CASS: A distributed network clustering algorithm based on structure similarity for large-scale network.

As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched. However, these algorithms cannot analyze large-scale network data because of memory size issues. As a solution, we propose a network clustering algorithm for large-scale network data analysis using Apache Spark by changing the paradigm of the conventional clustering algorithm to improve its efficiency in the Apache Spark environment. We also apply optimization approaches such as Bloom filter and shuffle selection to reduce memory usage and execution time. By evaluating our proposed algorithm based on an average normalized cut, we confirmed that the algorithm can analyze diverse large-scale network datasets such as biological, co-authorship, internet topology and social networks. Experimental results show that the proposed algorithm can develop more accurate clusters than comparative algorithms with less memory usage. Furthermore, we confirm the proposed optimization approaches and the scalability of the proposed algorithm. In addition, we validate that clusters found from the proposed algorithm can represent biologically meaningful functions

The adhesion protein IgSF9b is coupled to neuroligin 2 via S-SCAM to promote inhibitory synapse development

Synaptic adhesion molecules regulate diverse aspects of synapse formation and maintenance. Many known synaptic adhesion molecules localize at excitatory synapses, whereas relatively little is known about inhibitory synaptic adhesion molecules. Here we report that IgSF9b is a novel, brain-specific, homophilic adhesion molecule that is strongly expressed in GABAergic interneurons. IgSF9b was preferentially localized at inhibitory synapses in cultured rat hippocampal and cortical interneurons and was required for the development of inhibitory synapses onto interneurons. IgSF9b formed a subsynaptic domain distinct from the GABA(A) receptor- and gephyrin-containing domain, as indicated by super-resolution imaging. IgSF9b was linked to neuroligin 2, an inhibitory synaptic adhesion molecule coupled to gephyrin, via the multi-PDZ protein S-SCAM. IgSF9b and neuroligin 2 could reciprocally cluster each other. These results suggest a novel mode of inhibitory synaptic organization in which two subsynaptic domains, one containing IgSF9b for synaptic adhesion and the other containing gephyrin and GABA(A) receptors for synaptic transmission, are interconnected through S-SCAM and neuroligin 2.137381sciescopu

SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance

Copyright © 2019 The Authors, some rights reserved;We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA–encoding and target sequence pairs. Deep learning–based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity–predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas911sciescopu

Interfacial Thermal Conductance Observed to be Higher in Semiconducting than Metallic Carbon Nanotubes

Thermal transport at carbon nanotube (CNT) interfaces was investigated by characterizing the interfacial thermal conductance between metallic or semiconducting CNTs and three different surfactants. We thereby resolved a difference between metallic and semiconducting CNTs. CNT portions separated by their electronic type were prepared in aqueous suspensions. After slightly heating the CNTs dispersed in the suspension, we obtained cooling curves by monitoring the transient changes in absorption, and from these cooling curves, we extracted the interfacial thermal conductance by modeling the thermal system. We found that the semiconducting CNTs unexpectedly exhibited a higher conductance of 11.5 MW/m²·K than that of metallic CNTs (9 MW/m²·K). Meanwhile, the type of surfactants hardly influenced the heat transport at the interface. The surfactant dependence is understood in terms of the coupling between the low-frequency vibrational modes of the CNTs and the surfactants. Explanations for the electronic-type dependency are considered based on the defect density in CNTs and the packing density of surfactants