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The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza

1The prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. This paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. We model outbreaks with compartment models and explicitly model non-influenza influenza-like illnesses.

epidemiology

Transmission dynamics and between-species interactions of multidrug-resistant Enterobacteriaceae

Widespread resistance to antibiotics is among the gravest threats to modern medicine, and controlling the spread of multi-drug resistant Enterobacteriaceae has been given priority status by the World Health Organization. Interventions to reduce transmission within hospital wards may be informed by modifiable patient-level risk factors for becoming colonised, however understanding of factors that influence a patients risk of acquisition is limited. We analyse data from a one year prospective carriage study in a neonatal intensive care unit in Cambodia using Bayesian hierarchical models to estimate the daily probability of acquiring multi-drug resistant organisms, while accounting for patient-level time-varying covariates, including interactions between species, and interval-censoring of transmission events. We estimate the baseline daily probability for becoming colonised with third generation cephalosporin resistant (3GC-R) Klebsiella pneumoniae as 0.142 (95% credible interval [CrI] 0.066, 0.27), nearly ten times higher than the daily probability of acquiring 3GC-R Escherichia coli (0.016 [95% CrI 0.0038, 0.049]). Prior colonization with 3GC-R K. pneumoniae was associated with a greatly increased risk of a patient acquiring 3GC-R E. coli (odds ratio [OR] 6.4 [95% CrI 2.8, 20.9]). Breast feeding was associated with a reduced risk of colonization with both 3GC-R K. pneumoniae (OR 0.73 [95% CrI 0.38, 1.5]) and E. coli (OR 0.62 [95% CrI 0.28, 1.6]). The use of an oral probiotic (Lactobacillus acidophilus) did not show clear evidence of protection against colonization with either 3GC-R K. pneumoniae (OR 0.83 [95% CrI 0.51, 1.3]) or 3GC-R E. coli (OR 1.3 [95% CrI 0.77, 2.1]). Antibiotic consumption within the past 48 hours did not strongly influence the risk of acquiring 3GC-R K. pneumoniae. For 3GC-R E. coli, ceftriaxone showed the strongest effect for increasing the risk of acquisition (OR 2.2 [95% CrI 0.66, 6.2]) and imipenem was associated with a decreased risk (OR 0.31 [95% CrI 0.099, 0.76). Using 317 whole-genome assemblies of K. pneumoniae, we determined putatively related clusters and used a range of models to infer transmission rates. Model comparison strongly favored models with a time-varying force of infection term that increased in proportion with the number of colonized patients, providing evidence of patient-to-patient transmission, including among a cluster of Klebsiella quasipneumoniae. Our findings provide support for the hypothesis that K. pneumoniae can be spread person-to-person within ward settings. Subsequent horizontal gene transfer within patients from K. pneumoniae provides the most parsimonious explanation for the strong association between colonization with 3GC-R K. pneumoniae and acquisition of 3GC-R E. coli.

epidemiology

Adjustment for index event bias in genome-wide association studies of subsequent events

Following numerous genome-wide association studies of disease susceptibility, there is increasing interest in genetic associations with disease prognosis, survival or other subsequent events. Such associations are vulnerable to index event bias, by which selection of subjects according to their disease status creates biased associations if common causes of incidence and prognosis are not accounted for. We propose a novel adjustment for index event bias using the residuals from the regression of genetic effects on prognosis on genetic effects on incidence. Our approach eliminates this bias when direct genetic effects on incidence and prognosis are independent, and otherwise reduces bias in realistic situations. In a study of idiopathic pulmonary fibrosis, we resolved a paradoxical association of the strong susceptibility gene MUC5B with increased survival, identifying instead a significant association with decreased survival. In re-analysis of a study of Crohns disease prognosis, four regions remained associated at genome-wide significance albeit with increased P-values.

epidemiology

GeneHummus: A pipeline to define gene families and their expression in legumes and beyond

