Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursive-input fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.Comment: Accepted by ICCV201

Financial Intermediation Chains in an OTC Market

This paper analyzes financial intermediation chains in a search model with an endogenous intermediary sector. We show that the chain length and price dispersion among interdealer trades are decreasing in search cost, search speed, and market size but increasing in investors\u27 trading needs. Using data from the U.S. corporate bond market, we find evidence broadly consistent with these predictions. Moreover, as search speed approaches infinity, the search equilibrium does not always converge to the centralized-market equilibrium: prices and allocation converge, but the trading volume might not. Finally, we analyze the multiplicity and stability of the equilibrium

Financial Intermediation Chains in an OTC Market

This paper analyzes financial intermediation chains in a search model with an endogenous intermediary sector. We show that the chain length and price dispersion among inter-dealer trades are decreasing in search cost, search speed, and market size, but increasing in investors’ trading needs. Using data from the U.S. corporate bond market, we find evidence broadly consistent with these predictions. Moreover, as search speed approaches infinity, the search equilibrium does not always converge to the centralized-market equilibrium: prices and allocation converge, but the trading volume may not. Finally, the multiplicity and stability of the equilibrium is analyzed

Financial Intermediation Chains in an OTC Market

This paper analyzes financial intermediation chains in a search model with an endogenous intermediary sector. We show that the chain length and price dispersion among inter-dealer trades are decreasing in search cost, search speed, and market size, but increasing in investors’ trading needs. Using data from the U.S. corporate bond market, we find evidence broadly consistent with these predictions. Moreover, as search speed approaches infinity, the search equilibrium does not always converge to the centralized-market equilibrium: prices and allocation converge, but the trading volume may not. Finally, the multiplicity and stability of the equilibrium is analyzed

Improving accuracy of protein contact prediction using balanced network deconvolution

Residue contact map is essential for protein three‐dimensional structure determination. But most of the current contact prediction methods based on residue co‐evolution suffer from high false‐positives as introduced by indirect and transitive contacts (i.e., residues A–B and B–C are in contact, but A–C are not). Built on the work by Feizi et al. (Nat Biotechnol 2013; 31:726–733), which demonstrated a general network model to distinguish direct dependencies by network deconvolution, this study presents a new balanced network deconvolution (BND) algorithm to identify optimized dependency matrix without limit on the eigenvalue range in the applied network systems. The algorithm was used to filter contact predictions of five widely used co‐evolution methods. On the test of proteins from three benchmark datasets of the 9th critical assessment of protein structure prediction (CASP9), CASP10, and PSICOV (precise structural contact prediction using sparse inverse covariance estimation) database experiments, the BND can improve the medium‐ and long‐range contact predictions at the L/5 cutoff by 55.59% and 47.68%, respectively, without additional central processing unit cost. The improvement is statistically significant, with a P‐value < 5.93 × 10−3 in the Student's t‐test. A further comparison with the ab initio structure predictions in CASPs showed that the usefulness of the current co‐evolution‐based contact prediction to the three‐dimensional structure modeling relies on the number of homologous sequences existing in the sequence databases. BND can be used as a general contact refinement method, which is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/BND/. Proteins 2015; 83:485–496. © 2014 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110720/1/prot24744.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110720/2/prot24744-sup-0001-suppinfo.pd

Sensitive and easily recyclable plasmonic SERS substrate based on Ag nanowires in mesoporous silica

Raman spectra were obtained by a Renishaw inVia with a laser of 532 nm and 0.5% strength, samples were arranged on the silica plate. X-ray diffraction (XRD) patterns of the samples were recorded on a Rigaku D/MAX- 2550 diffractometer using Cu Kα radiation of wavelength 1.5406 Å, typically run at a voltage of 40 kV and current of 100 mA. UV-visible absorbance spectra were achieved for the dry pressed disk samples using a Scan UV-Vis spectrophotometer (Varian, Cary 500) equipped with an integrating sphere assembly, using BaSO4 as a reflectance sample. Transmission electron microscopy (TEM) images were collected on a JEOL JEM 2010F, electron microscope operated at an acceleration voltage of 200 kV. By utilizing the Barrett−Joyner−Halenda (BJH) model, the pore volumes and pore size distributions were got from the adsorption branches of isotherms

BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks

Modern science of networks has brought significant advances to our understanding of complex systems biology. As a representative model of systems biology, Protein Interaction Networks (PINs) are characterized by a remarkable modular structures, reflecting functional associations between their components. Many methods were proposed to capture cohesive modules so that there is a higher density of edges within modules than those across them. Recent studies reveal that cohesively interacting modules of proteins is not a universal organizing principle in PINs, which has opened up new avenues for revisiting functional modules in PINs. In this paper, functional clusters in PINs are found to be able to form unorthodox structures defined as bi-sparse module. In contrast to the traditional cohesive module, the nodes in the bi-sparse module are sparsely connected internally and densely connected with other bi-sparse or cohesive modules. We present a novel protocol called the BinTree Seeking (BTS) for mining both bi-sparse and cohesive modules in PINs based on Edge Density of Module (EDM) and matrix theory. BTS detects modules by depicting links and nodes rather than nodes alone and its derivation procedure is totally performed on adjacency matrix of networks. The number of modules in a PIN can be automatically determined in the proposed BTS approach. BTS is tested on three real PINs and the results demonstrate that functional modules in PINs are not dominantly cohesive but can be sparse. BTS software and the supporting information are available at: www.csbio.sjtu.edu.cn/bioinf/BTS/

Higher-order Oscillatory Planar Hall Effect in Topological Kagome Metal

Exploration of exotic transport behavior for quantum materials is of great interest and importance for revealing exotic orders to bring new physics. In this Letter, we report the observation of exotic prominent planar Hall effect (PHE) and planar anisotropic magnetoresistivity (PAMR) in strange kagome metal KV$_3$Sb$_5$. The PHE and PAMR, which are driven by an in-plane magnetic field and display sharp difference from other Hall effects driven by an out-of-plane magnetic field or magnetization, exhibit exotic higher-order oscillations in sharp contrast to those following empirical rule only allowing twofold symmetrical oscillations. These higher-order oscillations exhibit strong field and temperature dependence and vanish around charge density wave (CDW) transition. The unique transport properties suggest a significant interplay of the lattice, magnetic and electronic structure in KV$_3$Sb$_5$. This interplay can couple the hidden anisotropy and transport electrons leading to the novel PHE and PAMR in contrast to other materials