The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.

Symmetry is an organizing principle in neuronal dendrotaxis

Symmetry is a fundamental property found in both the physical and natural worlds. Bilateral symmetry is also present in the organization of the brain, however the degree to which symmetry is also an organizing principal between and within the key constituent elements of the nervous system, neurons, is not known. We compared and contrasted the structural organization of principal neurons (PN) in the three subnuclei of the lateral amygdala (LA) of the rat and for comparison also from the infralimbic cortex (IL)...

Moral vitalism: Seeing good and evil as real, agentic forces

Moral vitalism refers to a tendency to view good and evil as actual forces that can influence people and events. We introduce a scale designed to assess the belief in moral vitalism. High scorers on the scale endorse items such as “There are underlying forces of good and evil in this world”. After establishing the reliability and criterion validity of the scale (Studies 1, 2a, 2b), we examined the predictive validity of the moral vitalism scale, showing that “moral vitalists” worry about being possessed by evil (Study 3), being contaminated through contact with evil people (Study 4), and forfeiting their own mental purity (Study 5). We discuss the nature of moral vitalism and the implications of the construct for understanding the role of metaphysical lay theories about the nature of good and evil in moral reasoning.

A recurrent network in the lateral amygdala: A mechanism for temporal coincidence detection

The dorsal lateral amygdala (LAd) is a vital nucleus for the formation of associations between aversive unconditioned stimuli (US) and neutral stimuli, such as auditory tones, which can become conditioned (CS) to the US through temporal pairing. Important aspects of CS-US associations are believed to occur within the LAd, however relatively little is known about the temporal behavior of local LAd networks. Information about the CS and US enters the LA via a rapid and direct thalamic input and a longer latency cortical path...

Quantification of the total neuronal structure of the fear conditioning circuit of the lateral amygdala of the rat

During Pavlovian auditory fear conditioning a previously neutral auditory stimulus (CS) gains emotional significance through pairing with a noxious unconditioned stimulus (US). These associations are believed to be formed by way of plasticity at auditory input synapses on principal neurons in the lateral nucleus of the amygdala (LA). In order to begin to understand how fear memories are stored and processed by synaptic changes in the LA, we have quantified both the entire neural number and the sub-cellular structure of LA principal neurons.We first used stereological cell counting methods on Gimsa or GABA immunostained rat brain. We identified 60,322+/-1408 neurons in the LA unilaterally (n=7). Of these 16,917+/-471 were GABA positive. The intercalated nuclei were excluded from the counts and thus GABA cells are believed to represent GABAergic interneurons. The sub-nuclei of the LA were also independently counted. We then quantified the morphometric properties of in vitro electrophysiologically identified principal neurons of the LA, corrected for shrinkage in xyz planes. The total dendritic length was 9.97+/-2.57mm, with 21+/-4 nodes (n=6). Dendritic spine density was 0.19+/-0.03 spines/um (n=6). Intra-LA axon collaterals had a bouton density of 0.1+/-0.02 boutons/um (n=5). These data begin to reveal the finite cellular and sub-cellular processing capacity of the lateral amygdala, and should facilitate efforts to understand mechanisms of plasticity in LA.

The good, the bad, and the efficient: Productivity, efficiency, and technical change in the airline industry, 2004–11

This study models the joint production of desirable and undesirable output production (that is, CO2 emissions) of airlines. The Malmquist–Luenberger productivity index is employed to measure productivity growth when undesirable output production is incorporated into the production model. The results show that pollution abatement activities of airlines lowers productivity growth, which suggests that the traditional approach of measuring productivity growth, which ignores CO2 emissions, overstates ‘true’ productivity growth. The reliability of the results is also tested and verified using confidence intervals based on bootstrapping.

A roadmap for interpreting the literature on vision and driving

Over the past several decades there has been a sharp increase in the number of studies focused on the relationship between vision and driving. The intensified attention to this topic has most likely been stimulated by the lack of an evidence basis for determining vision standards for driving licensure and a poor understanding about how vision impairment impacts driver safety and performance. Clinicians depend on the literature on vision and driving to advise visually impaired patients appropriately about driving fitness. Policy makers also depend on the scientific literature in order to develop guidelines that are evidence-based and are thus fair to persons who are visually impaired. Thus it is important for clinicians and policy makers alike to understand how various study designs and measurement methods should be interpreted so that the conclusions and recommendations they make are not overly broad, too narrowly constrained, or even misguided. We offer a methodological framework to guide interpretations of studies on vision and driving that can also serve as a heuristic for researchers in the area. Here, we discuss research designs and general measurement methods for the study of vision as they relate to driver safety, driver performance, and driver-centered (self-reported) outcomes.

