Synthesis and properties of a new AB-cross-linked copolymer membrane system

The alcohol permeability and permselectivity properties as well as the morphology of membranes made of a newly developed AB-cross-linked copolymer system composed of elastomeric and glassy components were investigated. The copolymer was synthesized by a hydrosilylation reaction between poly(styrene-stat-isoprenes) (Mn from 40,000 to 100,000 g/mol) with high content in unsaturated side groups (≈ 60% of entire isoprene content) and polyhydrogen polysiloxanes with varying SiH content (0.75 10.7 mol %) and molecular mass, Mn, from 2,500 to 36,000 g/mol. A two-track approach was taken to determine the morphology of the copolymer system. The first employed the usual polymer characterization methods such as electron microscopy, DSC, IR spectroscopy, the density gradient method, and mechanical measurements. For the second approach, different copolymer permeability models were tested so as to give an insight into the copolymer morphology. As a final step, the permeability and permselectivity properties were correlated with the morphological structure of the copolymer system. It was observed that the respective continuous microphase dominated the copolymer's physical properties, as, e.g., permeability, permselectivity, and mechanical properties. The microphase inversion in the copolymer system was proved by the permeability/permselectivity as well as by the mechanical measurements

Facultative secondary lecithotrophy in the megalopa of the shrimp Lysmata seticaudata (Risso, 1816) (Decapoda : Hippolytidae) under laboratory conditions

Certain decapod crustaceans can catabolize internal reserves to undergo partial or full larval development. This feature is termed secondary lecithotrophy, if energy used results from plankton derived organic matter accumulated Ig earlier larval stages. The present work reports the ability of Lysmata seticaudata megalopa to moll to the first juvenile stage in the absence of food. Unlike previous records of secondary lecithotrophy displayed by nonfeeding last larval stages of hermit crabs and spiny lobsters, the megalopa of L. seticaudata retains its feeding capacity. This is the first time such a feature has been reported in decapods, and the term facultative secondary lecithotrophy is proposed. The build up of energy reserves continues during the last zoeal stage of L. seticaudata, with starved zoea IX failing to molt to megalopa. Energy reserves that enable starved megalopa to molt to juvenile seem to be partially depleted, with starved juveniles produced either from, starved or fed megalopae being unable to molt to the next juvenile stage. The longer resistance of starved juveniles produced from fed megalopae (nine days), compared to that of starved juveniles produced from starved megalopae (five days), indicates that some energy reserves may pass to juvenile, not being totally depleted at metamorphosis.info:eu-repo/semantics/publishedVersio

Two-chamber lattice model for thermodiffusion in polymer solutions

When a temperature gradient is applied to a polymer solution, the polymer typically migrates to the colder regions of the fluid as a result of thermal diffusion (Soret effect). However, in recent thermodiffusion experiments on poly(ethylene-oxide) (PEO) in a mixed ethanol/water solvent it is observed that for some solvent compositions the polymer migrates to the cold side, while for other compositions it migrates to the warm side. In order to understand this behavior, we have developed a two-chamber lattice model approach to investigate thermodiffusion in dilute polymer solutions. For a short polymer chain in an incompressible, one-component solvent we obtain exact results for the partitioning of the polymer between a warm and a cold chamber. In order to describe mixtures of PEO, ethanol, and water, we have extended this simple model to account for compressibility and hydrogen bonding between PEO and water molecules. For this complex system, we obtain approximate results for the composition in the warmer and cooler chambers that allow us to calculate Soret coefficients for given temperature, pressure, and solvent composition. The sign of the Soret coefficient is found to change from negative (polymer enriched in warmer region) to positive (polymer enriched in cooler region) as the water content of the solution is increased, in agreement with experimental data. We also investigate the temperature dependence of the Soret effect and find that a change in temperature can induce a change in the sign of the Soret coefficient. We note a close relationship between the solvent quality and the partitioning of the polymer between the two chambers, which may explain why negative Soret coefficients for polymers are so rarely observed.Comment: 12 pages, 8 figure

Epigenetic regulation of PLS3 by the macrosatellite DXZ4 and the transcriptional regulator CHD4

