Collective Dynamics of Random Polyampholytes

We consider the Langevin dynamics of a semi-dilute system of chains which are random polyampholytes of average monomer charge $q$ and with a fluctuations in this charge of the size $Q^{-1}$ and with freely floating counter-ions in the surrounding. We cast the dynamics into the functional integral formalism and average over the quenched charge distribution in order to compute the dynamic structure factor and the effective collective potential matrix. The results are given for small charge fluctuations. In the limit of finite $q$ we then find that the scattering approaches the limit of polyelectrolyte solutions.Comment: 13 pages including 6 figures, submitted J. Chem. Phy

PEAKFORCE QUANTITATIVE NANOMECHANICAL MAPPING FOR SURFACE ENERGY CHARACTERIZATION ON THE NANOSCALE: A MINI-REVIEW

Surface energy characterization is important to design the fabrication process of reliable electronic devices. Surface energy is influenced by various factors such as surface functionality and morphology. Owing to the high surface-to-volume ratio, surface energy at the nanoscale can be different from that of the bulk. However, the conventional methods for characterization of surface energy such as a sessile drop or Washburn methods cannot be used for nanoscale samples, owing to the limited volume for characterization. Recently, surface energy characterization on the nanoscale using atomic force microscopy (AFM) with Peak Force-Quantitative Nanomechanical Mapping (PF-QNM) imaging mode has been proposed. The nanoscale AFM tips measure the adhesion forces at the nanoscale, which are converted into surface energy through pre-calibrated curves. Successful surface energy characterization of nanoscale metal samples using AFM with the PF-QNM method has been reported previously. This mini-review discusses the recent progress on surface energy characterization at the nanoscale using AFM with the PF-QNM method. The fundamentals of the PF-QNM mode are introduced, and the results of surface energy characterization are summarized. Consequently, the future research direction for surface energy characterization at the nanoscale is discussed

Stiff polymer in monomer ensemble

We make use of the previously developed formalism for a monomer ensemble and include angular dependence of the segments of the polymer chains thus described. In particular we show how to deal with stiffness when the polymer chain is confined to certain regions. We investigate the stiffness from the perspectives of a differential equation, integral equations, or recursive relations for both continuum and lattice models. Exact analytical solutions are presented for two cases, whereas numerical results are shown for a third case.Comment: 10 pages, including 6 figure

Bioactive secondary metabolites from the endophytic fungus Chaetomium sp. isolated from Salvia officinalis growing in Morocco

This study reports the chemical investigation and cytotoxic activity of the secondary metabolites produced by the endophytic fungus Chaetomium sp. isolated from Salvia officinalis growing in Morocco. This plant was collected from the Beni-Mellal Mountain in Morocco and belongs to the Lamiaceae family and is named in Morocco “Salmia”. The endophytic fungus Chaetomium sp. was isolated from the tissues of the stem of this plant. The fungal strain was identified by PCR. The crude organic extract of the fungal strain was proven to be active when tested for cytotoxicity against L5178Y mouse lymphoma cells. Chemical investigation of the secondary metabolites showed that cochliodinol is the main component beside isocochliodinol. The structures of the isolated compounds were determined on the basis of NMR analysis (1H, 13C, COSY and HMBC) as well as by mass spectrometry using ESI (Electron Spray Ionisation) as source

Vision-Based Autonomous Driving: A Model Learning Approach

We present an integrated approach for perception and control for an autonomous vehicle and demonstrate this approach in a high-fidelity urban driving simulator. Our approach first builds a model for the environment, then trains a policy exploiting the learned model to identify the action to take at each time-step. To build a model for the environment, we leverage several deep learning algorithms. To that end, first we train a variational autoencoder to encode the input image into an abstract latent representation. We then utilize a recurrent neural network to predict the latent representation of the next frame and handle temporal information. Finally, we utilize an evolutionary-based reinforcement learning algorithm to train a controller based on these latent representations to identify the action to take. We evaluate our approach in CARLA, a high-fidelity urban driving simulator, and conduct an extensive generalization study. Our results demonstrate that our approach outperforms several previously reported approaches in terms of the percentage of successfully completed episodes for a lane keeping task.Comment:

Targeting DNA G-quadruplexes with helical small molecules.

We previously identified quinoline-based oligoamide helical foldamers and a trimeric macrocycle as selective ligands of DNA quadruplexes. Their helical structures might permit targeting of the backbone loops and grooves of G-quadruplexes instead of the G-tetrads. Given the vast array of morphologies G-quadruplex structures can adopt, this might be a way to achieve sequence selective binding. Here, we describe the design and synthesis of molecules based on macrocyclic and helically folded oligoamides. We tested their ability to interact with the human telomeric G-quadruplex and an array of promoter G-quadruplexes by using FRET melting assay and single-molecule FRET. Our results show that they constitute very potent ligands--comparable to the best so far reported. Their modes of interaction differ from those of traditional tetrad binders, thus opening avenues for the development of molecules specific for certain G-quadruplex conformations.We thank the “Cancer Research UK” for doctoral funding (SM) and “Association pour la recherche sur le cancer” for a postdoctoral fellowship (KLR). The Balasubramanian laboratory is core-funded by a programme grant from Cancer Research UK. TH acknowledges the NIH grant GM065367.This is the final version. It was first published by Wiley at http://onlinelibrary.wiley.com/doi/10.1002/cbic.201402439/abstract

Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning

BACKGROUND: The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results’ interpretability. METHODS: We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios. RESULTS: In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC. CONCLUSION: Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection

In vitro culture with gemcitabine augments death receptor and NKG2D ligand expression on tumour cells

Much effort has been made to try to understand the relationship between chemotherapeutic treatment of cancer and the immune system. Whereas much of that focus has been on the direct effect of chemotherapy drugs on immune cells and the release of antigens and danger signals by malignant cells killed by chemotherapy, the effect of chemotherapy on cells surviving treatment has often been overlooked. In the present study, tumour cell lines: A549 (lung), HCT116 (colon) and MCF-7 (breast), were treated with various concentrations of the chemotherapeutic drugs cyclophosphamide, gemcitabine (GEM) and oxaliplatin (OXP) for 24 hours in vitro. In line with other reports, GEM and OXP upregulated expression of the death receptor CD95 (fas) on live cells even at sub-cytotoxic concentrations. Further investigation revealed that the increase in CD95 in response to GEM sensitised the cells to fas ligand treatment, was associated with increased phosphorylation of stress activated protein kinase/c-Jun N-terminal kinase and that other death receptors and activatory immune receptors were co-ordinately upregulated with CD95 in certain cell lines. The upregulation of death receptors and NKG2D ligands together on cells after chemotherapy suggest that although the cells have survived preliminary treatment with chemotherapy they may now be more susceptible to immune cell-mediated challenge. This re-enforces the idea that chemotherapy-immunotherapy combinations may be useful clinically and has implications for the make-up and scheduling of such treatments