Increasing enjoyable activities to treat depression in nursing home residents with dementia: A pilot study

This pilot study aimed to assess the feasibility and acceptability of a Behavioral Activities intervention (BE-ACTIV) in Australian nursing homes. BE-ACTIV was developed by researchers at the University of Louisville, USA, to improve mood and quality of life (QOL) in nursing home residents with mild to moderate dementia. An 8-week trial was conducted and 10 residents with mild to moderate dementia received the BE-ACTIV intervention while 8 residents received a Walking and Talking intervention. Measures of depression (GDS–12R), and QOL (QOL-AD-NH) were administered prior to and following the interventions. Qualitative feedback indicated residents benefited from BE-ACTIV, evident by improved mood, although no statistically significant treatment effect was found. Moreover, the intervention was found to be feasible and acceptable to Australian nursing home staff and our findings highlight the importance of individualizing activities for people with dementia, of which 1:1 staff attention was a key component.

Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N = 1345 young and elderly subjects

Deficits in lentiform nucleus volume and morphometry are implicated in a number of genetically influenced disorders, including Parkinson's disease, schizophrenia, and ADHD. Here we performed genome-wide searches to discover common genetic variants associated with differences in lentiform nucleus volume in human populations. We assessed structural MRI scans of the brain in two large genotyped samples: the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 706) and the Queensland Twin Imaging Study (QTIM; N = 639). Statistics of association from each cohort were combined meta-analytically using a fixed-effects model to boost power and to reduce the prevalence of false positive findings. We identified a number of associations in and around the flavin-containing monooxygenase (FMO) gene cluster. The most highly associated SNP, rs1795240, was located in the FMO3 gene; after meta-analysis, it showed genome-wide significant evidence of association with lentiform nucleus volume (PMA = 4. 79 × 10-8). This commonly-carried genetic variant accounted for 2. 68 % and 0. 84 % of the trait variability in the ADNI and QTIM samples, respectively, even though the QTIM sample was on average 50 years younger. Pathway enrichment analysis revealed significant contributions of this gene to the cytochrome P450 pathway, which is involved in metabolizing numerous therapeutic drugs for pain, seizures, mania, depression, anxiety, and psychosis. The genetic variants we identified provide replicated, genome-wide significant evidence for the FMO gene cluster's involvement in lentiform nucleus volume differences in human populations.

Bivariate genome-wide association study of genetically correlated neuroimaging phenotypes from DTI and MRI through a seemingly unrelated regression model

Large multisite efforts (e.g., the ENIGMA Consortium), have shown that neuroimaging traits including tract integrity (from DTI fractional anisotropy, FA) and subcortical volumes (from T1-weighted scans) are highly heritable and promising phenotypes for discovering genetic variants associated with brain structure. However, genetic correlations (rg) among measures from these different modalities for mapping the human genome to the brain remain unknown. Discovering these correlations can help map genetic and neuroanatomical pathways implicated in development and inherited risk for disease. We use structural equation models and a twin design to find rg between pairs of phenotypes extracted from DTI and MRI scans. When controlling for intracranial volume, the caudate as well as related measures from the limbic system - hippocampal volume - showed high rg with the cingulum FA. Using an unrelated sample and a Seemingly Unrelated Regression model for bivariate analysis of this connection, we show that a multivariate GWAS approach may be more promising for genetic discovery than a univariate approach applied to each trait separately.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

Discovery and replication of gene influences on brain structure using LASSO regression

We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4, and CDH13. The top genes we identified with this method also displayed significant and widespread post hoc effects on voxelwise, tensor-based morphometry (TBM) maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2.We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8±2.2 SD years). Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain.

The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method

Background Biochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need for numerical methods that are both fast and accurate. The Bulirsch-Stoer method is an established method for solving ordinary differential equations that possesses both of these qualities. Results In this paper, we present the Stochastic Bulirsch-Stoer method, a new numerical method for simulating discrete chemical reaction systems, inspired by its deterministic counterpart. It is able to achieve an excellent efficiency due to the fact that it is based on an approach with high deterministic order, allowing for larger stepsizes and leading to fast simulations. We compare it to the Euler τ-leap, as well as two more recent τ-leap methods, on a number of example problems, and find that as well as being very accurate, our method is the most robust, in terms of efficiency, of all the methods considered in this paper. The problems it is most suited for are those with increased populations that would be too slow to simulate using Gillespie’s stochastic simulation algorithm. For such problems, it is likely to achieve higher weak order in the moments. Conclusions The Stochastic Bulirsch-Stoer method is a novel stochastic solver that can be used for fast and accurate simulations. Crucially, compared to other similar methods, it better retains its high accuracy when the timesteps are increased. Thus the Stochastic Bulirsch-Stoer method is both computationally efficient and robust. These are key properties for any stochastic numerical method, as they must typically run many thousands of simulations.