During the last decade, plant biotechnological laboratories have sparked a monumental revolution with the rapid development of next sequencing technologies at affordable prices. Soon, these sequencing technologies and assembling of whole genomes will extend beyond the plant computational biologists and become commonplace within the plant biology disciplines. The current availability of large-scale genomic resources for non-traditional plant model systems (the so-called orphan crops) is enabling the construction of high-density integrated physical and genetic linkage maps with potential applications in plant breeding. The newly available fully sequenced plant genomes represent an incredible opportunity for comparative analyses that may reveal new aspects of genome biology and evolution. Analysis of the expansion and evolution of gene families across species is a common approach to infer biological functions. To date, the extent and role of gene families in plants has only been partially addressed and many gene families remain to be investigated. Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family, typically combining numerous BLAST searches and manually cleaning data. Due to the increasing abundance of genome sequences and the agronomical interest in plant gene families, the field needs a clear, automated annotation tool. Here, we present the GeneHummus pipeline, a step-by-step R-based pipeline for the identification, characterization and expression analysis of plant gene families. The impact of this pipeline comes from a reduction in hands-on annotation time combined with high specificity and sensitivity in extracting only proteins from the RefSeq database and providing the conserved domain architectures based on SPARCLE. As a case study we focused on the auxin receptor factors gene (ARF) family in Cicer arietinum (chickpea) and other legumes. We anticipate that our pipeline should be suitable for any plant gene family, and likely other gene families, vastly improving the speed and ease of genomic data processing.

bioinformatics

What drives mortality among HIV patients in a conflict setting? A prospective cohort study in the Central African Republic

BackgroundProvision of antiretroviral therapy (ART) during conflict settings is rarely attempted and little is known about the expected patterns of mortality. The Central African Republic (CAR) continues to have a low coverage of ART despite an estimated 120,000 people living with HIV and 11,000 AIDS-related deaths in 2013. We present results from a cohort in Zemio, Haut-Mboumou prefecture. This region had the highest prevalence of HIV nationally (14.8% in 2010) and was subject to repeated attacks by armed groups on civilians during the observed period.\n\nMethodsConflict from armed groups can impact cohort mortality rates i) directly if HIV patients are victims of armed conflict, or ii) indirectly if population displacement or fear of movement reduces access to ART. Using monthly counts of civilian deaths, injuries and abductions, we estimated the impact of the conflict on patient mortality. We also determine patient-level risk factors for mortality and how this varies with time spent in the cohort. Model-fitting was performed in a Bayesian framework, using generalised-linear models with terms accounting for temporal autocorrelation.\n\nResultsPatients were recruited and observed from October 2011 to May 2017. Overall 1631 patients were enrolled, giving 4107 person-years and 148 deaths. Our first model shows that patient mortality did not increase during periods of heightened conflict. The monthly risk (probability) of mortality was markedly higher at the beginning of the program (0.047 in November 2011 [95% credible interval; CrI 0.0078, 0.21]) and had declined greater than ten-fold by the end of the observed period (0.0016 in June 2017 [95% CrI 0.00042, 0.0036]). Our second model shows the risk of mortality for individual patients was highest in the first five months spent in the cohort. Male sex was associated with a higher mortality (odds ratio; OR 1.7 [95% CrI 1.2, 2.8]) along with the severity of opportunistic infections at baseline.\n\nConclusionsOur results show that chronic conflict did not appear to adversely affect rates of mortality in this cohort, and that mortality was driven predominantly by patient specific risk factors. In areas initiating ART for the first time, particular attention should be focussed on stabilising patients with advanced symptoms.\n\nFundingMedecins Sans Frontieres

epidemiology

First detection of a highly invasive freshwater amphipod (Crangonyx floridanus) in the United Kingdom

The freshwater gammarid, Crangonyx floridanus, originates from North America but has invaded and subsequently spread rapidly throughout Japan. We provide here the first genetic and microscopic evidence that C. floridanus has now also reached the United Kingdom. We found this species in two locations separated by more than 200 km (Lake Windermere in the North of the UK and Smestow Brook, West Midlands). The current distribution of C. floridanus is currently unknown, however both sites are well connected to other river and channel systems therefore the chance of further spread is high. Genetic analyses of C. floridanus indicate that British inland waters are colonised by the same linage, which also has invaded Japan. We recommend further work to assess the distribution of this species and its impact on the local fauna and flora.

