Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we propose a biologically plausible mechanistic model of the circuit. Our model predicts the ORN → LN synaptic weights found in the connectome and demonstrate that they reflect correlations in ORN activity patterns. Additionally, our model explains the relation between ORN → LN and LN – LN synaptic weight and the arising of different LN types. This global synaptic organization can autonomously arise through Hebbian plasticity, and thus allows the circuit to adapt to different environments in an unsupervised manner. Functionally, we propose LNs extract redundant input correlations and dampen them in ORNs, thus partially whitening and normalizing the stimulus representations in ORNs. Our work proposes a comprehensive framework to combine structure, activity, function, and learning, and uncovers a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient.SignificanceThe brain represents information with patterns of neural activity. At the periphery, due to the properties of the external world and of encoding neurons, these patterns contain correlations, which are detrimental for stimulus discrimination. We study the peripheral olfactory neural circuit of the Drosophila larva, that preprocesses neural representations before relaying them to higher brain areas. A comprehensive understanding of this preprocessing is, however, lacking. Here, we propose a mechanistic and normative framework describing the function of the circuit and predict the circuit’s synaptic organization based on the circuit’s input neural activity. We show how the circuit can autonomously adapt to different environments, extracts stimulus features, and decorrelate and normalize input representations, which facilitates odor discrimination downstream.

Structure and pigment make the eyed elater’s eyespots black

Surface structures that trap light leading to near complete structural absorption creates an appearance of “super black.” Well known in the natural world from bird feathers and butterfly scales, super black has evolved independently from various anatomical structures. Due to an exceptional ability to reduce specular reflection, these biological materials have garnered interest from optical industries. Here we describe the false eyes of the eyed elater click beetle, which, while not classified as super black, still attains near complete absorption of light partly due to an array of vertically-aligned microtubules. These cone-shaped microtubules are modified hairs (setae) that are localized to eyespots on the dorsum of the beetle, and absorb 96.1% of incident light (at a 24.8° collection angle) in the spectrum between 300–700 nm. Filled with melanin, the setae combine structure and pigment to generate multiple reflections and refractions causing light to travel a greater distance. This light-capturing architecture leaves little light available to receivers and the false eyes appear as deep black making them appear more conspicuous to predators.

Structure- and sequence-based design of synthetic single-domain antibody libraries

Single-domain antibody fragments known as VHH have emerged in the pharmaceutical industry as useful biotherapeutics. These molecules, which are naturally produced by camelids, share the characteristics of high affinity and specificity with traditional human immunoglobulins, while consisting of only a single heavy chain. Currently, the most common method for generating VHH is via animal immunization, which can be costly and time-consuming. Here we describe the development of a synthetic VHH library for in vitro selection of single domain binders. We combine structure-based design and next-generation sequencing analysis to build a library with characteristics that closely mimic the natural repertoire. To validate the performance of our synthetic library, we isolated VHH against three model antigens (soluble mouse PD-1 ectodomain, amyloid-β peptide, and MrgX1 GPCR) of different sizes and characteristics. We were able to isolate diverse binders targeting different epitopes with high affinity (as high as 5 nM) against all three targets. We then show that anti-mPD-1 binders have functional activity in a receptor blocking assay.

Exploring Secondary Metabolites Database of Apocynaceae, Menispermaceae, and Annonaceae to Select Potential Anti-HCV Compounds

Background: Hepatitis C is a disease that constitutes a serious global health problem, is often asymptomatic and difficult to diagnose and about 60-80% of infected patients develop chronic diseases over time. As there is no vaccine against hepatitis C virus (HCV), developing new cheap treatments is a big challenge. Objective: The search for new drugs from natural products has been outstanding in recent years. The aim of this study was to combine structure-based and ligand-based virtual screening (VS) techniques to select potentially active molecules against four HCV target proteins from in-house secondary metabolite dataset (SistematX). Materials and Methods: From the ChEMBL database, we selected four sets of 1199, 355, 290 and 237chemical structures with inhibitory activity against different targets of HCV to create random forest models with an accuracy value higher than 82% for cross-validation and test sets. Afterward, a ligandbased virtual screen of the entire 1848 secondary metabolites database stored in SistematX was performed. In addition, a structure-based virtual screening was also performed for the same set of secondary metabolites using molecular docking. Results: Finally, using consensus analyses approach combining ligand-based and structure-based VS, three alkaloids were selected as potential anti-HCV compounds. Conclusion: The selected structures are a starting point for further studies in order to develop new anti- HCV compounds based on natural products.

