Improvement of the liquid-chromatographic analysis of protein tryptic digests by the use of long-capillary monolithic columns with UV and MS detection

Optimisation of peak capacity is an important strategy in gradient liquid chromatography (LC). This can be achieved by using either long columns or columns packed with small particles. Monolithic columns allow the use of long columns at relatively low back-pressure. The gain in peak capacity using long columns was evaluated by the separation of a tryptic bovine serum albumin digest with an LC–UV–mass spectrometry (MS) system and monolithic columns of different length (150 and 750 mm). Peak capacities were determined from UV chromatograms and MS/MS data were used for Mascot database searching. Analyses with a similar gradient slope for the two columns produced ratios of the peak capacities that were close to the expected value of the square root of the column length ratio. Peak capacities of the short column were 12.6 and 25.0 with 3 and 15 min gradients, respectively, and 29.7 and 41.0 for the long column with 15 and 75 min gradients, respectively. Protein identification scores were also higher for the long column, 641 and 750 for the 3- and 15-min gradients with the short column and 1,376 and 993 for the 15- and 75-min gradients with the long column. Thus, the use of long monolithic columns provides improved peptide separation and increased reliability of protein identification

MicroMotility: State of the art, recent accomplishments and perspectives on the mathematical modeling of bio-motility at microscopic scales

Mathematical modeling and quantitative study of biological motility (in particular, of motility at microscopic scales) is producing new biophysical insight and is offering opportunities for new discoveries at the level of both fundamental science and technology. These range from the explanation of how complex behavior at the level of a single organism emerges from body architecture, to the understanding of collective phenomena in groups of organisms and tissues, and of how these forms of swarm intelligence can be controlled and harnessed in engineering applications, to the elucidation of processes of fundamental biological relevance at the cellular and sub-cellular level. In this paper, some of the most exciting new developments in the fields of locomotion of unicellular organisms, of soft adhesive locomotion across scales, of the study of pore translocation properties of knotted DNA, of the development of synthetic active solid sheets, of the mechanics of the unjamming transition in dense cell collectives, of the mechanics of cell sheet folding in volvocalean algae, and of the self-propulsion of topological defects in active matter are discussed. For each of these topics, we provide a brief state of the art, an example of recent achievements, and some directions for future research

Black-box policy search with probabilistic programs

In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box variational methods which generalize to probabilistic program inference. We present case studies in the Canadian traveler problem, Rock Sample, and a benchmark for optimal diagnosis inspired by Guess Who. Each study illustrates how programs can efficiently represent policies using moderate numbers of parameters

Learning disentangled representations with semi-supervised deep generative models

Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets

Learning disentangled representations with semi-supervised deep generative models

Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets

Structured disentangled representations

Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation by introducing modifications to the standard objective function. These approaches generally assume a simple diagonal Gaussian prior and as a result are not able to reliably disentangle discrete factors of variation. We propose a two-level hierarchical objective to control relative degree of statistical independence between blocks of variables and individual variables within blocks. We derive this objective as a generalization of the evidence lower bound, which allows us to explicitly represent the trade-offs between mutual information between data and representation, KL divergence between representation and prior, and coverage of the support of the empirical data distribution. Experiments on a variety of datasets demonstrate that our objective can not only disentangle discrete variables, but that doing so also improves disentanglement of other variables and, importantly, generalization even to unseen combinations of factors

The effectiveness and cost evaluation of pain exposure physical therapy and conventional therapy in patients with complex regional pain syndrome type 1. Rationale and design of a randomized controlled trial

Contains fulltext : 109801.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: Pain Exposure Physical Therapy is a new treatment option for patients with Complex Regional Pain Syndrome type 1. It has been evaluated in retrospective as well as in prospective studies and proven to be safe and possibly effective. This indicates that Pain Exposure Physical Therapy is now ready for clinical evaluation. The results of an earlier performed pilot study with an n = 1 design, in which 20 patients with Complex Regional Pain Syndrome type 1 were treated with Pain Exposure Physical Therapy, were used for the design and power calculation of the present study. After completion and evaluation of this phase III study, a multi-centre implementation study will be conducted. The aim of this study is to determine whether Pain Exposure Physical Therapy can improve functional outcomes in patients with Complex Regional Pain Syndrome type 1. Methods/design This study is designed as a single-blinded, randomized clinical trial. 62 patients will be randomized with a follow-up of 9 months to demonstrate the expected treatment effect. Complex Regional Pain Syndrome type 1 is diagnosed in accordance with the Bruehl/International Association for the Study of Pain criteria. Conventional therapy in accordance with the Dutch guideline will be compared with Pain Exposure Physical Therapy. Primary outcome measure is the Impairment level SumScore, restricted version. DISCUSSION: This is the first randomized controlled study with single blinding that has ever been planned in patients with Complex Regional Pain Syndrome type 1 and does not focus on a single aspect of the pain syndrome but compares treatment strategies based on completely different pathophysiological and cognitive theories. Trial registration Clinical trials NCT00817128; National Trial Register NTR2090

Integrated criteria document propylene oxide

Bij dit rapport hoort een losse bijlage met rapportnummer 758473008 getiteld: Propylene oxide Integrated Criteria Document Effects.<br>Onderhavig document omvat gegevens over propyleenoxide inzake de bronnen en het verspreidingspatroon (bodem, water, lucht, biota), de risico's op basis van afweging van blootstellingsconcentraties en -routes enerzijds en schadelijke concentraties voor mens, (onderdelen van) ecosystemen en materialen anderzijds, en de technische mogelijkheden en economische gevolgen met betrekking tot reductie van deze risico's. Deze informatie dienst als wetenschappelijke basis voor het formuleren van het effectgericht normstellingsbeleid.DGMH/BWS-S /Cornet JPWijnen HLT

Ontwerp Basisdocument Propyleenoxide

Betreft de engelse editie van rapport nr. 758473001. Bij dit rapport hoort een losse bijlage met hetzelfde rapportnummer.<br>Dit rapport bevat een systematisch overzicht en een kritische evaluatie van de belangrijkste gegevens over de prioritaire stof propyleenoxide ten behoeve van het effectgericht milieubeleid.DGM/SR /Cornet J