How does cognition evolve? Phylogenetic comparative psychology.

Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution.

Comparative genomic data of the Avian Phylogenomics Project.

BACKGROUND: The evolutionary relationships of modern birds are among the most challenging to understand in systematic biology and have been debated for centuries. To address this challenge, we assembled or collected the genomes of 48 avian species spanning most orders of birds, including all Neognathae and two of the five Palaeognathae orders, and used the genomes to construct a genome-scale avian phylogenetic tree and perform comparative genomics analyses (Jarvis et al. in press; Zhang et al. in press). Here we release assemblies and datasets associated with the comparative genome analyses, which include 38 newly sequenced avian genomes plus previously released or simultaneously released genomes of Chicken, Zebra finch, Turkey, Pigeon, Peregrine falcon, Duck, Budgerigar, Adelie penguin, Emperor penguin and the Medium Ground Finch. We hope that this resource will serve future efforts in phylogenomics and comparative genomics. FINDINGS: The 38 bird genomes were sequenced using the Illumina HiSeq 2000 platform and assembled using a whole genome shotgun strategy. The 48 genomes were categorized into two groups according to the N50 scaffold size of the assemblies: a high depth group comprising 23 species sequenced at high coverage (>50X) with multiple insert size libraries resulting in N50 scaffold sizes greater than 1 Mb (except the White-throated Tinamou and Bald Eagle); and a low depth group comprising 25 species sequenced at a low coverage (~30X) with two insert size libraries resulting in an average N50 scaffold size of about 50 kb. Repetitive elements comprised 4%-22% of the bird genomes. The assembled scaffolds allowed the homology-based annotation of 13,000 ~ 17000 protein coding genes in each avian genome relative to chicken, zebra finch and human, as well as comparative and sequence conservation analyses. CONCLUSIONS: Here we release full genome assemblies of 38 newly sequenced avian species, link genome assembly downloads for the 7 of the remaining 10 species, and provide a guideline of genomic data that has been generated and used in our Avian Phylogenomics Project. To the best of our knowledge, the Avian Phylogenomics Project is the biggest vertebrate comparative genomics project to date. The genomic data presented here is expected to accelerate further analyses in many fields, including phylogenetics, comparative genomics, evolution, neurobiology, development biology, and other related areas.

Air pollution, Fuel Usage and Health Outcomes in Madre de Dios, Peru: a Comparative Cross Sectional Study

Air pollution is a common problem. Particulate matter generated from air pollution has been tied to adverse health outcomes associated with cardiovascular disease. Biomass fuels are a specific contributor to increased particulate matter and arise as a result of indoor heating, cook stoves and indoor food preparation. This is a two part cross sectional study looking at communities in the Madre de Dios region. Survey data was collected from 9 communities along the Madre de Dios River. Individual level household PM2.5 was also collected as a means to generate average PM data stratified by fuel use. Data collection was affected by a number of outside factors, which resulted in a loss of data. Results from the cross-sectional study indicate that hypertension is not a significant source of morbidity. Obesity is prevalent and significantly associated with kitchen venting method indicating a potential relationship.

Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.

Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.

Restorative Justice and Political Forgiveness: A Comparative Study of Truth and Reconciliation Commissions

This research project involves a comparative, cross-national study of truth and reconciliation commissions (TRCs) in countries around the world that have used these extra-judicial institutions to pursue justice and promote national reconciliation during periods of democratic transition or following a civil conflict marked by intense violence and severe human rights abuses. An important objective of truth and reconciliation commissions involves instituting measures to address serious human rights abuses that have occurred as a result of discrimination, ethnocentrism and racism. In recent years, rather than solely utilizing traditional methods of conflict resolution and criminal prosecution, transitional governments have established truth and reconciliation commissions as part of efforts to foster psychological, social and political healing.

The primary objective of this research project is to determine why there has been a proliferation of truth and reconciliation commissions around the world in recent decades, and assess whether the perceived effectiveness of these commissions is real and substantial. In this work, using a multi-method approach that involves quantitative and qualitative analysis, I consider the institutional design and structural composition of truth and reconciliation commissions, as well as the roles that these commissions play in the democratic transformation of nations with a history of civil conflict and human rights violations.

In addition to a focus on institutional design of truth and reconciliation commissions, I use a group identity framework that is grounded in social identity theory to examine the historical background and sociopolitical context in which truth commissions have been adopted in countries around the world. This group identity framework serves as an invaluable lens through which questions related to truth and reconciliation commissions and other transitional justice mechanisms can be explored. I also present a unique theoretical framework, the reconciliatory democratization paradigm, that is especially useful for examining the complex interactions between the various political elements that directly affect the processes of democratic consolidation and reconciliation in countries in which truth and reconciliation commissions have been established. Finally, I tackle the question of whether successor regimes that institute truth and reconciliation commissions can effectively address the human rights violations that occurred in the past, and prevent the recurrence of these abuses.

A Comparative Review of Computational Methods as Applied to Gold(I) Complexes and Mechanisms

In the last two decades, the field of homogeneous gold catalysis has been

extremely active, growing at a rapid pace. Another rapidly-growing field—that of

computational chemistry—has often been applied to the investigation of various gold-

catalyzed reaction mechanisms. Unfortunately, a number of recent mechanistic studies

have utilized computational methods that have been shown to be inappropriate and

inaccurate in their description of gold chemistry. This work presents an overview of

available computational methods with a focus on the approximations and limitations

inherent in each, and offers a review of experimentally-characterized gold(I) complexes

and proposed mechanisms as compared with their computationally-modeled

counterparts. No aim is made to identify a “recommended” computational method for

investigations of gold catalysis; rather, discrepancies between experimentally and

computationally obtained values are highlighted, and the systematic errors between

different computational methods are discussed.