The Additivity Project - Use Cases and User Interface

Herbarium specimens are central to botanical science and of rising importance thanks to increasing accessibility and broadened usability. Alongside the many new uses of specimen data, sit a range of traditional uses supporting the collection of morphological data and their application to taxonomy and systematics. (Henning et al. 2018). Technical workflows are needed to support the sustainable collection of this traditional information and maintain the high quality of the morphological data. Data exchange and re-usability requires the use of accepted controlled vocabularies (community approved) that are accessible (web-based ontologies and term vocabularies) and reliable (long-term availability/unique identifiers). The same applies to datasets that must be stored accessibly and sustainably by maintaining all data relationships that would facilitate convenient re-use. This project aims to construct a comprehensive workflow to optimise the delimitation and characterisation (“descriptions”) of taxa (see complementary talk by Plitzner et al.). It is implemented on the open-source software framework of the EDIT Platform for Cybertaxonomy (http://www.cybertaxonomy.org, Ciardelli et al. 2009) extending the workflow for sample data processing developed in a preceding project (Kilian et al. 2015). The principal goals of this new software component are: specimen-level recording and storage of character data in structured character matrices generating taxon characterisations by aggregating the individual specimen-based datasets using and developing community-coordinated, ontology-based exemplar vocabularies persistently linking character datasets with source specimens for high visibility and re-usability specimen-level recording and storage of character data in structured character matrices generating taxon characterisations by aggregating the individual specimen-based datasets using and developing community-coordinated, ontology-based exemplar vocabularies persistently linking character datasets with source specimens for high visibility and re-usability The angiosperm order, Caryophyllales, provides an exemplar use case through cooperation with the Global Caryophyllales Initiative (Borsch et al. 2015). A basic set of morphological terms and vocabularies has been obtained from various online sources (ontologies, glossaries) and can be used, searched and expanded in the EDIT platform. The terms are categorised into: structures, properties and states. Different editors have been developed to combine structure and property terms to characters and assign a customised state vocabulary (categorical) or suitable values and units (numerical) to them. The workflow is built around a data set defining the taxonomic environment of individual use cases. A data set is specified by the characters and a taxonomic group, which can be filtered by area or rank. The dataset can be opened in a tabular representation (character matrix) to enter preselected state terms or values for the individual specimen. The matrix provides several features for basic comparison and analysis and allows the entry of alternative datasets (e.g. literature). Finally, the aggregation of data subsets to potential taxonomic units by adding up the values and summarising character states, allows the convenient test of taxonomic hypotheses. The term additivity is used here to describe this set of workflows and processes adding value to herbarium specimens and accumulating the specimen data for a taxon description.

Structural features underlying the activity of benzimidazole derivatives that target phosphopeptide recognition by the tandem BRCT domain of the BRCA1 protein

