Musical performance at the Athletic contest

Thesis (M.M.)--Boston Universit

Race, Writing, and Difference

Symposium - The Law and Southern Literatur

A CONVERSATION WITH CONDOLEEZZA RICE

http://dx.doi.org/10.1017/S1742058X1100010

A Monte Carlo Evaluation of Weighted Community Detection Algorithms

The past decade has been marked with a proliferation of community detection algorithms that aim to organize nodes (e.g., individuals, brain regions, variables) into modular structures that indicate subgroups, clusters, or communities. Motivated by the emergence of big data across many fields of inquiry, these methodological developments have primarily focused on the detection of communities of nodes from matrices that are very large. However, it remains unknown if the algorithms can reliably detect communities in smaller graph sizes (i.e., 1000 nodes and fewer) which are commonly used in brain research. More importantly, these algorithms have predominantly been tested only on binary or sparse count matrices and it remains unclear the degree to which the algorithms can recover community structure for different types of matrices, such as the often used cross-correlation matrices representing functional connectivity across predefined brain regions. Of the publicly available approaches for weighted graphs that can detect communities in graph sizes of at least 1000, prior research has demonstrated that Newman's spectral approach (i.e., Leading Eigenvalue), Walktrap, Fast Modularity, the Louvain method (i.e., multilevel community method), Label Propagation, and Infomap all recover communities exceptionally well in certain circumstances. The purpose of the present Monte Carlo simulation study is to test these methods across a large number of conditions, including varied graph sizes and types of matrix (sparse count, correlation, and reflected Euclidean distance), to identify which algorithm is optimal for specific types of data matrices. The results indicate that when the data are in the form of sparse count networks (such as those seen in diffusion tensor imaging), Label Propagation and Walktrap surfaced as the most reliable methods for community detection. For dense, weighted networks such as correlation matrices capturing functional connectivity, Walktrap consistently outperformed the other approaches for recovering communities

A Pilot Double-Blind Randomized Controlled Trial of Cognitive Training Combined with Transcranial Direct Current Stimulation for Amnestic Mild Cognitive Impairment

Background: There is currently no effective intervention for improving memory in people at increased risk for dementia. Cognitive training (CT) has been promising, though effects are modest, particularly at follow-up. Objective: To investigate whether adjunctive non-invasive brain stimulation (transcranial direct current stimulation, tDCS) could enhance the memory benefits of CT in amnestic mild cognitive impairment (aMCI). Methods: Participants with aMCI were randomized to receive CT with either Active tDCS (2mA for 30min and 0.016mA for 30min) or Sham tDCS (0.016mA for 60min) for 15 sessions over a period of 5 weeks in a double-blind, sham-controlled, parallel group clinical trial. The primary outcome measure was the California Verbal Learning Task 2nd Edition. Results: 68 participants commenced the intervention. Intention-to-treat (ITT) analysis showed that the CT+Active tDCS group significantly improved at post treatment (p=0.033), and the CT+Sham tDCS group did not (p=0.050), but there was no difference between groups. At the 3-month follow-up, both groups showed large-sized memory improvements compared to pre-treatment (CT+Active tDCS: p<0.01, d=0.99; CT+Sham tDCS: p<0.01, d=0.74), although there was no significant difference between groups. Conclusion: This study found that CT+Active tDCS did not produce greater memory improvement compared to CT+Sham tDCS. Large-sized memory improvements occurred in both conditions at follow-up. One possible interpretation, based on recent novel findings, is that low intensity tDCS (used as 'sham') may have contributed biological effects. Further work should use a completely inert tDCS sham condition

Reversible DNA i-motif to hairpin switching induced by copper(II) cations

i-Motif DNA structures have previously been utilised for many different nanotechnological applications, but all have used changes in pH to fold the DNA. Herein we describe how copper(ii) cations can alter the conformation of i-motif DNA into an alternative hairpin structure which is reversible by chelation with EDTA

Strengths and weaknesses in executive functioning in children with intellectual disability

Children with intellectual disability (ID) were given a comprehensive range of executive functioning measures, which systematically varied in terms of verbal and non-verbal demands. Their performance was compared to the performance of groups matched on mental age (MA) and chronological age (CA), respectively. Twenty-two children were included in each group. Children with ID performed on par with the MA group on switching, verbal executive-loaded working memory and most fluency tasks, but below the MA group on inhibition, planning, and non-verbal executive-loaded working memory. Children with ID performed below CA comparisons on all the executive tasks. We suggest that children with ID have a specific profile of executive functioning, with MA appropriate abilities to generate new exemplars (fluency) and to switch attention between tasks, but difficulties with respect to inhibiting pre-potent responses, planning, and non-verbal executive-loaded working memory The development of different types of executive functioning skills may, to different degrees, be related to mental age and experience