Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification.

With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial in Re-ID. Consequently, a Visible-Infrared Person Re-Identification (VI Re-ID) task is proposed, which aims to match pairs of person images from the visible and infrared modalities. The significant modality discrepancy between the modalities poses a major challenge. Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. Contours exhibit modality invariance, which is vital for learning effective identity representations and cross-modal matching. In addition, due to the low intra-modal diversity in the visible modality, it is difficult to distinguish the boundaries between some hard samples. To address these issues, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN). Firstly, the Contour Expansion Module (CEM) incorporates the contour information of a person into the original samples, further reducing the modality discrepancy and leading to improved matching stability between image pairs of different modalities. Additionally, to better distinguish cross-modal hard sample pairs during the training process, an innovative Cross-modality Graph Sampler (CGS) is designed for sample selection before training. The CGS calculates the feature distance between samples from different modalities and groups similar samples into the same batch during the training process, effectively exploring the boundary relationships between hard classes in the cross-modal setting. Some experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed method. Specifically, in the VIS→IR task, the experimental results on the RegDB dataset achieve 93.69% for Rank-1 and 92.56% for mAP.

CLIP-Based Adaptive Graph Attention Network for Large-Scale Unsupervised Multi-Modal Hashing Retrieval.

With the proliferation of multi-modal data generated by various sensors, unsupervised multi-modal hashing retrieval has been extensively studied due to its advantages in storage, retrieval efficiency, and label independence. However, there are still two obstacles to existing unsupervised methods: (1) As existing methods cannot fully capture the complementary and co-occurrence information of multi-modal data, existing methods suffer from inaccurate similarity measures. (2) Existing methods suffer from unbalanced multi-modal learning and data semantic structure being corrupted in the process of hash codes binarization. To address these obstacles, we devise an effective CLIP-based Adaptive Graph Attention Network (CAGAN) for large-scale unsupervised multi-modal hashing retrieval. Firstly, we use the multi-modal model CLIP to extract fine-grained semantic features, mine similar information from different perspectives of multi-modal data and perform similarity fusion and enhancement. In addition, this paper proposes an adaptive graph attention network to assist the learning of hash codes, which uses an attention mechanism to learn adaptive graph similarity across modalities. It further aggregates the intrinsic neighborhood information of neighboring data nodes through a graph convolutional network to generate more discriminative hash codes. Finally, this paper employs an iterative approximate optimization strategy to mitigate the information loss in the binarization process. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms several representative hashing methods in unsupervised multi-modal retrieval tasks.

Selection and characterization of bispecific aptamers against malachite green and leucomalachite green.

In order to develop multi-residues rapid detection, the bispecific aptamers against malachite green (MG) and leucomalachite green (LMG) were isolated by the capture systematic evolution of ligands by exponential enrichment (Capture-SELEX). After thirteen rounds of selection, the enriched ssDNA pools were sent for high-throughput sequencing. Nine aptamer candidates (A1-A9) were picked out to test their specificity by gold nanoparticles (AuNPs) colorimetric assay. Three aptamers (A2, A3, A5) with good selectivity were truncated to verify their affinity by fluorescence assay. Finally, three truncated aptamers (A2-a, A3-a, A5-a) with bispecificity and high affinity were identified. For LMG, the dissociation constant (Kd) of them were 8.4 ± 0.8 nM, 8.2 ± 1.2 nM, and 13.7 ± 1.4 nM, respectively. For MG, Kd of them were 3.4 ± 0.3 µM, 2.3 ± 0.2 µM, 3.0 ± 0.2 µM. Among them, A3-a is the best. Our work will provide novel probes for the development of multi-residues rapid detection as well as opportunities for multiple target aptamer discovery.

Hierarchical Predictive Coding-Based JND Estimation for Image Compression.

The human visual system (HVS) is a hierarchical system, in which visual signals are processed hierarchically. In this paper, the HVS is modeled as a three-level communication system and visual perception is divided into three stages according to the hierarchical predictive coding theory. Then, a novel just noticeable distortion (JND) estimation scheme is proposed. In visual perception, the input signals are predicted constantly and spontaneously in each hierarchy, and neural response is evoked by the central residue and inhibited by surrounding residues. These two types' residues are regarded as the positive and negative visual incentives which cause positive and negative perception effects, respectively. In neuroscience, the effect of incentive on observer is measured by the surprise of this incentive. Thus, we propose a surprise-based measurement method to measure both perception effects. Specifically, considering the biased competition of visual attention, we define the product of the residue self-information (i.e., surprise) and the competition biases as the perceptual surprise to measure the positive perception effect. As for the negative perception effect, it is measured by the average surprise (i.e., the local Shannon entropy). The JND threshold of each stage is estimated individually by considering both perception effects. The total JND threshold is finally obtained by non-linear superposition of three stage thresholds. Furthermore, the proposed JND estimation scheme is incorporated into the codec of Versatile Video Coding for image compression. Experimental results show that the proposed JND model outperforms the relevant existing ones, and over 16% of bit rate can be reduced without jeopardizing the perceptual quality.

Antiviral effect of copper chloride on feline calicivirus and synergy with ribavirin in vitro.