The tandem BRCT (tBRCT) domains of BRCA1 engage pSer-containing motifs in target proteins to propagate intracellular signals initiated by DNA damage, thereby controlling cell cycle arrest and DNA repair. Recently, we identified Bractoppin, a benzimidazole that represents a first selective small molecule inhibitor of phosphopeptide recognition by the BRCA1 tBRCT domains, which selectively interrupts BRCA1-mediated cellular responses evoked by DNA damage. Here, we combine structure-guided chemical elaboration, protein mutagenesis and cellular assays to define the structural features that underlie the biochemical and cellular activities of Bractoppin. Bractoppin fails to bind mutant forms of BRCA1 tBRCT bearing single residue substitutions that alter K1702, a key residue mediating phosphopeptide recognition (K1702A), or alter hydrophobic residues (F1662R or L1701K) that adjoin the pSer-recognition site. However, mutation of BRCA1 tBRCT residue M1775R, which engages the Phe residue in the consensus phosphopeptide motif pSer-X-X-Phe, does not affect Bractoppin binding. Collectively, these findings confirm a binding mode for Bractoppin that blocks the phosphopeptide-binding site via structural features distinct from the substrate phosphopeptide. We explored these structural features through structure-guided chemical elaboration of Bractoppin, synthesizing analogs bearing modifications on the left and right hand side (LHS/RHS) of Bractoppin’s benzimidazole ring. Characterization of these analogs in biochemical assay reveal structural features underlying potency. Analogs where the LHS phenyl is replaced by cyanomethyl (2091) and 4-methoxyphenoxypropyl (2113) conceptualized from structure-guided strategies like GIST and dimer interface analysis expose the role of phenyl and isopropyl as critical hydrophobic anchors. Two Bractoppin analogs, 2088 and 2103 were effective in abrogating BRCA1 foci formation and inhibiting G2 arrest induced by irradiation of cells. Collectively, our findings reveal structural features underlying the biochemical and cellular activity of a novel benzimidazole inhibitor of phosphopeptide recognition by the BRCA1 tBRCT domain, providing fresh insights to guide the development of inhibitors that target the protein-protein interactions of this previously undrugged family of protein domains.

Development of zero energy flexible unit with no Infrastructure for disaster and disaster response

In this paper a new kind of unit-prefabricated building is shown. The unit-prefabricated buildings are made up living unit, energy unit, water unit. The each unit was adapted new combine structure function as a high flexible design type. Moreover the design trend implemented the energy insulation, Solar PV panels, Energy storage system which are maintained for zero energy buildings. We made a prototype for zero energy flexible residential unit. The first step, we was evaluated physical performance and living environment, insulation, airtightness, thermal environmental, acoustic performance. The second step we was evaluated energy performance building to design heating and cooling system to combined PV, ESS system in the different plan type, and climate. As a results, The insulation performance wall was 0.18 W/(m2•K). The results of air-tightness was 12.13 ACH@50 (1/h). Further research we develop the structure and construction technology for zero energy flexible unit. To designed the high performance energy performance for zero energy building in the natural disaster.

RNAdetect: efficient computational detection of novel non-coding RNAs

Motivation Non-coding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and there is a pressing need for accurate computational detection methods that could be used to efficiently scan genomes to detect novel ncRNAs. However, unlike coding genes, ncRNAs often lack distinctive sequence features that could be used for recognizing them. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful cues for computational prediction, it has been also shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. Currently, the most effective ncRNA detection methods combine structure-based techniques with a comparative genome analysis approach to improve the prediction performance. Results In this paper, we propose RNAdetect, a computational method incorporating novel features for accurate detection of ncRNAs in combination with comparative genome analysis. Given a sequence alignment, RNAdetect can accurately detect the presence of functional ncRNAs by incorporating novel predictive features based on the concept of generalized ensemble defect (GED), which assesses the degree of structure conservation across multiple related sequences and the conformation of the individual folding structures to a common consensus structure. Furthermore, n-gram models (NGMs) are used to extract features that can effectively capture sequence homology to known ncRNA families. Utilization of NGMs can enhance the detection of ncRNAs that have sparse folding structures with many unpaired bases. Extensive performance evaluation based on the Rfam database and bacterial genomes demonstrate that RNAdetect can accurately and reliably detect novel ncRNAs, outperforming the current state-of-the-art methods. Availability and implementation The source code for RNAdetect and the benchmark data used in this paper can be downloaded at https://github.com/bjyoontamu/RNAdetect.

Double-focusing mixing jet for XFEL study of chemical kinetics

Several liquid sample injection methods have been developed to satisfy the requirements for serial femtosecond X-ray nanocrystallography, which enables radiation-damage-free determination of molecular structure at room temperature. Time-resolved nanocrystallography would combine structure analysis with chemical kinetics by determining the structures of the transient states and chemical kinetic mechanisms simultaneously. A windowless liquid mixing jet device has been designed for this purpose. It achieves fast uniform mixing of substrates and enzymes in the jet within 250 µs, with an adjustable delay between mixing and probing by the X-ray free-electron laser beam of up to 1 s for each frame of a `movie'. The principle of the liquid mixing jet device is illustrated using numerical simulation, and experimental results are presented using a fluorescent dye.