RGB-T salient object detection via fusing multi-level CNN features

RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast

How many supermassive black hole binaries are detectable through tracking relative motions by sub/millimeter VLBI

The sub/millimeter wavelengths (86-690 GHz) very long baseline interferometry (VLBI) will provide $\sim5-40\ \mu$as angular resolution, $\sim10$ mJy baseline sensitivity, and $\sim 1\ \mu$as/yr proper motion precision, which can directly detect supermassive black hole binary (SMBHB) systems by imaging the two visible sources and tracking their relative motions. Such a way exhibits an advantage compared to indirect detect methods of observing periodic signals in motion and light curves, which are difficult to confirm from competing models. Moreover, tracking relative motion at sub/millimeter wavelengths is more reliable, as there is a negligible offset between the emission region and the black hole center. In this way, it is unnecessary to correct the black hole location by a prior of jet morphology as it would be required at longer wavelengths. We extend the formalism developed in D'Orazio & Loeb (2018) to link the observations with the orbital evolution of SMBHBs from the $\lesssim$10 kpc dynamical friction stages to the $\lesssim 0.01$ pc gravitational radiation stages, and estimate the detectable numbers of SMBHBs. By assuming 5\% of AGNs holding SMBHBs, we find that the number of detectable SMBHBs with redshift $z\le 0.5$ and mass $M\leq 10^{11}M_\odot$ is about 20. Such detection relies heavily on proper motion precision and sensitivity. Furthermore, we propose that the simultaneous multi-frequency technique plays a key role in meeting the observational requirements

3-Chloro-6-(3,5-dimethyl-1H-pyrazol-1-yl)picolinic acid–triphenyl­phosphine oxide (1/1)

In the title 1:1 adduct, C11H10ClN3O2·C18H15OP, the dihedral angle between the pyridine and pyrazole rings is 10.3 (2)°. The two components of the adduct are linked by an O—H⋯O hydrogen bond

3-Methyl-1-(3-nitro­phen­yl)-5-phenyl-4,5-dihydro-1H-pyrazole

In the title compound, C16H15N3O2, the planar [maximum deviation 0.156 (2) Å] pyrazoline ring is nearly coplanar with the 3-nitro­phenyl group and is approximately perpendicular to the phenyl ring, making dihedral angles of 3.80 (8) and 80.58 (10)°, respectively. Weak inter­molecular C—H⋯O hydrogen bonding is present in the crystal structure

5-(2-Fur­yl)-3-methyl-1-(3-nitro­phen­yl)-4,5-dihydro-1H-pyrazole

In the title compound, C14H13N3O3, the pyrazoline ring assumes an envelope conformation with the furanyl-bearing C atom at the flap position. The dihedral angle between the furan and nitrobenzene rings is 84.40 (9)°. Weak inter­molecular C—H⋯O hydrogen bonding is present in the crystal structure

N′-[(E)-3-Indol-3-ylmethyl­ene]isonicotino­hydrazide monohydrate

Crystals of the title compound, C15H12N4O·H2O, were obtained from a condensation reaction of isonicotinylhydrazine and 3-indolylformaldehyde. The mol­ecule assumes an E configuration, with the isonicotinoylhydrazine and indole units located on the opposite sites of the C=N double bond. In the mol­ecular structure the pyridine ring is twisted with respect to the indole ring system, forming a dihedral angle of 44.72 (7)°. Extensive classical N—H⋯N, N—H⋯O, O—H⋯O and O—H⋯N hydrogen bonding and weak C—H⋯O inter­actions are present in the crystal structure

The protein disulfide isomerase 1 of Phytophthora parasitica (PpPDI1) is associated with the haustoria-like structures and contributes to plant infection