BACKGROUND: Feline calicivirus (FCV) is a common and highly prevalent pathogen causing upper respiratory diseases in kittens and felines in recent years. Due to the substantial genetic variability of the viral genes, existing vaccines cannot provide complete protection. Therefore, research on FCV antiviral drugs has received much attention. RESULTS: In this study, we found that copper chloride had dose-dependent antiviral effects on FCV in F81 cells. We also found that the combination of copper chloride and ribavirin had a synergistic protective effect against FCV in F81 cells. In contrast, the combination of copper chloride and horse anti-FCV immunoglobulin F (ab')2 showed an antagonistic effect, likely because copper chloride has an effect on F (ab')2 immunoglobulin; however, further research is needed to clarify this supposition. CONCLUSIONS: In summary, we found that copper chloride had low cytotoxicity and significant antiviral effects on FCV in F81 cells, providing a new drug candidate for the prevention and treatment of FCV infection.

Ψ-Net: Focusing on the border areas of intracerebral hemorrhage on CT images.

BACKGROUND AND OBJECTIVE: The volume of the intracerebral hemorrhage (ICH) obtained from CT scans is essential for quantification and treatment planning. However,a fast and accurate volume acquisition brings great challenges. On the one hand, it is both time consuming and operator dependent for manual segmentation, which is the gold standard for volume estimation. On the other hand, low contrast to normal tissues, irregular shapes and distributions of the hemorrhage make the existing automatic segmentation methods hard to achieve satisfactory performance. METHOD: To solve above problems, a CNN-based architecture is proposed in this work, consisting of a novel model, which is named as Ψ-Net and a multi-level training strategy. In the structure of Ψ-Net, a self-attention block and a contextual-attention block is designed to suppresses the irrelevant information and segment border areas of the hemorrhage more finely. Further, an multi-level training strategy is put forward to facilitate the training process. By adding the slice-level learning and a weighted loss, the multi-level training strategy effectively alleviates the problems of vanishing gradient and the class imbalance. The proposed training strategy could be applied to most of the segmentation networks, especially for complex models and on small datasets. RESULTS: The proposed architecture is evaluated on a spontaneous ICH dataset and a traumatic ICH dataset. Compared to the previous works on the ICH sementation, the proposed architecture obtains the state-of-the-art performance(Dice of 0.950) on the spontaneous ICH, and comparable results(Dice of 0.895) with the best method on the traumatic ICH. On the other hand, the time consumption of the proposed architecture is much less than the previous methods on both training and inference. Morever, experiment results on various of models prove the universality of the multi-level training strategy. CONCLUSIONS: This study proposed a novel CNN-based architecture, Ψ-Net with multi-level training strategy. It takes less time for training and achives superior performance than previous ICH segmentaion methods.

[Simultaneous and rapid determination of malachite green and leucomalachite green by a label-free colorimetric aptasensor].

A label-free colorimetric aptasensor, using a bispecific aptamer (A3) as a sensing probe, gold nanoparticles (AuNPs) as an indicator, and NaCl solution as an aggregation inducer, was successfully developed for the simultaneous, rapid and visual detection of malachite green (MG) and leucomalachite green (LMG) in aquatic products. This method is based on the aptamer A3 having bispecific binding ability with MG and LMG, making it an ideal recognition receptor for MG and LMG. It can adsorb on the AuNPs and protect AuNPs against salt-induced aggregation, maintaining the red color of the solution. When MG or LMG was added to a solution, aggregation of AuNPs was specifically induced by desorption of aptamer from the AuNPs surface upon formation of the aptamer-target complex. Therefore, the salt could trigger aggregation of AuNPs and the solution color was changed from red to blue. This color change allowed the qualitative determination of MG and LMG visually, and quantitative determination by measuring the ratio of the absorbances at 520 nm and 650 nm. In this study, 50 µL of the nucleic acid aptamer A3 (final concentrations 150 nmol/L) and 150 µL of AuNPs (final concentrations 1.25 nmol/L) were incubated at room temperature (RT) for 6 min, then 50 µL of the sample was added and incubated at RT for 30 min, and finally 50 µL NaCl solution (final concentrations 150 mmol/L) was added. After 4 min, the solution color change was observed, and the absorbances at 520 nm and 650 nm were measured. Under the optimal conditions, MG and LMG could be detected specifically without any cross-reactivity with sulfadiazine (SDZ) and nitrofurantoin (NFT). The absorbance were related to the concentrations of MG and LMG, and a good linear relationship was obtained in the range of 0-17.5 µmol/L. The correlation coefficients (R2) were 0.9938 and 0.9715, respectively. The limits of detection of MG and LMG were 6.93 nmol/L and 6.38 nmol/L, respectively. The spiked recoveries of MG and LMG ranged from 88.60% to 93.30% and 101.80% to 107.00%, respectively. The relative standard deviations (RSDs) of MG and LMG ranged from 2.27% to 3.55% and 2.62% to 3.75%, respectively. This colorimetric method is simple, rapid, sensitive, and allows visual, and it can provide a new method for the simultaneous and rapid determination of the MG and LMG in aquatic products.