Part I: Spinal muscular atrophy (SMA) is a devastating motor neuron disorder caused by homozygous loss of the Survival of Motor Neuron 1 (SMN1) gene and insufficient functional SMN protein produced by the SMN2 copy gene. Overexpression of Plastin 3 (PLS3) fully protects from mild types of SMA. The mechanisms that regulate PLS3 expression are not fully understood. We performed a multi-omics analysis of SMA-discordant families using lymphoblastoids, fibroblasts and iPSC-derived spinal motor neurons and identified mechanisms, which contribute to the regulation of PLS3. We found a 1-fold expression difference in spinal motor neurons of PLS3 between female asymptomatic and their SMA-affected brothers, which can be explained by the gene’s escape from X-chromosomal inactivation. The X-linked PLS3 is located in close proximity to DXZ4, a microsatellite, which is essential for X-chromosomal inactivation. By Molecular Combing, we measured the copy number of DXZ4 and found a significant correlation with the expression of PLS3 in females. Additionally, we identified Chromodomain Helicase DNA Binding Protein 4 (CHD4) as an epigenetic transcriptional regulator of PLS3. By application of siRNA-mediated knock-down, overexpression, chromatin immunoprecipitation and promoter luciferase assays, we validated the regulation of PLS3 by CHD4. Thus, we provide evidence for a multilevel epigenetic regulation of PLS3. Part II: Spinraza, a SMN antisense oligonucleotide (ASO) that restores full-length SMN2 transcripts, has been FDA- and EMA-approved for SMA therapy. Hence, the availability of biomarkers allowing reliable disease monitoring would be of great importance. The BforSMA study identified about 200 putative SMA biomarkers. We took advantage of a previously developed intermediate SMA mouse model, treated with presymptomatic low-dose SMN-ASO injections and measured the plasma concentrations of SMN and six other SMA biomarkers from the BforSMA study, namely Cartilage Oligomeric Matrix Protein (COMP), Dipeptidyl Peptidase 4 (DPP4), Tetranectin (C-type Lectin Family 3 Member B, CLEC3B), Osteopontin (Secreted Phosphoprotein 1, SPP1), Vitronectin (VTN) and Fetuin A (Alpha 2-HS Glycoprotein, AHSG). Only COMP and DPP4 showed high and SPP1 moderate correlation with the SMA phenotype. PLS3 overexpression from a human transgene neither influenced the SMN level nor the six biomarkers, supporting the hypothesis that PLS3 acts as an independent protective modifier of SMA

An Analysis of Rating Systems Used by Watchdog Organizations for Nonprofit Charities in the Health and Human Services Sector

In the United State, lack of trust and accountability are developing trends among donors in regard to charitable nonprofits in the health and human services sector. Watchdog organizations are working diligently to provide useful data to donors to combat this growing issue. While some watchdog organizations focus on quality of statistics, others focus largely on quantity of metrics. Additionally, some rating systems are solely based on financial data, while others consider nonfinancial data as well. Although different methodologies concentrate on varying metrics, this thesis seeks to find a comprehensive, yet easy-to-use rating system that allows users to understand both financial and nonfinancial data. In comparing this proposed system with current methodologies for specific charities, overall ratings did not differ as greatly as hypothesized. Because managerial decisions and financial health correlate so closely, the focus on financial versus nonfinancial data in rating systems created little difference in overall grades. Additionally, the focus on quality over quantity, and vice versa, seemed to create almost no difference in ratings. Although this difference in ratings was not large for different ratings systems, using portions of certain rating systems can benefit individuals if they have more concerns in one area of a charity’s business than another. Through this research and analysis, it can be concluded that individuals can trust current watchdog organizations in regard to overall ratings. However, discretion is still advised, and this thesis recommends verifying the accuracy of scores on published websites before donating to charities in the health and human services sector

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.Comment: Accepted for publication at the Seventh International Conference on Learning Representations (ICLR 2019

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families

We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where classical HMC is not an option due to intractable gradients, KMC adaptively learns the target's gradient structure by fitting an exponential family model in a Reproducing Kernel Hilbert Space. Computational costs are reduced by two novel efficient approximations to this gradient. While being asymptotically exact, KMC mimics HMC in terms of sampling efficiency, and offers substantial mixing improvements over state-of-the-art gradient free samplers. We support our claims with experimental studies on both toy and real-world applications, including Approximate Bayesian Computation and exact-approximate MCMC.Comment: 20 pages, 7 figure