The relationship between visual working memory and attention: retention of precise colour information in the absence of effects on perceptual selection

We examined the conditions under which a feature value in visual working memory (VWM) recruits visual attention to matching stimuli. Previous work has suggested that VWM supports two qualitatively different states of representation: an active state that interacts with perceptual selection and a passive (or accessory) state that does not. An alternative hypothesis is that VWM supports a single form of representation, with the precision of feature memory controlling whether or not the representation interacts with perceptual selection. The results of three experiments supported the dual-state hypothesis. We established conditions under which participants retained a relatively precise representation of a parcticular colour. If the colour was immediately task relevant, it reliably recruited attention to matching stimuli. However, if the colour was not immediately task relevant, it failed to interact with perceptual selection. Feature maintenance in VWM is not necessarily equivalent with feature-based attentional selection.     

Development of a pharmacophore model for the catecholamine release-inhibitory peptide catestatin: Virtual screening and functional testing identify novel small molecule therapeutics of hypertension

The endogenous catecholamine release-inhibitory peptide catestatin (CST) regulates events leading to hypertension and cardiovascular disease. Earlier we studied the structure of CST by NMR, molecular modeling, and amino acid scanning mutagenesis. That structure has now been exploited for elucidation of interface pharmacophores that mediate binding of CST to its target, with consequent secretory inhibition. Designed pharmacophore models allowed screening of 3D structural domains. Selected compounds were tested on both cultured catecholaminergic cells and an in vivo model of hypertension; in each case, the candidates showed substantial mimicry of native CST actions, with preserved or enhanced potency and specificity. The approach and compounds have thus enabled rational design of novel drug candidates for treatment of hypertension or autonomic dysfunction.     

Foraging success under uncertainty: search tradeoffs and optimal space use

Understanding the structural complexity and the main drivers of animal search behaviour is pivotal to foraging ecology. Yet, the role of uncertainty as a generative mechanism of movement patterns is poorly understood. Novel insights from search theory suggest that organisms should collect and assess new information from the environment by producing complex exploratory strategies. Based on an extension of the first passage time theory, and using simple equations and simulations, we unveil the elementary heuristics behind search behaviour. In particular, we show that normal diffusion is not enough for determining optimal exploratory behaviour but anomalous diffusion is required. Searching organisms go through two critical sequential phases (approach and detection) and experience fundamental search tradeoffs that may limit their encounter rates. Using experimental data, we show that biological search includes elements not fully considered in contemporary physical search theory. In particular, the need to consider search movement as a non‐stationary process that brings the organism from one informational state to another. For example, the transition from remaining in an area to departing from it may occur through an exploratory state where cognitive search is challenged. Therefore, a more comprehensive view of foraging ecology requires including current perspectives about movement under uncertainty.     

Investigating core genetic-and-epigenetic cell cycle networks for stemness and carcinogenic mechanisms,and cancer drug design using big database mining and genome-wide next-generation sequencing data

Recent studies have demonstrated that cell cycle plays a central role in development and carcinogenesis. Thus, the use of big databases and genome-wide high-throughput data to unravel the genetic and epigenetic mechanisms underlying cell cycle progression in stem cells and cancer cells is a matter of considerable interest.

Real genetic-and-epigenetic cell cycle networks (GECNs) of embryonic stem cells (ESCs) and HeLa cancer cells were constructed by applying system modeling, system identification, and big database mining to genome-wide next-generation sequencing data. Real GECNs were then reduced to core GECNs of HeLa cells and ESCs by applying principal genome-wide network projection. In this study, we investigated potential carcinogenic and stemness mechanisms for systems cancer drug design by identifying common core and specific GECNs between HeLa cells and ESCs. Integrating drug database information with the specific GECNs of HeLa cells could lead to identification of multiple drugs for cervical cancer treatment with minimal side-effects on the genes in the common core. We found that dysregulation of miR-29C, miR-34A, miR-98, and miR-215; and methylation of ANKRD1, ARID5B, CDCA2, PIF1, STAMBPL1, TROAP, ZNF165, and HIST1H2AJ in HeLa cells could result in cell proliferation and anti-apoptosis through NFκB, TGF-β, and PI3K pathways. We also identified 3 drugs, methotrexate, quercetin, and mimosine, which repressed the activated cell cycle genes, ARID5B, STK17B, and CCL2, in HeLa cells with minimal side-effects.     

Protein‐fold recognition using an improved single‐source K diverse shortest paths algorithm

Protein structure prediction, when construed as a fold recognition problem, is one of the most important applications of similarity search in bioinformatics. A new protein‐fold recognition method is reported which combines a single‐source K diverse shortest path (SSKDSP) algorithm with Enrichment of Network Topological Similarity (ENTS) algorithm to search a graphic feature space generated using sequence similarity and structural similarity metrics. A modified, more efficient SSKDSP algorithm is developed to improve the performance of graph searching. The new implementation of the SSKDSP algorithm empirically requires 82% less memory and 61% less time than the current implementation, allowing for the analysis of larger, denser graphs. Furthermore, the statistical significance of fold ranking generated from SSKDSP is assessed using ENTS. The reported ENTS‐SSKDSP algorithm outperforms original ENTS that uses random walk with restart for the graph search as well as other state‐of‐the‐art protein structure prediction algorithms HHSearch and Sparks‐X, as evaluated by a benchmark of 600 query proteins. The reported methods may easily be extended to other similarity search problems in bioinformatics and chemoinformatics. The SSKDSP software is available at http://compsci.hunter.cuny.edu/~leixie/sskdsp.html . Proteins 2016; 84:467–472. © 2016 Wiley Periodicals, Inc.     