The POLR2E rs3787016 polymorphism is strongly associated with the risk of female breast and cervical cancer.

The rs3787016 polymorphism, in polymerase II polypeptide E (POLR2E), was previously identified as being associated with the risk for prostate cancer, esophageal cancer, breast cancer, papillary thyroid carcinoma and liver cancer, suggesting that rs3787016 may server as a common genetic factor to affect individual susceptibility to cancer. To prove the hypothesis, we here performed a case-control study to explore the association between rs3787016 and cervical cancer risk, and to confirm the association between rs3787016 and breast cancer in a central Chinese population, which was followed by a meta-analysis to precisely estimate the association between rs3787016 and risk of female breast and cervical cancer. The genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and confirmed by sequencing. Our results indicated that rs3787016 was associated with the risk of both breast cancer and cervical cancer, and stratified analysis indicated that the association remained particularly for ≤60 years old females who smoke and drink. Moreover, after grouping breast cancer and cervical cancer together, our meta-analysis demonstrated that rs3787016 was associated with overall cancer risk and breast cancer risk. Collectively, the POLR2E rs3787016 polymorphism may be a valuable biomarker for female breast and cervical cancer predisposition.

Canine distemper virus isolated from a monkey efficiently replicates on Vero cells expressing non-human primate SLAM receptors but not human SLAM receptor.

BACKGROUND: In 2008, an outbreak of canine distemper virus (CDV) infection in monkeys was reported in China. We isolated CDV strain (subsequently named Monkey-BJ01-DV) from lung tissue obtained from a rhesus monkey that died in this outbreak. We evaluated the ability of this virus on Vero cells expressing SLAM receptors from dog, monkey and human origin, and analyzed the H gene of Monkey-BJ01-DV with other strains. RESULTS: The Monkey-BJ01-DV isolate replicated to the highest titer on Vero cells expressing dog-origin SLAM (10(5.2±0.2) TCID50/ml) and monkey-origin SLAM (10(5.4±0.1) TCID50/ml), but achieved markedly lower titers on human-origin SLAM cells (10(3.3±0.3) TCID50/ml). Phylogenetic analysis of the full-length H gene showed that Monkey-BJ01-DV was highly related to other CDV strains obtained during recent CDV epidemics among species of the Canidae family in China, and these Monkey strains CDV (Monkey-BJ01-DV, CYN07-dV, Monkey-KM-01) possessed a number of amino acid specific substitutions (E276V, Q392R, D435Y and I542F) compared to the H protein of CDV epidemic in other animals at the same period. CONCLUSIONS: Our results suggested that the monkey origin-CDV-H protein could possess specific substitutions to adapt to the new host. Monkey-BJ01-DV can efficiently use monkey- and dog-origin SLAM to infect and replicate in host cells, but further adaptation may be required for efficient replication in host cells expressing the human SLAM receptor.

Genetic characterization of an isolate of canine distemper virus from a Tibetan Mastiff in China.

Canine distemper (CD) is a highly contagious, often fatal, multisystemic, and incurable disease in dogs and other carnivores, which is caused by canine distemper virus (CDV). Although vaccines have been used as the principal means of controlling the disease, CD has been reported in vaccinated animals. The hemoagglutinin (H) protein is one of the most important antigens for inducing protective immunity against CD, and antigenic variation of recent CDV strains may explain vaccination failure. In this study, a new CDV isolate (TM-CC) was obtained from a Tibetan Mastiff that died of distemper, and its genome was characterized. Phylogenetic analysis of the H gene revealed that the CDV-TM-CC strain is unique among 20 other CDV strains and can be classified into the Asia-1 group with the Chinese strains, Hebei and HLJ1-06, and the Japanese strain, CYN07-hV. The H gene of CDV-TM-CC shows low identity (90.4 % nt and 88.9 % aa) with the H gene of the classical Onderstepoort vaccine strain, which may explain the inability of the Tibetan Mastiff to mount a protective immune response. We also performed a comprehensive phylogenetic analysis of the N, P, and F protein sequences, as well as potential N-glycosylation sites and cysteine residues. This analysis shows that an N-glycosylation site at aa 108-110 within the F protein of CDV-TM-CC is specific for the wild-type strains (5804P, A75/17, and 164071) and the Asia-1 group strains, and may be another important factor for the poor immune response. These results provide important information for the design of CD vaccines in the China region and elsewhere.

Construction and immunogenicity of a recombinant pseudotype baculovirus expressing the glycoprotein of rabies virus in mice.

A pseudotype baculovirus with the glycoprotein of vesicular stomatitis virus (VSV-G) on the envelope was used as a vector for the construction of recombinant baculovirus expressing the G protein of rabies virus (RABV) under the cytomegalovirus (CMV) promoter. The generated recombinant baculovirus (BV-G) efficiently expressed the RABV G proteins in mammalian cells. Intramuscular vaccination with BV-G (10(9) PFU/mouse) induced the production of RABV G-specific neutralizing antibodies and strong T cell responses in mice. Our data clearly indicate that pseudotype baculovirus-mediated gene delivery can be utilized as an alternative strategy to develop a new generation of vaccine against RABV infection.