Liquid Phase Hydrodechlorination of Dieldrin and DDT over Pd/C and Raney-Ni

Selectivity and product distribution of hydrodechlorination (HDCl) of dieldrin and DDT are studied in different liquid phase systems, namely in: (1) in ethanol; and (2) in the supported ionic liquid heterogeneous catalytic system (multiphase system), composed by the organic phase and aqueous KOH, a quaternary ammonium ionic liquid promoter (Aliquat 336), and a metal catalyst, e.g. 5% Pd/C, 5% Pt/C, or Raney-Ni. At 50 8C and atmospheric pressure of hydrogen, a quantitative hydrodechlorination of DDT in the biphasic system with ionic liquid layer is achieved in 40 min and in 4 h with Raney-Ni and Pd/C, respectively, while the reaction on Pt/C or on Pd/C without Aliquat 336 is slow. Dieldrin undergoes partial dechlorination, with high selectivity achievable only for its mono- and bi-dechlorination products. Dechlorination pathways and reactivity of different types of organic chlorine atoms versus the catalyst nature and other conditions are discussed

Personality Is Reflected in the Brain's Intrinsic Functional Architecture

Personality describes persistent human behavioral responses to broad classes of environmental stimuli. Investigating how personality traits are reflected in the brain's functional architecture is challenging, in part due to the difficulty of designing appropriate task probes. Resting-state functional connectivity (RSFC) can detect intrinsic activation patterns without relying on any specific task. Here we use RSFC to investigate the neural correlates of the five-factor personality domains. Based on seed regions placed within two cognitive and affective ‘hubs’ in the brain—the anterior cingulate and precuneus—each domain of personality predicted RSFC with a unique pattern of brain regions. These patterns corresponded with functional subdivisions responsible for cognitive and affective processing such as motivation, empathy and future-oriented thinking. Neuroticism and Extraversion, the two most widely studied of the five constructs, predicted connectivity between seed regions and the dorsomedial prefrontal cortex and lateral paralimbic regions, respectively. These areas are associated with emotional regulation, self-evaluation and reward, consistent with the trait qualities. Personality traits were mostly associated with functional connections that were inconsistently present across participants. This suggests that although a fundamental, core functional architecture is preserved across individuals, variable connections outside of that core encompass the inter-individual differences in personality that motivate diverse responses

An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included

Neural Representations for Visual Categories

This poster presented at Cognitive Neuroscience Society 2016 addresses debates in the literature on the neural representation of visual category information. A localized model has argued that each category is selective to domain-specific brain areas, whereas a distributed model posits that multiple areas contribute information to each category. Here, we reconciled these disparate findings using a study that showed participants four visual categories (faces, letters, fruits/veggies, and vehicles) in a slow event-related fMRI design. Patterns of brain activity were measured in ventral visual areas and used to predict the category seen on each trial. We classified patterns of brain activity for each region in isolation (individual region analysis) or all together (combined region model). We found that while category information is distributed across regions (individual region analysis), when we remove the shared information found between regions (combined region analysis), then we found the unique information for each category is localized

Category Representations 2

Zarrar Shehzad and Greg McCarthy have a poster at Cognitive Neuroscience Society 2016 entitled "Distributed or Localized Neural Representations For Visual Categories?". The work addresses debates in the literature on the neural representation of visual category information. A localized model has argued that each category is selective to domain-specific brain areas, whereas a distributed model posits that multiple areas contribute information to each category. Here, we reconciled these disparate findings using a study that showed participants four visual categories (faces, letters, fruits/veggies, and vehicles) in a slow event-related fMRI design. Patterns of brain activity were measured in ventral visual areas and used to predict the category seen on each trial. We classified patterns of brain activity for each region in isolation (individual region analysis) or all together (combined region model). We found that while category information is distributed across regions (individual region analysis), when we remove the shared information found between regions (combined region analysis), then we found the unique information for each category is localized

quality-assessment-protocol: Release 1.0.0

<p>This is the initial release of the Preprocessed Connectome Project's Quality Assessment Protocol (http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/). This includes a collection of quality assessment metrics from the neuroimaging literature. These metrics are meant to be applied to raw structural and functional neuroimaging datasets.</p