ecology

Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity

ObjectivesGini coefficients have been used to describe the distribution of Chlamydia trachomatis (CT) infections among individuals with different levels of sexual activity. The objectives of this study were to investigate Gini coefficients for different sexually transmitted infections (STIs), and to determine how STI control interventions might affect the Gini coefficient over time.\n\nMethodsWe used population-based data for sexually experienced women from two British National Surveys of Sexual Attitudes and Lifestyles (Natsal-2: 1999-2001; Natsal-3: 2010-2012) to calculate Gini coefficients for CT, Mycoplasma genitalium (MG), and human papillomavirus (HPV) types 6, 11, 16 and 18. We applied bootstrap methods to assess uncertainty and to compare Gini coefficients for different STIs. We then used a mathematical model of STI transmission to study how control interventions affect Gini coefficients.\n\nResultsGini coefficients for CT and MG were 0.33 (95% confidence interval (CI): 0.18-0.49) and 0.16 (95% CI: 0.02-0.36), respectively. The relatively small coefficient for MG suggests a longer infectious duration compared with CT. The coefficients for HPV types 6, 11, 16 and 18 ranged from 0.15-0.38. During the decade between Natsal-2 and Natsal-3, the Gini coefficient for CT did not change. The transmission model shows that higher STI treatment rates are expected to reduce prevalence and increase the Gini coefficient of STIs. In contrast, increased condom use reduces STI prevalence but does not affect the Gini coefficient.\n\nConclusionsGini coefficients for STIs can help us to understand the distribution of STIs in the population, according to level of sexual activity, and could be used to inform STI prevention and treatment strategies.\n\nKey messagesO_LIThe Gini coefficient can be used to describe the distribution of STIs in a population, according to different levels of sexual activity.\nC_LIO_LIGini coefficients for Chlamydia trachomatis (CT) and human papillomavirus (HPV) type 18 appear to be higher than for Mycoplasma genitalium and HPV 6, 11 and 16.\nC_LIO_LIMathematical modelling suggests that CT screening interventions should reduce prevalence and increase the Gini coefficient, whilst condom use reduces prevalence without affecting the Gini coefficient.\nC_LIO_LIChanges in Gini coefficients over time could be used to assess the impact of STI prevention and treatment strategies.\nC_LI

epidemiology

Cytoscape stringApp: Network analysis and visualization of proteomics data

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics dataset, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp.

bioinformatics

A spatio-temporal individual-based network framework for West Nile virus in the USA: spreading pattern of West Nile virus

West Nile virus (WNV)--a mosquito-borne arbovirus-- entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential--short-range dispersal, 2) power-law--long-range dispersal in all directions, and 3) power-law biased by flyway direction--long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.\n\nAuthor summaryThe underlying pattern of West Nile virus (WNV) geographic spread across the United States is not completely clear, which is a necessary step for continental or state level mitigation strategies to reduce WNV transmission. We report a network model that explains the geographic spread of WNV in the United States. West Nile virus is a mosquito-borne pathogen that infects many avian species with different movement ranges. From our research, we found that migration patterns and routes play an essential role in the WNV spatial distribution. The virus spreads in all directions at short distances because of local birds and short-distance migratory birds. However, the virus also disperses long distances along the avian migratory routes. Our model is designed to be flexible and therefore can be used to explore spreading patterns of other infectious diseases in other geographic locations.

epidemiology

Phase Separation of YAP Reprograms Cells for Long-term YAP Target Gene Expression

Yes-associated Protein (YAP) is a transcriptional co-activator that regulates cell proliferation and survival by binding to a selective set of enhancers for potent target gene activation, but how YAP coordinates these transcriptional responses is unknown. Here, we demonstrate that YAP forms liquid-like condensates in the nucleus in response to macromolecular crowding. Formed within seconds of hyperosmotic stress, YAP condensates compartmentalized YAPs DNA binding cofactor TEAD1 along with other YAP-related transcription co-activators, including TAZ, and subsequently induced transcription of YAP-specific proliferation genes. Super-resolution imaging using Assay for Transposase Accessible Chromatin with photoactivated localization microscopy (ATAC-PALM) revealed that YAP nuclear condensates were areas enriched in accessible chromatin domains organized as super-enhancers. Initially devoid of RNA Polymerase II (Pol II), the accessible chromatin domains later acquired Pol II, producing newly transcribed RNA. Removal of YAPs intrinsically-disordered transcription activation domain (TAD) prevented YAP condensate formation and diminished downstream YAP signaling. Thus, dynamic changes in genome organization and gene activation during YAP reprogramming is mediated by liquid-liquid phase separation.

