Context-specific activations are a hallmark of the neural basis of individual differences in general executive function

Common executive functioning (cEF) is a domain-general factor that captures shared variance in performance across diverse executive function tasks. To investigate the neural mechanisms of individual differences in cEF (e.g., goal maintenance, biasing), we conducted the largest fMRI study of multiple executive tasks to date (N = 546). Group average activation during response inhibition (antisaccade task), working memory updating (keep track task), and mental set shifting (number–letter switch task) overlapped in classic cognitive control regions. However, there were no areas across tasks that were consistently correlated with individual differences in cEF ability. Although similar brain areas are recruited when completing different executive function tasks, activation levels of those areas are not consistently associated with better performance. This pattern is inconsistent with a simple model in which higher cEF is associated with greater or less activation of a set of control regions across different task contexts; however, it is potentially consistent with a model in which individual differences in cEF primarily depend on activation of domain-specific targets of executive function. Brain features that explain commonalities in executive function performance across tasks remain to be discovered

Modeling and Validating Chronic Pharmacological Manipulation of Circadian Rhythms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/1/psp4201334-sup-0010.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/2/psp4201334-sup-0009.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/3/psp4201334-sup-0011.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/4/psp4201334-sup-0008.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/5/psp4201334-sup-0005.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/6/psp4201334-sup-0012.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/7/psp4201334-sup-0006.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/8/psp4201334-sup-0013.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/9/psp4201334.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110096/10/psp4201334-sup-0007.pd

Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Toward open sharing of task-based fMRI data: the OpenfMRI project

The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function

Multiple publications: The main reason for the retraction of papers in computer science

This paper intends to review the reasons for the retraction over the last decade. The paper particularly aims at reviewing these reasons with reference to computer science field to assist authors in comprehending the style of writing. To do that, a total of thirty-six retracted papers found on the Web of Science within Jan 2007 through July 2017 are explored. Given the retraction notices which are based on ten common reasons, this paper classifies the two main categories, namely random and nonrandom retraction. Retraction due to the duplication of publications scored the highest proportion of all other reasons reviewed

A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a “massive univariate” approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement

Genomic determinants of organohalide-respiration in Geobacter lovleyi, an unusual member of the Geobacteraceae

Background Geobacter lovleyi is a unique member of the Geobacteraceae because strains of this species share the ability to couple tetrachloroethene (PCE) reductive dechlorination to cis-1,2-dichloroethene (cis-DCE) with energy conservation and growth (i.e., organohalide respiration). Strain SZ also reduces U(VI) to U(IV) and contributes to uranium immobilization, making G. lovleyi relevant for bioremediation at sites impacted with chlorinated ethenes and radionuclides. G. lovleyi is the only fully sequenced representative of this distinct Geobacter clade, and comparative genome analyses identified genetic elements associated with organohalide respiration and elucidated genome features that distinguish strain SZ from other members of the Geobacteraceae. Results Sequencing the G. lovleyi strain SZ genome revealed a 3.9 Mbp chromosome with 54.7% GC content (i.e., the percent of the total guanines (Gs) and cytosines (Cs) among the four bases within the genome), and average amino acid identities of 53–56% compared to other sequenced Geobacter spp. Sequencing also revealed the presence of a 77 kbp plasmid, pSZ77 (53.0% GC), with nearly half of its encoded genes corresponding to chromosomal homologs in other Geobacteraceae genomes. Among these chromosome-derived features, pSZ77 encodes 15 out of the 24 genes required for de novo cobalamin biosynthesis, a required cofactor for organohalide respiration. A plasmid with 99% sequence identity to pSZ77 was subsequently detected in the PCE-dechlorinating G. lovleyi strain KB-1 present in the PCE-to-ethene-dechlorinating consortium KB-1. Additional PCE-to-cis-DCE-dechlorinating G. lovleyi strains obtained from the PCE-contaminated Fort Lewis, WA, site did not carry a plasmid indicating that pSZ77 is not a requirement (marker) for PCE respiration within this species. Chromosomal genomic islands found within the G. lovleyi strain SZ genome encode two reductive dehalogenase (RDase) homologs and a putative conjugative pilus system. Despite the loss of many c-type cytochrome and oxidative-stress-responsive genes, strain SZ retained the majority of Geobacter core metabolic capabilities, including U(VI) respiration. Conclusions Gene acquisitions have expanded strain SZ’s respiratory capabilities to include PCE and TCE as electron acceptors. Respiratory processes core to the Geobacter genus, such as metal reduction, were retained despite a substantially reduced number of c-type cytochrome genes. pSZ77 is stably maintained within its host strains SZ and KB-1, likely because the replicon carries essential genes including genes involved in cobalamin biosynthesis and possibly corrinoid transport. Lateral acquisition of the plasmid replicon and the RDase genomic island represent unique genome features of the PCE-respiring G. lovleyi strains SZ and KB-1, and at least the latter signifies adaptation to PCE contamination

