Probabilistic divergence time estimation without branch lengths: dating the origins of dinosaurs, avian flight and crown birds

Branch lengths—measured in character changes—are an essential requirement of clock-based divergence estimation, regardless of whether the fossil calibrations used represent nodes or tips. However, a separate set of divergence time approaches are typically used to date palaeontological trees, which may lack such branch lengths. Among these methods, sophisticated probabilistic approaches have recently emerged, in contrast with simpler algorithms relying on minimum node ages. Here, using a novel phylogenetic hypothesis for Mesozoic dinosaurs, we apply two such approaches to estimate divergence times for: (i) Dinosauria, (ii) Avialae (the earliest birds) and (iii) Neornithes (crown birds). We find: (i) the plausibility of a Permian origin for dinosaurs to be dependent on whether Nyasasaurus is the oldest dinosaur, (ii) a Middle to Late Jurassic origin of avian flight regardless of whether Archaeopteryx or Aurornis is considered the first bird and (iii) a Late Cretaceous origin for Neornithes that is broadly congruent with other node- and tip-dating estimates. Demonstrating the feasibility of probabilistic time-scaling further opens up divergence estimation to the rich histories of extinct biodiversity in the fossil record, even in the absence of detailed character data

Density-Based Unsupervised Classification for Remote Sensing *

Most image classification methods are supervised and use a parametric model of the classes that have to be detected. The models of the different classes are trained by means of a set of training regions that usually have to be marked and classified by a human interpreter. Unsupervised classification methods are data-driven methods that do not use such a set of training samples. Instead, these methods look for (repeated) structures in the data. In this paper we describe a non-parametric unsupervised classification method. The method uses biased sampling to obtain a learning sample with little noise. Next, density estimation based clustering is used to find the structure in the learning data. The method generates a non-parametric model for each of the classes and uses these models to classify the pixels in the image

Why the Realist-Instrumentalist Debate about Rational Choice Rests on a Mistake

Within the social sciences, much controversy exists about which status should be ascribed to the rationality assumption that forms the core of rational choice theories. Whilst realists argue that the rationality assumption is an empirical claim which describes real processes that cause individual action, instrumentalists maintain that it amounts to nothing more than an analytically set axiom or ‘as if’ hypothesis which helps in the generation of accurate predictions. In this paper, I argue that this realist-instrumentalist debate about rational choice theory can be overcome once it is realised that the rationality assumption is neither an empirical description nor an ‘as if’ hypothesis, but a normative claim

Seir immune strategy for instance weighted naive bayes classification

© Springer International Publishing Switzerland 2015. Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods

A role for the collagen I/III and MMP-1/-13 genes in primary inguinal hernia?

BACKGROUND: Abnormal collagen metabolism is thought to play an important role in the development of primary inguinal hernia. This is underlined by detection of altered collagen metabolism and structural changes of the tissue in patients with primary inguinal hernia. However, it is still unknown whether these alterations reflect a basic dysfunction of the collagen synthesis, or of collagen degradation. METHODS: In the present study, we analysed type I and type III procollagen messenger ribonucleic acid (mRNA) and MMP-1 and MMP-13 mRNA in cultured fibroblasts from the skin of patients with primary inguinal hernia, and from patients without hernia (controls) by reverse transcription polymerase chain reaction (RT-PCR) and Northern Blot. RESULTS: The results indicated that the ratio of type I to type III procollagen mRNA was decreased in patients with primary hernia, showing significant differences as compared to controls (p = 0.01). This decrease was mainly due to the increase of type III procollagen mRNA. Furthermore, RT-PCR analysis revealed that the expression of MMP-1 mRNA in patients with primary hernia is equivalent to that of controls (p > 0.05). In addition, MMP-13 mRNA is expressed neither in patients with primary hernia nor in controls. CONCLUSION: We concluded that abnormal change of type I and type III collagen mRNAs contribute to the development of primary inguinal hernia, whereas the expressions of MMP-1 and MMP-13 mRNA appears not to be involved in the development of primary inguinal hernia. Thus, the knowledge on the transcriptional regulation of collagen in patients with primary inguinal hernia may help to understand the pathogenesis of primary inguinal hernia, and implies new therapeutic strategies for this disease

Propagation of chaos for rank-based interacting diffusions and long time behaviour of a scalar quasilinear parabolic equation

