Color Image Segmentation Based on Modified Kuramoto Model

AbstractA new approach for color image segmentation is proposed based on Kuramoto model in this paper. Firstly, the classic Kuramoto model which describes a global coupled oscillator network is changed to be one that is locally coupled to simulate the neuron activity in visual cortex and to describe the influence for phase changing by external stimuli. Secondly, a rebuilt method of coupled neuron activities is proposed by introducing and computing instantaneous frequency. Three oscillating curves corresponding to the pixel values of R, G, B for color image are formed by the coupled network and are added up to produce the superposition of oscillation. Finally, color images are segmented according to the synchronization of the oscillating superposition by extracting and checking the frequency of the oscillating curves. The performance is compared with that from other representative segmentation approaches

Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI), to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications

New Global Synchronization Analysis for Complex Networks with Coupling Delay

Global synchronization analysis for complex networks with coupling delay is investigated. Firstly the constant time delay is analyzed and then the case for time-varying delay is considered. Sufficient conditions for network synchronization are given based on Lyapunov functional, linear matrix inequality, and Kronecker product technique. The unknown variables in the sufficient conditions are fewer than those in the recent reference. Moreover, for the time-varying delay case, we find that the conditions are dependent on the bounds of both time delay and its derivative, and the derivative of the time-varying delay can be any value in the bounds. Finally, numerical examples are given to validate the effectiveness of the obtained results

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics

Strategies to improve the therapeutic efficacy of mesenchymal stem cell‐derived extracellular vesicle (MSC-EV): a promising cell-free therapy for liver disease

Liver disease has emerged as a significant worldwide health challenge due to its diverse causative factors and therapeutic complexities. The majority of liver diseases ultimately progress to end-stage liver disease and liver transplantation remains the only effective therapy with the limitations of donor organ shortage, lifelong immunosuppressants and expensive treatment costs. Numerous pre-clinical studies have revealed that extracellular vesicles released by mesenchymal stem cells (MSC-EV) exhibited considerable potential in treating liver diseases. Although natural MSC-EV has many potential advantages, some characteristics of MSC-EV, such as heterogeneity, uneven therapeutic effect, and rapid clearance in vivo constrain its clinical translation. In recent years, researchers have explored plenty of ways to improve the therapeutic efficacy and rotation rate of MSC-EV in the treatment of liver disease. In this review, we summarized current strategies to enhance the therapeutic potency of MSC-EV, mainly including optimization culture conditions in MSC or modifications of MSC-EV, aiming to facilitate the development and clinical application of MSC-EV in treating liver disease

SY18ΔL60L: a new recombinant live attenuated African swine fever virus with protection against homologous challenge

IntroductionAfrican swine fever (ASF) is an acute and highly contagious disease and its pathogen, the African swine fever virus (ASFV), threatens the global pig industry. At present, management of ASF epidemic mainly relies on biological prevention and control methods. Moreover, due to the large genome of ASFV, only half of its genes have been characterized in terms of function.MethodsHere, we evaluated a previously uncharacterized viral gene, L60L. To assess the function of this gene, we constructed a deletion strain (SY18ΔL60L) by knocking out the L60L gene of the SY18 strain. To evaluate the growth characteristics and safety of the SY18ΔL60L, experiments were conducted on primary macrophages and pigs, respectively.ResultsThe results revealed that the growth trend of the recombinant strain was slower than that of the parent strain in vitro. Additionally, 3/5 (60%) pigs intramuscularly immunized with a 105 50% tissue culture infectious dose (TCID50) of SY18ΔL60L survived the 21-day observation period. The surviving pigs were able to protect against the homologous lethal strain SY18 and survive. Importantly, there were no obvious clinical symptoms or viremia.DiscussionThese results suggest that L60L could serve as a virulence- and replication-related gene. Moreover, the SY18ΔL60L strain represents a new recombinant live-attenuated ASFV that can be employed in the development of additional candidate vaccine strains and in the elucidation of the mechanisms associated with ASF infection

Enzymatic Redesigning of Biologically Active Heparan Sulfate

Heparan sulfate carries a wide range of biological activities, regulating blood coagulation, cell differentiation, and inflammatory responses. The sulfation patterns of the polysaccharide are essential for the biological activities. In this study, we report an enzymatic method for the sulfation of multimilligram amounts of heparan sulfate with specific functions using immobilized sulfotransferases combined with a 3′-phosphoadenosine 5′-phosphosulfate regeneration system. By selecting appropriate enzymatic modification steps, an inactive precursor has been converted to the heparan sulfate having three distinct biological activities, associated with binding to antithrombin, fibroblast growth factor-2, and herpes simplex virus envelope glycoprotein D. Because the recombinant sulfotransferases are expressed in bacteria, and the method uses a low cost sulfo donor, it can be readily utilized to synthesize large quantities of anticoagulant heparin drug or other biologically active heparan sulfates

Rapid and Sensitive Recombinase Polymerase Amplification Combined With Lateral Flow Strip for Detecting African Swine Fever Virus

African swine fever virus (ASFV), the etiological agent of African swine fever (ASF), a hemorrhagic fever of domestic pigs, has devastating consequences for the pig farming industry. More than 1,000,000 pigs have been slaughtered since 3 August 2018 in China. However, vaccines or drugs for ASF have yet to be developed. As such, a rapid test that can accurately detect ASFV on-site is important to the timely implementation of control measures. In this study, we developed a rapid test that combines recombinase polymerase amplification (RPA) of the ASFV p72 gene with lateral flow detection (LFD). Results showed that the sensitivity of recombinase polymerase amplification with lateral flow dipstick (RPA-LFD) for ASFV was 150 copies per reaction within 10 min at 38°C. The assay was highly specific to ASFV and had no cross-reactions with other porcine viruses, including classical swine fever virus (CSFV). A total of 145 field samples were examined using our method, and the agreement of the positive rate between RPA-LFD (10/145) and real-time PCR (10/145) was 100%. Overall, RPA-LFD provides a novel alternative for the simple, sensitive, and specific identification of ASFV and showed potential for on-site ASFV detection

Identification of DYNLT1 associated with proliferation, relapse, and metastasis in breast cancer

BackgroundBreast cancer (BC) is the most common malignant disease worldwide. Although the survival rate is improved in recent years, the prognosis is still bleak once recurrence and metastasis occur. It is vital to investigate more efficient biomarkers for predicting the metastasis and relapse of BC. DYNLT1 has been reported that participating in the progression of multiple cancers. However, there is still a lack of study about the correlation between DYNLT1 and BC.MethodsIn this study, we evaluated and validated the expression pattern and prognostic implication of DYNLT1 in BC with multiple public cohorts and BC tumor microarrays (TMAs) of paraffin-embedded tissues collected from the Affiliated Hospital of Jining Medical University. The response biomarkers for immune therapy, such as tumor mutational burden (TMB), between different DYNLT1 expression level BC samples were investigated using data from the TCGA-BRCA cohort utilizing public online tools. In addition, colony formation and transwell assay were conducted to verify the effects of DYNLT1 in BC cell line proliferation and invasion.ResultsThe results demonstrated that DYNLT1 overexpressed in BC and predicted poor relapse-free survival in our own BC TMA cohort. In addition, DYNLT1 induced BC development by promoting MDA-MB-231 cell proliferation migration, and metastasis.ConclusionAltogether, our findings proposed that DYNLT1 could be a diagnostic and prognostic indicator in BC