Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to computer vision allow us to overcome this difficulty by explicitly modeling the physical image formation process. Using generative object models, the analysis of an observed image is performed via Bayesian inference of the posterior distribution. This conceptually simple approach tends to fail in practice because of several difficulties stemming from sampling the posterior distribution: high-dimensionality and multi-modality of the posterior distribution as well as expensive simulation of the rendering process. The main difficulty of sampling approaches in a computer vision context is choosing the proposal distribution accurately so that maxima of the posterior are explored early and the algorithm quickly converges to a valid image interpretation. In this work, we propose to use a Bayesian Neural Network for estimating an image dependent proposal distribution. Compared to a standard Gaussian random walk proposal, this accelerates the sampler in finding regions of the posterior with high value. In this way, we can significantly reduce the number of samples needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201

Greedy Structure Learning of Hierarchical Compositional Models

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of im-ages which show examples of the object in the presenceof variable background clutter. Existing approaches tothis problem are limited by making strong a-priori assump-tions about the object’s geometric structure and require seg-mented training data for learning. In this paper, we pro-pose a novel framework for learning hierarchical compo-sitional models (HCMs) which do not suffer from the men-tioned limitations. We present a generalized formulation ofHCMs and describe a greedy structure learning frameworkthat consists of two phases: Bottom-up part learning andtop-down model composition. Our framework integratesthe foreground-background segmentation problem into thestructure learning task via a background model. As a result, we can jointly optimize for the number of layers in thehierarchy, the number of parts per layer and a foreground-background segmentation based on class labels only. Weshow that the learned HCMs are semantically meaningfuland achieve competitive results when compared to othergenerative object models at object classification on a stan-dard transfer learning dataset

Decreased Linezolid Serum Concentrations in Three Critically Ill Patients: Clinical Case Studies of a Potential Drug Interaction between Linezolid and Rifampicin

Linezolid is a valuable treatment option for treating infections caused by multi-resistant gram-positive pathogens. Lack of effective linezolid levels due to the co-administration of rifampicin has been described in healthy subjects. However, the clinical significance of this potential drug interaction (DI) for critically ill patients is still unclear. This was a retrospective analysis of 3 critically ill patients with the combination therapy of linezolid and rifampicin or rifampicin pre-treatment. Despite increasing the dose of linezolid, the majority of observed linezolid trough concentrations in all 3 patients were below 2 mg/l. Furthermore, linezolid trough concentrations remained below 2 mg/l after discontinuation of rifampicin. This potential DI between linezolid and rifampicin could lead to treatment failure. Therefore, we strongly recommend that linezolid serum concentrations be monitored in patients with rifampicin co-administration or rifampicin pretreatment. (C) 2016 S. Karger AG, Base

Cerebrospinal fluid penetration of meropenem in neurocritical care patients with proven or suspected ventriculitis: a prospective observational study

Background: Ventriculitis is a complication of temporary intraventricular drains. The limited penetration of meropenem into the cerebrospinal fluid (CSF) is well known. However, ventricular CSF pharmacokinetic data in patients with ventriculitis are lacking. The aim of this study was to evaluate meropenem pharmacokinetics in the serum and CSF of neurocritical care patients with proven or suspected ventriculitis. Methods: We conducted an observational pharmacokinetic study of neurocritical care patients with proven or suspected ventriculitis receiving meropenem. Multiple blood and CSF samples were taken and were described using nonparametric pharmacokinetic modelling with Pmetrics. Results: In total, 21 patients (median age 52 years, median weight 76 kg) were included. The median (range) of peak and trough concentrations in serum were 20.16 (4.40-69.00) mg/L and 2.54 (0.00-31.40) mg/L, respectively. The corresponding peak and trough concentrations in CSF were 1.20 (0.00-6.20) mg/L and 1.28 (0.00-4.10) mg/L, respectively, with a median CSF/serum ratio (range) of 0.09 (0.03-0.16). Median creatinine clearance ranged from 60. 7 to 217.6 ml/minute (median 122.5 ml/minute). A three-compartment linear population pharmacokinetic model was most appropriate. No covariate relationships could be supported for any of the model parameters. Meropenem demonstrated poor penetration into CSF, with a median CSF/serum ratio of 9 % and high interindividual pharmacokinetic variability. Conclusions: Administration of higher-than-standard doses of meropenem and therapeutic drug monitoring in both serum and CSF should be considered to individualise meropenem dosing in neurocritical care patients with ventriculitis

The mediating role of innovation in the relationship between market orientation and university performance in Pakistan