Genomic determinants of organohalide-respiration in Geobacter lovleyi, an unusual member of the Geobacteraceae

Background Geobacter lovleyi is a unique member of the Geobacteraceae because strains of this species share the ability to couple tetrachloroethene (PCE) reductive dechlorination to cis-1,2-dichloroethene (cis-DCE) with energy conservation and growth (i.e., organohalide respiration). Strain SZ also reduces U(VI) to U(IV) and contributes to uranium immobilization, making G. lovleyi relevant for bioremediation at sites impacted with chlorinated ethenes and radionuclides. G. lovleyi is the only fully sequenced representative of this distinct Geobacter clade, and comparative genome analyses identified genetic elements associated with organohalide respiration and elucidated genome features that distinguish strain SZ from other members of the Geobacteraceae. Results Sequencing the G. lovleyi strain SZ genome revealed a 3.9 Mbp chromosome with 54.7% GC content (i.e., the percent of the total guanines (Gs) and cytosines (Cs) among the four bases within the genome), and average amino acid identities of 53–56% compared to other sequenced Geobacter spp. Sequencing also revealed the presence of a 77 kbp plasmid, pSZ77 (53.0% GC), with nearly half of its encoded genes corresponding to chromosomal homologs in other Geobacteraceae genomes. Among these chromosome-derived features, pSZ77 encodes 15 out of the 24 genes required for de novo cobalamin biosynthesis, a required cofactor for organohalide respiration. A plasmid with 99% sequence identity to pSZ77 was subsequently detected in the PCE-dechlorinating G. lovleyi strain KB-1 present in the PCE-to-ethene-dechlorinating consortium KB-1. Additional PCE-to-cis-DCE-dechlorinating G. lovleyi strains obtained from the PCE-contaminated Fort Lewis, WA, site did not carry a plasmid indicating that pSZ77 is not a requirement (marker) for PCE respiration within this species. Chromosomal genomic islands found within the G. lovleyi strain SZ genome encode two reductive dehalogenase (RDase) homologs and a putative conjugative pilus system. Despite the loss of many c-type cytochrome and oxidative-stress-responsive genes, strain SZ retained the majority of Geobacter core metabolic capabilities, including U(VI) respiration. Conclusions Gene acquisitions have expanded strain SZ’s respiratory capabilities to include PCE and TCE as electron acceptors. Respiratory processes core to the Geobacter genus, such as metal reduction, were retained despite a substantially reduced number of c-type cytochrome genes. pSZ77 is stably maintained within its host strains SZ and KB-1, likely because the replicon carries essential genes including genes involved in cobalamin biosynthesis and possibly corrinoid transport. Lateral acquisition of the plasmid replicon and the RDase genomic island represent unique genome features of the PCE-respiring G. lovleyi strains SZ and KB-1, and at least the latter signifies adaptation to PCE contamination

Ethical and methodological issues in engaging young people living in poverty with participatory research methods

This paper discusses the methodological and ethical issues arising from a project that focused on conducting a qualitative study using participatory techniques with children and young people living in disadvantage. The main aim of the study was to explore the impact of poverty on children and young people's access to public and private services. The paper is based on the author's perspective of the first stage of the fieldwork from the project. It discusses the ethical implications of involving children and young people in the research process, in particular issues relating to access and recruitment, the role of young people's advisory groups, use of visual data and collection of data in young people's homes. The paper also identifies some strategies for addressing the difficulties encountered in relation to each of these aspects and it considers the benefits of adopting participatory methods when conducting research with children and young people