The inevitable QSAR renaissance

QSAR approaches, including recent advances in 3D-QSAR, are advantageous during the lead optimization phase of drug discovery and complementary with bioinformatics and growing data accessibility. Hints for future QSAR practitioners are also offered

Interpreting linear support vector machine models with heat map molecule coloring

<p>Abstract</p> <p>Background</p> <p>Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.</p> <p>Results</p> <p>We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.</p> <p>Conclusions</p> <p>In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.</p

Semiochemical-based alternatives to synthetic toxicant insecticides for pollen beetle management

There is an urgent need to develop sustainable pest management systems to protect arable crops in order to replace the current over-reliance on synthetic insecticides. Semiochemicals are insect- or plant-derived chemicals that are used by organisms as information signals. Integrated pest management tools are currently in development that utilise semiochemicals to manipulate the behaviour of pest insects and their natural enemies to provide effective control of pests within the crop. These innovative tools usually require fewer inputs and can involve multiple elements therefore reducing the likelihood of resistance developing compared with use of synthetic toxicants. We review here the life cycle of the pollen beetle Brassicogethes aeneus (previously known as Meligethes aeneus) which is a pest insect of oilseed rape (Brassica napus) and describe the current knowledge of any behaviour mediated by semiochemicals in this species. We discuss the behavioural processes where semiochemical-based control approaches may be appropriate and consider how these approaches could be integrated into an integrated pest management strategy for this important arable crop

ICTs and the Challenge of Health System Transition in Low and Middle-Income Countries

The aim of this paper is to contribute to debates about how governments and other stakeholders can influence the application of ICTs to increase access to safe, effective and affordable treatment of common illnesses, especially by the poor. First, it argues that the health sector is best conceptualized as a ‘knowledge economy’. This supports a broadened view of health service provision that includes formal and informal arrangements for the provision of medical advice and drugs. This is particularly important in countries with a pluralistic health system, with relatively underdeveloped institutional arrangements. It then argues that reframing the health sector as a knowledge economy allows us to circumvent the blind spots associated with donor-driven ICT-interventions and consider more broadly the forces that are driving e-health innovations. It draws on small case studies in Bangladesh and China to illustrate new types of organization and new kinds of relationship between organizations that are emerging. It argues that several factors have impeded the rapid diffusion of ICT innovations at scale including: the limited capacity of innovations to meet health service needs, the time it takes to build new kinds of partnership between public and private actors and participants in the health and communications sectors and the lack of a supportive regulatory environment. It emphasises the need to understand the political economy of the digital health knowledge economy and the new regulatory challenges likely to emerge. It concludes that governments will need to play a more active role to facilitate the diffusion of beneficial ICT innovations at scale and ensure that the overall pattern of health system development meets the needs of the population, including the poor

Landmarking the brain for geometric morphometric analysis: An error study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes. © 2014 Chollet et al

DnaC Inactivation in Escherichia coli K-12 Induces the SOS Response and Expression of Nucleotide Biosynthesis Genes

Background: Initiation of chromosome replication in E. coli requires the DnaA and DnaC proteins and conditionally-lethal dnaA and dnaC mutants are often used to synchronize cell populations. Methodology/Principal Findings: DNA microarrays were used to measure mRNA steady-state levels in initiation-deficient dnaA46 and dnaC2 bacteria at permissive and non-permissive temperatures and their expression profiles were compared to MG1655 wildtype cells. For both mutants there was altered expression of genes involved in nucleotide biosynthesis at the non-permissive temperature. Transcription of the dnaA and dnaC genes was increased at the non-permissive temperature in the respective mutant strains indicating auto-regulation of both genes. Induction of the SOS regulon was observed in dnaC2 cells at 38uC and 42uC. Flow cytometric analysis revealed that dnaC2 mutant cells at non-permissive temperature had completed the early stages of chromosome replication initiation. Conclusion/Significance: We suggest that in dnaC2 cells the SOS response is triggered by persistent open-complex formation at oriC and/or by arrested forks that require DnaC for replication restart

Effects of Insemination Quantity on Honey Bee Queen Physiology

Mating has profound effects on the physiology and behavior of female insects, and in honey bee (Apis mellifera) queens, these changes are permanent. Queens mate with multiple males during a brief period in their early adult lives, and shortly thereafter they initiate egg-laying. Furthermore, the pheromone profiles of mated queens differ from those of virgins, and these pheromones regulate many different aspects of worker behavior and colony organization. While it is clear that mating causes dramatic changes in queens, it is unclear if mating number has more subtle effects on queen physiology or queen-worker interactions; indeed, the effect of multiple matings on female insect physiology has not been broadly addressed. Because it is not possible to control the natural mating behavior of queens, we used instrumental insemination and compared queens inseminated with semen from either a single drone (single-drone inseminated, or SDI) or 10 drones (multi-drone inseminated, or MDI). We used observation hives to monitor attraction of workers to SDI or MDI queens in colonies, and cage studies to monitor the attraction of workers to virgin, SDI, and MDI queen mandibular gland extracts (the main source of queen pheromone). The chemical profiles of the mandibular glands of virgin, SDI, and MDI queens were characterized using GC-MS. Finally, we measured brain expression levels in SDI and MDI queens of a gene associated with phototaxis in worker honey bees (Amfor). Here, we demonstrate for the first time that insemination quantity significantly affects mandibular gland chemical profiles, queen-worker interactions, and brain gene expression. Further research will be necessary to elucidate the mechanistic bases for these effects: insemination volume, sperm and seminal protein quantity, and genetic diversity of the sperm may all be important factors contributing to this profound change in honey bee queen physiology, queen behavior, and social interactions in the colony

Evaluation of Pax6 Mutant Rat as a Model for Autism

Autism is a highly variable brain developmental disorder and has a strong genetic basis. Pax6 is a pivotal player in brain development and maintenance. It is expressed in embryonic and adult neural stem cells, in astrocytes in the entire central nervous system, and in neurons in the olfactory bulb, amygdala, thalamus, and cerebellum, functioning in highly context-dependent manners. We have recently reported that Pax6 heterozygous mutant (rSey2/+) rats with a spontaneous mutation in the Pax6 gene, show impaired prepulse inhibition (PPI). In the present study, we further examined behaviors of rSey2/+ rats and revealed that they exhibited abnormality in social interaction (more aggression and withdrawal) in addition to impairment in rearing activity and in fear-conditioned memory. Ultrasonic vocalization (USV) in rSey2+ rat pups was normal in male but abnormal in female. Moreover, treatment with clozapine successfully recovered the defects in sensorimotor gating function, but not in fear-conditioned memory. Taken together with our prior human genetic data and results in other literatures, rSey2/+ rats likely have some phenotypic components of autism