We study a quasilinear parabolic Cauchy problem with a cumulative distribution function on the real line as an initial condition. We call 'probabilistic solution' a weak solution which remains a cumulative distribution function at all times. We prove the uniqueness of such a solution and we deduce the existence from a propagation of chaos result on a system of scalar diffusion processes, the interactions of which only depend on their ranking. We then investigate the long time behaviour of the solution. Using a probabilistic argument and under weak assumptions, we show that the flow of the Wasserstein distance between two solutions is contractive. Under more stringent conditions ensuring the regularity of the probabilistic solutions, we finally derive an explicit formula for the time derivative of the flow and we deduce the convergence of solutions to equilibrium.Comment: Stochastic partial differential equations: analysis and computations (2013) http://dx.doi.org/10.1007/s40072-013-0014-

Demographic, risk behaviour and personal network variables associated with prevalent hepatitis C, hepatitis B, and HIV infection in injection drug users in Winnipeg, Canada

BACKGROUND: Previous studies have used social network variables to improve our understanding of HIV transmission. Similar analytic approaches have not been undertaken for hepatitis C (HCV) or B (HBV), nor used to conduct comparative studies on these pathogens within a single setting. METHODS: A cross-sectional survey consisting of a questionnaire and blood sample was conducted on injection drug users in Winnipeg between December 2003 and September 2004. Logistic regression analyses were used to correlate respondent and personal network data with HCV, HBV and HIV prevalence. RESULTS: At the multivariate level, pathogen prevalence was correlated with both respondent and IDU risk network variables. Pathogen transmission was associated with several distinct types of high-risk networks formed around specific venues (shooting galleries, hotels) or within users who are linked by their drug use preferences. Smaller, isolated pockets of IDUs also appear to exist within the larger population where behavioural patterns pose a lesser risk, unless or until, a given pathogen enters those networks. CONCLUSION: The findings suggest that consideration of both respondent and personal network variables can assist in understanding the transmission patterns of HCV, HBV, and HIV. It is important to assess these effects for multiple pathogens within one setting as the associations identified and the direction of those associations can differ between pathogens

Mining multi-item drug adverse effect associations in spontaneous reporting systems

<p>Abstract</p> <p>Background</p> <p>Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.</p> <p>Results</p> <p>Based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions.</p> <p>Conclusions</p> <p>Our findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.</p

Do Physicians Know When Their Diagnoses Are Correct?

This study explores the alignment between physicians' confidence in their diagnoses and the “correctness” of these diagnoses, as a function of clinical experience, and whether subjects were prone to over-or underconfidence. Design : Prospective, counterbalanced experimental design. Setting : Laboratory study conducted under controlled conditions at three academic medical centers. Participants : Seventy-two senior medical students, 72 senior medical residents, and 72 faculty internists. Intervention : We created highly detailed, 2-to 4-page synopses of 36 diagnostically challenging medical cases, each with a definitive correct diagnosis. Subjects generated a differential diagnosis for each of 9 assigned cases, and indicated their level of confidence in each diagnosis. Measurements And Main Results : A differential was considered “correct” if the clinically true diagnosis was listed in that subject's hypothesis list. To assess confidence, subjects rated the likelihood that they would, at the time they generated the differential, seek assistance in reaching a diagnosis. Subjects' confidence and correctness were “mildly” aligned (Κ=.314 for all subjects, .285 for faculty, .227 for residents, and .349 for students). Residents were overconfident in 41% of cases where their confidence and correctness were not aligned, whereas faculty were overconfident in 36% of such cases and students in 25%. Conclusions : Even experienced clinicians may be unaware of the correctness of their diagnoses at the time they make them. Medical decision support systems, and other interventions designed to reduce medical errors, cannot rely exclusively on clinicians' perceptions of their needs for such support.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74850/1/j.1525-1497.2005.30145.x.pd

Parameter identification problems in the modelling of cell motility

We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells. Two alternative formulations of the objective functional that measures the difference between the computed and observed data are proposed and the parameter identification problem is formulated as a minimisation problem of nonlinear least squares type. A Levenberg–Marquardt based optimisation method is applied to the solution of the minimisation problem and the details of the implementation are discussed. A number of numerical experiments are presented which illustrate the robustness of the algorithm to parameter identification in the presence of large deformations and noisy data and parameter identification in three dimensional models of cell motility. An application to experimental data is also presented in which we seek to identify parameters in a model for the monopolar growth of fission yeast cells using experimental imaging data. Our numerical tests allow us to compare the method with the two different formulations of the objective functional and we conclude that the results with both objective functionals seem to agree