The protein disulfide isomerase (PDI) is a ubiquitous and multifunction enzyme belonging to the thioredoxin (TRX) superfamily, which can reduce, oxidize, and catalyze dithiol-disulfide exchange reactions. Except performing housekeeping function in helping to maintain proteins in a more stable conformation, there is some evidence to indicate that PDI is involved in pathogen infection process. In a high-throughput screening for necrosis-inducing factors by Agrobacterium tumefaciens-mediated transient expression assay, a typical PDI gene from Phytophthora parasitica (PpPDI1) was identified and confirmed to induce strong cell death on Nicotiana benthamiana leaves. PpPDI1 is conserved in eukaryotes but predicted a secreted protein. Deletion mutant analyses showed that the first CGHC motif in the active domain of PpPDI1 is essential for inducing cell death. Using P. parasitica transformation method, the silencing efficiency was very low, suggesting that PpPDI1 is essential for the pathogen. Translational fusion to the enhanced green fluorescent protein (EGFP) in stable P. parasitica transformants showed that PpPDI1 is associated with haustoria-like structures during pathogen infection. Furthermore, the PpPDI1-EGFP-expressing transformants increase the number of haustoria-like structures and exhibit enhanced virulence to N. benthamiana. These results indicate that PpPDI1 might be a virulence factor of P. parasitica and contributes to plant infection

Effect of bone marrow mesenchymal stem cells on the Smad expression of hepatic fibrosis rats

AbstractObjectiveTo investigate the impact of bone marrow mesenchymal stem cells on Smad expression of hepatic fibrosis rats.MethodsA total of 48 adult female SD rats were randomly divided into three groups, normal control group (n=10), observation group (n=19) with liver fibrosis model rats injected with BMSCs cells; model group (n=19), with liver fibrosis model rats injected with physiological saline. Serum index, TGF-β1 and Smad expression were detected.ResultsType III procollagen, IV collagen, hyaluronic acid, laminin levels of observation group were significantly lower than those of model group (P<0.05). The content and expression of TGF-β1 in serum and liver tissue of observation group were significantly lower than those of model group(P<0.05). Compared with normal control group, the Smad3, Smad4 mRNA and protein expression of model group were significantly increased, the Smad7 mRNA and protein expression were significantly reduced (P<0.05). Compared with model group, Smad3, Smad4 mRNA and protein expression of observation group were significantly reduced, and Smad7 mRNA expression were significantly increased (P<0.05).ConclusionsBMSCs can regulate Smad expression to some extent, and reduce the degree of liver fibrosis

(E)-N′-[1-(Thio­phen-2-yl)ethyl­idene]benzohydrazide

The title compound, C13H12N2OS, was obtained from the condensation reaction of 2-acetyl­thio­phene and benzohydrazide. In the mol­ecule, the formohydrazide fragment is approximately planar (r.m.s deviation = 0.0146 Å) and the mean plane is oriented at dihedral angles of 24.47 (11) and 28.86 (13)°, respectively, to the phenyl and thio­phene rings. The thio­phene and phenyl rings make a dihedral angle of 53.21 (8)°. The benzamide fragment and thio­phene ring are located on the opposite sides of the C=N bond, showing an E conformation. Classical inter­molecular N—H⋯O hydrogen bonds and weak C—H⋯O inter­actions are present in the crystal structure: three such bonds occur to the same O-atom acceptor

Cooperative co-evolutionary module identification with application to cancer disease module discovery

none10siModule identification or community detection in complex networks has become increasingly important in many scientific fields because it provides insight into the relationship and interaction between network function and topology. In recent years, module identification algorithms based on stochastic optimization algorithms such as evolutionary algorithms have been demonstrated to be superior to other algorithms on small- to medium-scale networks. However, the scalability and resolution limit (RL) problems of these module identification algorithms have not been fully addressed, which impeded their application to real-world networks. This paper proposes a novel module identification algorithm called cooperative co-evolutionary module identification to address these two problems. The proposed algorithm employs a cooperative co-evolutionary framework to handle large-scale networks. We also incorporate a recursive partitioning scheme into the algorithm to effectively address the RL problem. The performance of our algorithm is evaluated on 12 benchmark complex networks. As a medical application, we apply our algorithm to identify disease modules that differentiate low- and high-grade glioma tumors to gain insights into the molecular mechanisms that underpin the progression of glioma. Experimental results show that the proposed algorithm has a very competitive performance compared with other state-of-the-art module identification algorithms.noneHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao, XHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao,