Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics

Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932–45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches.     

Structural basis for c-di-GMP-mediated inside-out signaling controlling periplasmic proteolysis

The bacterial second messenger bis-(3'-5') cyclic dimeric guanosine monophosphate (c-di-GMP) has emerged as a central regulator for biofilm formation. Increased cellular c-di-GMP levels lead to stable cell attachment, which in Pseudomonas fluorescens requires the transmembrane receptor LapD. LapD exhibits a conserved and widely used modular architecture containing a HAMP domain and degenerate diguanylate cyclase and phosphodiesterase domains. c-di-GMP binding to the LapD degenerate phosphodiesterase domain is communicated via the HAMP relay to the periplasmic domain, triggering sequestration of the protease LapG, thus preventing cleavage of the surface adhesin LapA. Here, we elucidate the molecular mechanism of autoinhibition and activation of LapD based on structure-function analyses and crystal structures of the entire periplasmic domain and the intracellular signaling unit in two different states. In the absence of c-di-GMP, the intracellular module assumes an inactive conformation. Binding of c-di-GMP to the phosphodiesterase domain disrupts the inactive state, permitting the formation of a trans-subunit dimer interface between adjacent phosphodiesterase domains via interactions conserved in c-di-GMP-degrading enzymes. Efficient mechanical coupling of the conformational changes across the membrane is realized through an extensively domain-swapped, unique periplasmic fold. Our structural and functional analyses identified a conserved system for the regulation of periplasmic proteases in a wide variety of bacteria, including many free-living and pathogenic species.     

Database resources of the National Center for Biotechnology Information

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources that operate on the data in GenBank and a variety of other biological data made available through NCBI's Web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing, Human MapViewer, GeneMap'99, Human-Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, Cancer Genome Anatomy Project (CGAP), SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheri-tance in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved Domain Database (CDD). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih. gov.     

Unraveling the Genetic Etiology of Adult Antisocial Behavior: A Genome-Wide Association Study

Crime poses a major burden for society. The heterogeneous nature of criminal behavior makes it difficult to unravel its causes. Relatively little research has been conducted on the genetic influences of criminal behavior. The few twin and adoption studies that have been undertaken suggest that about half of the variance in antisocial behavior can be explained by genetic factors. In order to identify the specific common genetic variants underlying this behavior, we conduct the first genome-wide association study (GWAS) on adult antisocial behavior. Our sample comprised a community sample of 4816 individuals who had completed a self-report questionnaire. No genetic polymorphisms reached genome-wide significance for association with adult antisocial behavior. In addition, none of the traditional candidate genes can be confirmed in our study. While not genome-wide significant, the gene with the strongest association (p-value = 8.7×10⁻⁵) was DYRK1A, a gene previously related to abnormal brain development and mental retardation. Future studies should use larger, more homogeneous samples to disentangle the etiology of antisocial behavior. Biosocial criminological research allows a more empirically grounded understanding of criminal behavior, which could ultimately inform and improve current treatment strategies.     

Optimization and validation of a cost‐effective protocol for biosurveillance of invasive alien species

Environmental DNA (eDNA) metabarcoding has revolutionized biodiversity monitoring and invasive pest biosurveillance programs. The introduction of insect pests considered invasive alien species (IAS) into a non‐native range poses a threat to native plant health. The early detection of IAS can allow for prompt actions by regulating authorities, thereby mitigating their impacts. In the present study, we optimized and validated a fast and cost‐effective eDNA metabarcoding protocol for biosurveillance of IAS and characterization of insect and microorganism diversity. Forty‐eight traps were placed, following the CFIA''s annual forest insect trapping survey, at four locations in southern Ontario that are high risk for forest IAS. We collected insects and eDNA samples using Lindgren funnel traps that contained a saturated salt (NaCl) solution in the collection jar. Using cytochrome c oxidase I (COI) as a molecular marker, a modified Illumina protocol effectively identified 2,535 Barcode Index Numbers (BINs). BINs were distributed among 57 Orders and 304 Families, with the vast majority being arthropods. Two IAS (Agrilus planipennis and Lymantria dispar) are regulated by the Canadian Food Inspection Agency (CFIA) as plant health pests, are known to occur in the study area, and were identified through eDNA in collected traps. Similarly, using 16S ribosomal RNA and nuclear ribosomal internal transcribed spacer (ITS), five bacterial and three fungal genera, which contain species of regulatory concern across several Canadian jurisdictions, were recovered from all sampling locations. Our study results reaffirm the effectiveness and importance of integrating eDNA metabarcoding as part of identification protocols in biosurveillance programs.     

