Fano resonance in two-dimensional optical waveguide arrays with a bi-modal defect

We study the two-dimensional extension of the Fano-Anderson model on the basis of a two-dimensional optical waveguide array with a bi-modal defect. We demonstrate numerically the persistence of the Fano resonance in wavepacket scattering process by the defect. An analytical approximation is derived for the total scattered light power

Nonlinear optics and light localization in periodic photonic lattices

We review the recent developments in the field of photonic lattices emphasizing their unique properties for controlling linear and nonlinear propagation of light. We draw some important links between optical lattices and photonic crystals pointing towards practical applications in optical communications and computing, beam shaping, and bio-sensing.Comment: to appear in Journal of Nonlinear Optical Physics & Materials (JNOPM

Functional characterization of human T cell hyporesponsiveness induced by CTLA4-Ig.

During activation, T cells integrate multiple signals from APCs and cytokine milieu. The blockade of these signals can have clinical benefits as exemplified by CTLA4-Ig, which blocks interaction of B7 co-stimulatory molecules on APCs with CD28 on T cells. Variants of CTLA4-Ig, abatacept and belatacept are FDA approved as immunosuppressive agents in arthritis and transplantation, yet murine studies suggested that CTLA4-Ig could be beneficial in a number of other diseases. However, detailed analysis of human CD4 cell hyporesponsivness induced by CTLA4-Ig has not been performed. Herein, we established a model to study the effect of CTLA4-Ig on the activation of human naïve T cells in a human mixed lymphocytes system. Comparison of human CD4 cells activated in the presence or absence of CTLA4-Ig showed that co-stimulation blockade during TCR activation does not affect NFAT signaling but results in decreased activation of NF-κB and AP-1 transcription factors followed by a profound decrease in proliferation and cytokine production. The resulting T cells become hyporesponsive to secondary activation and, although capable of receiving TCR signals, fail to proliferate or produce cytokines, demonstrating properties of anergic cells. However, unlike some models of T cell anergy, these cells did not possess increased levels of the TCR signaling inhibitor CBLB. Rather, the CTLA4-Ig-induced hyporesponsiveness was associated with an elevated level of p27kip1 cyclin-dependent kinase inhibitor

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials

XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications

Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The existing methods of experimental characterization of nanocatalysts, which make it possible to assess the possibility of using these materials in specific chemical reactions or applications, generate significant amounts of heterogeneous data. The acceleration of new functional materials, including nanocatalysts, directly depends on the speed and quality of extracting hidden dependencies and knowledge from the obtained experimental data. Usually, such experiments involve different characterization techniques and different types of X-ray absorption spectroscopy (XAS) too. Using the machine learning (ML) methods based on XAS data, we can study and predict the atomic-scale structure and another bunch of parameters for the nanocatalyst efficiently. However, before using any ML model, it is necessary to make sure that the XAS raw experimental data is properly pre-processed, cleared, and prepared for ML application. Usually, the XAS preprocessing stage is vaguely presented in scientific studies, and the main efforts of researchers are devoted to the ML description and implementation stage. However, the quality of the input data influences the quality of ML analysis and the prediction results used in the future. This paper fills the gap between the stage of obtaining XAS data from synchrotron facilities and the stage of using and customizing various ML analysis and prediction models. We aimed this study to develop automated tools for the preprocessing and presentation of data from physical experiments and the creation of deposited datasets on the basis of the example of studying palladium-based nanocatalysts using synchrotron radiation facilities. During the study, methods of preliminary processing of XAS data were considered, which can be conditionally divided into X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). This paper proposes a software toolkit that implements data preprocessing scenarios in the form of a single pipeline. The main preprocessing methods used in this study proposed are principal component analysis (PCA); z-score normalization; the interquartile method for eliminating outliers in the data; as well as the k-means machine learning method, which makes it possible to clarify the phase of the studied material sample by clustering feature vectors of experiments. Among the results of this study, one should also highlight the obtained deposited datasets of physical experiments on palladium-based nanocatalysts using synchrotron radiation. This will allow for further high-quality data mining to extract new knowledge about materials using artificial intelligence methods and machine learning models, and will ensure the smooth dissemination of these datasets to researchers and their reuse

Poised chromatin and bivalent domains facilitate the mitosis-to-meiosis transition in the male germline

BackgroundThe male germline transcriptome changes dramatically during the mitosis-to-meiosis transition to activate late spermatogenesis genes and to transiently suppress genes commonly expressed in somatic lineages and spermatogenesis progenitor cells, termed somatic/progenitor genes.ResultsThese changes reflect epigenetic regulation. Induction of late spermatogenesis genes during spermatogenesis is facilitated by poised chromatin established in the stem cell phases of spermatogonia, whereas silencing of somatic/progenitor genes during meiosis and postmeiosis is associated with formation of bivalent domains which also allows the recovery of the somatic/progenitor program after fertilization. Importantly, during spermatogenesis mechanisms of epigenetic regulation on sex chromosomes are different from autosomes: X-linked somatic/progenitor genes are suppressed by meiotic sex chromosome inactivation without deposition of H3K27me3.ConclusionsOur results suggest that bivalent H3K27me3 and H3K4me2/3 domains are not limited to developmental promoters (which maintain bivalent domains that are silent throughout the reproductive cycle), but also underlie reversible silencing of somatic/progenitor genes during the mitosis-to-meiosis transition in late spermatogenesis

CD4⁺ T cells activated in the presence of CTLA4-Ig do not have properties of Treg cells.

<p><b>A-C</b>. Naïve cells were activated to generate effector or anergic cells as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122198#pone.0122198.g001" target="_blank">Fig 1</a>. For generation of regulatory T cells (Treg cells), αCD3, αCD28, TGF-β, and IL-2 were added for the first activation. <b>A.</b> The expression of FOXP3 in CD4⁺ T cells was measured on day 4 of activation or <b>B.</b> after an additional 3 days of rest in the medium. Numbers are percent of CD4⁺FOXP3⁺ cells ± SEM (n = 11) <b>C.</b> Each group of CD4⁺ T cells was purified 4 days after activation, rested for 3 days in medium, and co-activated with freshly purified CFSE-labeled allogeneic naïve CD4⁺ cells in the presence of αCD3 + αCD28–coated beads (0.1 μl of beads per 100 μl of medium) for 4 days. The proliferation rate of CFSE-labeled naïve cells was analyzed by FACS. Shown is the average ± SEM percent inhibition of naïve cell proliferation (n = 6). EML, effector/memory-like T cells; iTreg, induced regulatory T cells.</p

Molecular characteristics of CD4⁺ T cells activated in the presence of co-stimulatory inhibition.

<p>Naïve CD4⁺ T cells were activated as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122198#pone.0122198.g001" target="_blank">Fig 1B</a>. <b>A.</b> Phosphorylation of ZAP70 in CD4⁺ T cells stimulated for the indicated time periods was analyzed by FACS. Shown is one representative experiment with the mean of fluorescence intensity for each population ± SEM from 5 independent experiments. Gray plot is naïve cells (N, unstimulated); dotted line is effector cells (E, αCD3 + αCD28); solid line is anergic cells (A, αCD3 + CTLA4-Ig). <b>B.</b> Activation of AKT and MAPKs in cytoplasm and <b>C.</b> translocation of transcription factors to the nucleus during activation were examined by western blot (cytoplasmic AKT and p38 and nuclear PARP1 were used as loading controls). Each experiment was repeated at least 3 times. <b>D.</b> mRNA expression of indicated genes was measured by real-time PCR (n = 5). Data are presented as mean ± SEM.</p