cell biology

Quantifying point-mutations in shotgun metagenomic data

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available from http://microbiology.se/software/mumame

bioinformatics

Asthma-Neoplasms Relationships: New Insights Using Machine Inference, Epidemiological Reasoning, And Big Data

BackgroundA relationship between asthma and the risk of having cancer has been identified in several studies. However, these studies have used different methodologies, been primarily cross-sectional in nature, and the results have been contradictory. Population-level analyses are required to determine if a relationship truly exists. MethodsWe developed a novel machine learning tool to infer associations, Causal Inference using the Composition of Transactions (CICT). Two all payers claim datasets of over two hundred million hospitalization encounters from the US-based Healthcare Cost and Utilization Project (HCUP) were used for discovery and validation. Associations between asthma and neoplasms were discovered in data from the State of Florida. Validation was conducted on eight cohorts of patients with asthma, and seven subtypes of asthma and COPD using datasets from the State of California. Control groups were matched by gender, age, race, and history of tobacco use. Odds ratio analysis with Bonferroni-Holm correction measured the association of asthma and COPD with 26 different benign and malignant neoplasms. ICD9CM codes were used to identify exposures and outcomes. FindingsCICT identified 17 associations between asthma and the risk of neoplasia in the discovery dataset. In the validation studies, 208 case-control analyses were conducted between subtypes of Asthma (N= 999,370, male= 33%, age= 50) and COPD (N=715,971, male = 50%, age=69) with the corresponding matched control groups (N=8,400,004, male= 42%, age= 47). Allergic asthma was associated with benign neoplasms of the meninges, salivary, pituitary, parathyroid, and thyroid glands (OR:1.52 to 2.52), and malignant neoplasms of the breast, intrahepatic biliary system, hematopoietic, and lymphatic system (OR: 1.45 to 2.05). COPD was associated with malignant neoplasms in the lung, bladder, and hematopoietic systems. InterpretationThe combined use of machine learning methods for knowledge discovery and epidemiological methods shows that allergic asthma is associated with the development of neoplasia, including in glandular organs, ductal tissues, and hematopoietic systems. Also, our findings differentiate the pattern of neoplasms between allergic asthma and obstructive asthma. This suggests that inflammatory pathways that are active in asthma also contribute to neoplastic transformation in specific organ systems such as secretory organs. FundingNone At a Glance CommentaryOver the past three decades, studies have suggested that asthma could increase the risk of developing cancer, but a consensus has not been reached. The debate persists because the current evidence has been derived using cross-sectional statistical designs, limited datasets, and small cohorts and conflicting results. In addition, the mechanism by which allergic airway inflammation contributes to neoplastic transformation is postulated but not proven. Here, we present the largest study to date on this association in patients with asthma or COPD. A knowledge discovery method was used for hypothesis generation that, when combined with epidemiological reasoning tools, identified associations between airway disease and neoplasia. The results reveal novel relationships between allergic asthma and benign glandular tumors and confirm the well-known connections between COPD and lung cancer. Further, we identified a novel association between COPD and asthma with hematological malignancies. These findings rectify contradictory results from other studies and demonstrate more specifically that the types of neoplasms associated with asthma compared to COPD that infers mechanistic plausibility.

epidemiology

LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations

Much effort has been made toward understanding the genetic architecture of complex traits and diseases. Recent results from genome-wide association studies (GWASs) suggest the importance of regulatory genetic effects and pervasive pleiotropy among complex traits. In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes, but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPMs performance and compared it with related methods. Then we applied it to analyze 44 GWASs with nine genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits. The LPM package is available at https://github.com/mingjingsi/LPM.

genetics

Population-scale proteome variation in human induced pluripotent stem cells

Realising the potential of human induced pluripotent stem cell (iPSC) technology for drug discovery, disease modelling and cell therapy requires an understanding of variability across iPSC lines. While previous studies have characterized iPS cell lines genetically and transcriptionally, little is known about the variability of the iPSC proteome. Here, we present the first comprehensive proteomic iPSC dataset, analysing 202 iPSC lines derived from 151 donors. We characterise the major genetic determinants affecting proteome and transcriptome variation across iPSC lines and identify key regulatory mechanisms affecting variation in protein abundance. Our data identified >700 human iPSC protein quantitative trait loci (pQTLs). We mapped trans regulatory effects, identifying an important role for protein-protein interactions. We discovered that pQTLs show increased enrichment in disease-linked GWAS variants, compared with RNA-based eQTLs.

