALIZATION

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade’s worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show RNA-seq data demonstrates unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find GC-content has a strong sample specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here we describe statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization (CQN) algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content, and quantile normalization to correct for global distortions.

Single-exposure dual-energy subtraction chest radiography: detection of pulmonary nodules and masses in clinical practice

The purpose of this retrospective study was to evaluate the impact of energy subtraction (ES) chest radiography on the detection of pulmonary nodules and masses in daily routine. Seventy-seven patients and 25 healthy subjects were examined with a single exposure digital radiography system. Five blinded readers evaluated first the non-subtracted PA and lateral chest radiographs alone and then together with the subtracted PA soft tissue images. The size, location and number of lung nodules or masses were registered with the confidence level. CT was used as standard of reference. For the 200 total lesions, a sensitivity of 33.5-52.5% was found at non-subtracted and a sensitivity of 43.5-58.5% at energy-subtracted radiography, corresponding to a significant improvement in four of five readers (p < 0.05). However, in three of five readers the rate of false positives was higher with ES. With ES, sensitivity, but not the area under the alternative free-response receiver operating characteristics (AFROC) curve, showed a good correlation with reader experience (R = 0.90, p = 0.026). In four of five readers, the diagnostic confidence improved with ES (p = 0.0036). We conclude that single-exposure digital ES chest radiography improves detection of most pulmonary nodules and masses, but identification of nodules <1 cm and false-positive findings remain a problem.

Prenatal sonographic diagnosis of skeletal dysplasias

OBJECTIVE: To assess the types and numbers of cases, gestational age at specific prenatal diagnosis and diagnostic accuracy of the diagnosis of skeletal dysplasias in a prenatal population from a single tertiary center. METHODS: This was a retrospective database review of type, prenatal and definitive postnatal diagnoses and gestational age at specific prenatal diagnosis of all cases of skeletal dysplasias from a mixed referral and screening population between 1985 and 2007. Prenatal diagnoses were grouped into 'correct ultrasound diagnosis' (complete concordance with postnatal pediatric or pathological findings) or 'partially correct ultrasound diagnosis' (skeletal dysplasias found postnatally to be a different one from that diagnosed prenatally). RESULTS: We included 178 fetuses in this study, of which 176 had a prenatal ultrasound diagnosis of 'skeletal dysplasia'. In 160 cases the prenatal diagnosis of a skeletal dysplasia was confirmed; two cases with skeletal dysplasias identified postnatally had not been diagnosed prenatally, giving 162 fetuses with skeletal dysplasias in total. There were 23 different classifiable types of skeletal dysplasia. The specific diagnoses based on prenatal ultrasound examination alone were correct in 110/162 (67.9%) cases and partially correct in 50/162 (30.9%) cases, (160/162 overall, 98.8%). In 16 cases, skeletal dysplasia was diagnosed prenatally, but was not confirmed postnatally (n = 12 false positives) or the case was lost to follow-up (n = 4). The following skeletal dysplasias were recorded: thanatophoric dysplasia (35 diagnosed correctly prenatally of 40 overall), osteogenesis imperfecta (lethal and non-lethal, 31/35), short-rib dysplasias (5/10), chondroectodermal dysplasia Ellis-van Creveld (4/9), achondroplasia (7/9), achondrogenesis (7/8), campomelic dysplasia (6/8), asphyxiating thoracic dysplasia Jeune (3/7), hypochondrogenesis (1/6), diastrophic dysplasia (2/5), chondrodysplasia punctata (2/2), hypophosphatasia (0/2) as well as a further 7/21 cases with rare or unclassifiable skeletal dysplasias. CONCLUSION: Prenatal diagnosis of skeletal dysplasias can present a considerable diagnostic challenge. However, a meticulous sonographic examination yields high overall detection. In the two most common disorders, thanatophoric dysplasia and osteogenesis imperfecta (25% and 22% of all cases, respectively), typical sonomorphology accounts for the high rates of completely correct prenatal diagnosis (88% and 89%, respectively) at the first diagnostic examination.

Prediction of 1-year mortality in patients with acute coronary syndromes undergoing percutaneous coronary intervention: validation of the logistic clinical SYNTAX (Synergy Between Percutaneous Coronary Interventions With Taxus and Cardiac Surgery) score

OBJECTIVES This study sought to validate the Logistic Clinical SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) score in patients with non-ST-segment elevation acute coronary syndromes (ACS), in order to further legitimize its clinical application. BACKGROUND The Logistic Clinical SYNTAX score allows for an individualized prediction of 1-year mortality in patients undergoing contemporary percutaneous coronary intervention. It is composed of a "Core" Model (anatomical SYNTAX score, age, creatinine clearance, and left ventricular ejection fraction), and "Extended" Model (composed of an additional 6 clinical variables), and has previously been cross validated in 7 contemporary stent trials (>6,000 patients). METHODS One-year all-cause death was analyzed in 2,627 patients undergoing percutaneous coronary intervention from the ACUITY (Acute Catheterization and Urgent Intervention Triage Strategy) trial. Mortality predictions from the Core and Extended Models were studied with respect to discrimination, that is, separation of those with and without 1-year all-cause death (assessed by the concordance [C] statistic), and calibration, that is, agreement between observed and predicted outcomes (assessed with validation plots). Decision curve analyses, which weight the harms (false positives) against benefits (true positives) of using a risk score to make mortality predictions, were undertaken to assess clinical usefulness. RESULTS In the ACUITY trial, the median SYNTAX score was 9.0 (interquartile range 5.0 to 16.0); approximately 40% of patients had 3-vessel disease, 29% diabetes, and 85% underwent drug-eluting stent implantation. Validation plots confirmed agreement between observed and predicted mortality. The Core and Extended Models demonstrated substantial improvements in the discriminative ability for 1-year all-cause death compared with the anatomical SYNTAX score in isolation (C-statistics: SYNTAX score: 0.64, 95% confidence interval [CI]: 0.56 to 0.71; Core Model: 0.74, 95% CI: 0.66 to 0.79; Extended Model: 0.77, 95% CI: 0.70 to 0.83). Decision curve analyses confirmed the increasing ability to correctly identify patients who would die at 1 year with the Extended Model versus the Core Model versus the anatomical SYNTAX score, over a wide range of thresholds for mortality risk predictions. CONCLUSIONS Compared to the anatomical SYNTAX score alone, the Core and Extended Models of the Logistic Clinical SYNTAX score more accurately predicted individual 1-year mortality in patients presenting with non-ST-segment elevation acute coronary syndromes undergoing percutaneous coronary intervention. These findings support the clinical application of the Logistic Clinical SYNTAX score.