Intact Transition Epitope Mapping – Targeted High-Energy Rupture of Extracted Epitopes (ITEM-THREE),

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Highlights

• •Multiplex epitope mapping/antigenic determinant identification in the gas phase.
• •Intact transition and controlled dissociation of immune complexes by MS.
• •Simultaneous identification and amino acid sequence determination of epitopes.
• •Simplified in-solution sample handling because of ion manipulation and filtering by MS.

     

Unlocking biodiversity and conservation studies in high‐diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes

Determining the species compositions of local assemblages is a prerequisite to understanding how anthropogenic disturbances affect biodiversity. However, biodiversity measurements often remain incomplete due to the limited efficiency of sampling methods. This is particularly true in freshwater tropical environments that host rich fish assemblages, for which assessments are uncertain and often rely on destructive methods. Developing an efficient and nondestructive method to assess biodiversity in tropical freshwaters is highly important. In this study, we tested the efficiency of environmental DNA (eDNA) metabarcoding to assess the fish diversity of 39 Guianese sites. We compared the diversity and composition of assemblages obtained using traditional and metabarcoding methods. More than 7,000 individual fish belonging to 203 Guianese fish species were collected by traditional sampling methods, and ~17 million reads were produced by metabarcoding, among which ~8 million reads were assigned to 148 fish taxonomic units, including 132 fish species. The two methods detected a similar number of species at each site, but the species identities partially matched. The assemblage compositions from the different drainage basins were better discriminated using metabarcoding, revealing that while traditional methods provide a more complete but spatially limited inventory of fish assemblages, metabarcoding provides a more partial but spatially extensive inventory. eDNA metabarcoding can therefore be used for rapid and large‐scale biodiversity assessments, while at a local scale, the two approaches are complementary and enable an understanding of realistic fish biodiversity.     

Effect of cow replacement strategy on cow and calf performance in the beef herd

Two contrasting replacement strategies are used by Irish beef farmers to select replacement females – animals sourced from within the suckler beef herd and sourced from the dairy herd. The objective of this study was to investigate the effect of replacement strategy (i.e. beef v. beef×dairy (BDX)) on cow and calf performance using data from the national beef database across a range of beef and dairy breeds. The association between replacement strategy and calving difficulty score, calving interval, weaning weight, weaning price and all carcass traits was investigated using a mixed model. The effect of replacement strategy on cow survival, calving dystocia and calf perinatal mortality was quantified using logistic regression. Beef cows were older (10.92 days; P<0.001) at their first calving, but were 1.15 times (P<0.01) more likely to survive to a subsequent lactation compared with BDX cows. Calving interval was 1.53 days shorter (P<0.001) for BDX compared with beef cows. Greater calving difficulty and calving dystocia was associated with beef cows (P<0.001) relative to BDX. However, BDX were 1.36 times (P<0.001) more likely to have a dead calf at birth relative to beef cows. Calves weaned from BDX were heavier (18.49 kg; P<0.001) at weaning, reached slaughter 12.8 days earlier (P<0.001), had 7.99 kg heavier carcass (P<0.001) and a greater fat score (P<0.001) compared with the progeny of beef cows. Beef cow progeny had a superior conformation score (0.5; P<0.001) and achieved a greater price per kilogram (P<0.001) compared with progeny from BDX. Beef cull cows had a heavier carcass (5.58 kg), superior carcass conformation, greater carcass price per kilogram and greater overall carcass value (P<0.001) than BDX. Results from this study show that replacement strategy is of fundamental importance depending on the type of system implemented by farmers and consideration must be given to the traits of importance within the context of the individual production system.     

Functional analysis tools for post‐translational modification: a post‐translational modification database for analysis of proteins and metabolic pathways

Post‐translational modifications (PTMs) are critical regulators of protein function, and nearly 200 different types of PTM have been identified. Advances in high‐resolution mass spectrometry have led to the identification of an unprecedented number of PTM sites in numerous organisms, potentially facilitating a more complete understanding of how PTMs regulate cellular behavior. While databases have been created to house the resulting data, most of these resources focus on individual types of PTM, do not consider quantitative PTM analyses or do not provide tools for the visualization and analysis of PTM data. Here, we describe the Functional Analysis Tools for Post‐Translational Modifications (FAT‐PTM) database ( https://bioinformatics.cse.unr.edu/fat-ptm/ ), which currently supports eight different types of PTM and over 49 000 PTM sites identified in large‐scale proteomic surveys of the model organism Arabidopsis thaliana. The FAT‐PTM database currently supports tools to visualize protein‐centric PTM networks, quantitative phosphorylation site data from over 10 different quantitative phosphoproteomic studies, PTM information displayed in protein‐centric metabolic pathways and groups of proteins that are co‐modified by multiple PTMs. Overall, the FAT‐PTM database provides users with a robust platform to share and visualize experimentally supported PTM data, develop hypotheses related to target proteins or identify emergent patterns in PTM data for signaling and metabolic pathways.