Reducing the Decline in Physical Activity during Pregnancy: A Systematic Review of Behaviour Change Interventions

Purpose

Physical activity (PA) typically declines throughout pregnancy. Low levels of PA are associated with excessive weight gain and subsequently increase risk of pre-eclampsia, gestational diabetes mellitus, hypertension disorders, delivery by caesarean section and stillbirth. Systematic reviews on PA during pregnancy have not explored the efficacy of behaviour change techniques or related theory in altering PA behaviour. This systematic review evaluated the content of PA interventions to reduce the decline of PA in pregnant women with a specific emphasis on the behaviour change techniques employed to elicit this change.

Search and Review Methodology

Literature searches were conducted in eight databases. Strict inclusion and exclusion criteria were employed. Two reviewers independently evaluated each intervention using the behaviour change techniques (BCT) taxonomy to identify the specific behaviour change techniques employed. Two reviewers independently assessed the risk of bias using the guidelines from the Cochrane Collaboration. Overall quality was determined using the GRADE approach.

Findings

A total of 1140 potentially eligible papers were identified from which 14 studies were selected for inclusion. Interventions included counselling (n = 6), structured exercise (n = 6) and education (n = 2). Common behaviour change techniques employed in these studies were goal setting and planning, feedback, repetition and substitution, shaping knowledge and comparison of behaviours. Regular face-to-face meetings were also commonly employed. PA change over time in intervention groups ranged from increases of 28% to decreases of 25%. In 8 out of 10 studies, which provided adequate data, participants in the intervention group were more physically active post intervention than controls.

Conclusions and Implications

Physical activity interventions incorporating behaviour change techniques help reduce the decline in PA throughout pregnancy. Range of behaviour change techniques can be implemented to reduce this decline including goals and planning, shaping knowledge and comparison of outcomes. A lack of high quality interventions hampers conclusions of intervention effectiveness.     

Phase Coexistence Curves for Off-lattice Polymer-Solvent Mixtures: Gibbs-Duhem Integration Simulations

The Gibbs-Duhem integration scheme is combined with the osmotic Gibbs-ensemble simulation method presented in previous work [Brennan, J.K. and Madden, W.G. "Phase coexistence curves for off-lattice polymer-solvent mixtures: Gibbs-ensemble simulations." Macromolecules , 2002, 35, 2827.] to calculate the phase coexistence of a polymer-solvent mixture. Gibbs-Duhem integration simulations are carried out at temperatures for which the osmotic Gibbs-ensemble method is not valid because the solvent-rich phase contains a significant amount of polymer. This combined strategy allows for the calculation of the full coexistence curve for polymer-solvent systems in the continuum. An alternative formulation of the Gibbs-Duhem integration algorithm is also presented. A major strength of the technique is that neither chain insertions nor deletions are required. The method allows for the calculation of the phase behavior of polymer-solvent mixtures containing long chains or branched and networked chains not previously possible.     

Reconstitution of Homomeric GluA2flop Receptors in Supported Lipid Membranes: FUNCTIONAL AND STRUCTURAL PROPERTIES*

AMPA receptors (AMPARs) are glutamate-gated ion channels ubiquitous in the vertebrate central nervous system, where they mediate fast excitatory neurotransmission and act as molecular determinants of memory formation and learning. Together with detailed analyses of individual AMPAR domains, structural studies of full-length AMPARs by electron microscopy and x-ray crystallography have provided important insights into channel assembly and function. However, the correlation between the structure and functional states of the channel remains ambiguous particularly because these functional states can be assessed only with the receptor bound within an intact lipid bilayer. To provide a basis for investigating AMPAR structure in a membrane environment, we developed an optimized reconstitution protocol using a receptor whose structure has previously been characterized by electron microscopy. Single-channel recordings of reconstituted homomeric GluA2_flop receptors recapitulate key electrophysiological parameters of the channels expressed in native cellular membranes. Atomic force microscopy studies of the reconstituted samples provide high-resolution images of membrane-embedded full-length AMPARs at densities comparable to those in postsynaptic membranes. The data demonstrate the effect of protein density on conformational flexibility and dimensions of the receptors and provide the first structural characterization of functional membrane-embedded AMPARs, thus laying the foundation for correlated structure-function analyses of the predominant mediators of excitatory synaptic signals in the brain.