Universities globally are going through a paradigm shift with a need to become more innovatively market-oriented to handle the issue of growing competition for funding, as well as attracting/retaining the international/local competent students and academicians. However, there appears to be a dearth of research on how such state of affairs could be addressed, particularly in the emerging economies like Pakistan. In the light of resource-based theory (RBT), as well as organizational-learning theory (OLT), literature suggests that market-orientation (MO) and innovation are to be the desirable unique resources, as well as the guiding philosophies, to enable universities for a more competitive performance. Hence, this study investigated how resources like marketorientation (MO), and innovation, can influence university performance (UP). The study also tested empirically the potential mediating effect of innovation on the MOUP relationship. In addition, how the dimensions of MO influenced the innovation and university performance (UP) were also tested empirically in the universities of Pakistan. Results of the PLS path modelling (with 369 respondents from the target public-sector universities) firstly confirmed significant effect of the “universal construct of MO” and two of its dimensions “the advising and mentoring, as well as the intelligencegeneration and response” on UP. However, one dimension of MO, which is the administration-leadership, was not significantly supported to directly influence the UP. Secondly, the study confirmed that there were significant direct effects of the “universal construct of MO”, as well as all of its dimensions, on innovation. Thirdly, the study also found that there was a significant effect of innovation on UP. Furthermore, the bootstrapping results found significant mediation of innovation between the MO-UP relationship. Hence, the results show that UP can be directly enhanced through MO and innovation. Even the use of innovation as a mediator can further strengthen the MO-UP relationship. Based on the findings, the study offers theoretical and practical implications, followed by its limitations and directions, for future research

Nutritional Value of the Duckweed Species of the Genus Wolffia (Lemnaceae) as Human Food

Species of the genus Wolffia are traditionally used as human food in some of the Asian countries. Therefore, all 11 species of this genus, identified by molecular barcoding, were investigated for ingredients relevant to human nutrition. The total protein content varied between 20 and 30% of the freeze-dry weight, the starch content between 10 and 20%, the fat content between 1 and 5%, and the fiber content was ~25%. The essential amino acid content was higher or close to the requirements of preschool-aged children according to standards of the World Health Organization. The fat content was low, but the fraction of polyunsaturated fatty acids was above 60% of total fat and the content of n-3 polyunsaturated fatty acids was higher than that of n-6 polyunsaturated fatty acids in most species. The content of macro- and microelements (minerals) not only depended on the cultivation conditions but also on the genetic background of the species. This holds true also for the content of tocopherols, several carotenoids and phytosterols in different species and even intraspecific, clonal differences were detected in Wolffia globosa and Wolffia arrhiza. Thus, the selection of suitable clones for further applications is important. Due to the very fast growth and the highest yield in most of the nutrients, Wolffia microscopica has a high potential for practical applications in human nutrition

Prospective evaluation of prognostic factors uPA/PAI-1 in node-negative breast cancer: Phase III NNBC3-Europe trial (AGO, GBG, EORTC-PBG) comparing 6 × FEC versus 3 × FEC/3 × Docetaxel

Contains fulltext : 98255.pdf (publisher's version ) (Open Access)BACKGROUND: Today, more than 70% of patients with primary node-negative breast cancer are cured by local therapy alone. Many patients receive overtreatment by adjuvant chemotherapy due to inadequate risk assessment. So far, few clinical trials have prospectively evaluated tumor biology based prognostic factors. Risk assessment by a biological algorithm including invasion factors urokinase-type plasminogen activator (uPA) and its inhibitor plasminogen activator inhibitor type 1 (PAI-1) will assess up to 35-55% of node-negative patients as low-risk and thus avoid chemotherapy. In contrast, a clinical-pathological algorithm will only classify 20-40% of patients as low-risk. High-risk node-negative patients should receive chemotherapy. Anthracycline-based regimens are accepted as a standard, the additional benefit of taxanes remains an open question. METHODS/DESIGN: The international NNBC3 ("Node Negative Breast Cancer 3-Europe") trial compares biological risk assessment (UP) using invasion factors uPA/PAI-1 with a clinical-pathological algorithm (CP). In this trial, the type of risk assessment (CP or UP) was chosen upfront by each center for its patients. Fresh frozen tissue was obtained to determine uPA/PAI-1 using an enzyme-linked immunosorbent assay (ELISA). Patients assessed as high-risk were stratified by human epidermal growth factor receptor 2 (HER2) status and then randomised to receive anthracycline-containing chemotherapy 5-Fluorouracil (F)/Epirubicin (E)/Cyclophosphymide (C) or an anthracycline-taxane sequence (FE(100)C*6 versus FE(100)C*3 followed by Docetaxel(100)*3). DISCUSSION: In this trial, 4,149 node-negative patients with operable breast cancer from 153 centers in Germany and France were included since 2002. Measurement of uPA/PAI-1 by ELISA was performed with standardised central quality assurance for 2,497 patients (60%) from 56 "UP"-centers. The NNBC 3-Europe trial showed that inclusion of patients into a clinical phase III trial is feasible based on biological testing of fresh frozen tumor material. In addition, 2,661 patients were classified as high-risk and thus received chemotherapy. As adjuvant chemotherapy, 1,334 high-risk patients received FE(100)C-Docetaxel(100), and 1,327 received French FE(100)C. No unexpected toxicities were observed. Chemotherapy efficacy and comparison of UP with CP will be evaluated after longer follow-up. TRIAL REGISTRATION: clinical Trials.gov NCT01222052