genomics

AMON: Annotation of metabolite origins via networks to better integrate microbiome and metabolome data

MotivationUntargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live with the host, or from other exposures such as diet or the environment.\n\nResultsWe address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to determine the degree to which annotated compounds in the metabolome may have been produced by bacteria present, the host, either (i.e. both the bacteria and host are capable of production), or neither (i.e. neither the human or the fecal microbiome are predicted to be capable of producing the observed metabolite).\n\nAvailability and ImplementationThis software is available at https://github.com/lozuponelab/AMON as well as via pip.\n\nContactcatherine.lozupone@ucdenver.edu

bioinformatics

Signal Transduction in Human Cell Lysate via Dynamic RNA Nanotechnology

Dynamic RNA nanotechnology with small conditional RNAs (scRNAs) offers a promising conceptual approach to introducing synthetic regulatory links into endogenous biological circuits. Here, we use human cell lysate containing functional Dicer and RNases as a testbed for engineering scRNAs for conditional RNA interference (RNAi). scRNAs perform signal transduction via conditional shape change: detection of a subsequence of mRNA input X triggers formation of a Dicer substrate that is processed to yield siRNA output anti-Y targeting independent mRNA Y for destruction. Automated sequence design is performed using the reaction pathway designer within NUPACK to encode this conditional hybridization cascade into the scRNA sequence subject to the sequence constraints imposed by X and Y. Because it is difficult for secondary structure models to predict which subsequences of mRNA input X will be accessible for detection, here we develop the RNAhyb method to experimentally determine accessible windows within the mRNA that are provided to the designer as sequence constraints. We demonstrate the programmability of scRNA regulators by engineering scRNAs for transducing in both directions between two full-length mRNAs X and Y, corresponding to either the forward molecular logic \"if X then not Y\" (X [boxvl] Y) or the reverse molecular logic \"if Y then not X\" (Y [boxvl] X). In human cell lysate, we observe a strong OFF/ON conditional response with low crosstalk, corresponding to a {approx}20-fold increase in production of the siRNA output in response to the cognate vs non-cognate full-length mRNA input. Because diverse biological pathways interact with RNA, scRNAs that transduce between detection of endogenous RNA inputs and production of biologically active RNA outputs hold great promise as a synthetic regulatory paradigm.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC=\"FIGDIR/small/439273_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (10K):\norg.highwire.dtl.DTLVardef@fb2a0org.highwire.dtl.DTLVardef@98517dorg.highwire.dtl.DTLVardef@e0fe8org.highwire.dtl.DTLVardef@1364f8d_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology

Bioorthogonal Antigens Allow The Study Of Intracellular Processing And Presentation Of Post-Translationally Modified Antigens

Proteolysis is fundamental to many biological processes. In the immune system, it underpins the activation of the adaptive immune response: degradation of antigenic material into short peptides and presentation thereof on major histocompatibility complexes, leads to activation of T-cells. This initiates the adaptive immune response against many pathogens.

immunology

Encoding of an engram for food location by satiety-promoting Drd2 hippocampal neurons

Associative learning guides feeding behavior in mammals in part by using cues that link location in space to food availability. However, the elements of the top-down circuitry encoding the memory of the location of food is largely unknown, as are the high-order processes that control satiety. Here we report that hippocampal dopamine 2 receptor (D2R) neurons are specifically activated by food and that modulation of their activity reduce food intake in mice. We also found that activation of these neurons interferes with the valence of food and the acquisition of a spatial memory linking food to a location via projections from the hippocampus to the lateral septum. Finally, we showed that inputs from lateral entorhinal cortex (LEC) to the hippocampus can also drive satiety via activation of D2R cells. These data describe a previously unidentified function for hippocampal D2R cells to regulate feeding behavior and identifies a LEC->Hippocampus->Septal high-order circuit that encodes the memory of food location.

animal behavior and cognition