Dynamics of a Predator-Prey System with a Mate-Finding Allee Effect

We consider a ratio-dependent predator-prey system with a mate-finding Allee effect on prey. The stability properties of the equilibria and a complete bifurcation analysis, including the existence of a saddle-node, a Hopf bifurcation, and, a Bogdanov-Takens bifurcations, have been proved theoretically and numerically. The blow-up method has been applied to investigate the structure of a neighborhood of the origin. Our mathematical results show the mate-finding Allee effect can reduce the complexity of system behaviors by making the complicated equilibrium less complicated, and it can be a destabilizing force as well, which makes the system has a high possibility of being threatened with extinction in ecology

Stability of delayed virus infection model with a general incidence rate and adaptive immune response

We present the dynamical behaviors of a virus infection model with general infection rate, immune responses and two intracellular delays which describe the interactions of the HIV virus, target cells, CTL cells and antibodies within host. Three factors are incorporated in this model: (1) the intrinsic growth rate of uninfected cells, (2) a nonlinear incidence rate function considering both virus-tocell infection and cell-to-cell transmission, and (3) a nonlinear productivity and removal function. By the method of Lyapunov functionals and LaSalle’s invariance principle, we show that the global dynamics of the model is determined by the reproductive numbers for viral infection R0, for antibody immune response R1, for CTL immune response R2, for CTL immune competition R3 and for antibody immune competition R4. The numerical simulations are given to illustrate our theoretical results

Oscillation Criteria of Third-Order Nonlinear Impulsive Differential Equations with Delay

This paper deals with the oscillation of third-order nonlinear impulsive equations with delay. The results in this paper improve and extend some results for the equations without impulses. Some examples are given to illustrate the main results

Variation and inheritance of the Xanthomonas raxX-raxSTAB gene cluster required for activation of XA21-mediated immunity

The rice XA21-mediated immune response is activated on recog-nition of the RaxX peptide produced by the bacterium Xanthomonas oryzae pv. oryzae (Xoo). The 60-residue RaxX pre-cursor is post-translationally modified to form a sulfated tyrosine peptide that shares sequence and functional similarity with the plant sulfated tyrosine (PSY) peptide hormones. The 5-kb raxX-raxSTAB gene cluster of Xoo encodes RaxX, the RaxST tyrosyl-protein sulfotransferase, and the RaxA and RaxB components of a predicted type I secretion system. To assess raxX-raxSTAB gene cluster evolution and to determine its phylogenetic distribution, we first identified rax gene homologues in other genomes. We detected the complete raxX-raxSTAB gene cluster only in Xanthomonas spp., in five distinct lineages in addition to X.ory-zae. The phylogenetic distribution of the raxX-raxSTAB gene cluster is consistent with the occurrence of multiple lateral (hori-zontal) gene transfer events during Xanthomonas speciation. RaxX natural variants contain a restricted set of missense substi-tutions, as expected if selection acts to maintain peptide hor-mone-like function. Indeed, eight RaxX variants tested all failed to activate the XA21-mediated immune response, yet retained peptide hormone activity. Together, these observations support the hypothesis that the XA21 receptor evolved specifically to rec-ognize Xoo RaxX.This study was supported by Public Health Service research grants GM059962 and GM122968 from the National Institute of General Medical Sciences awarded to P.C.R

Microbial signatures of neonatal bacterial meningitis from multiple body sites

As a common central nervous system infection in newborns, neonatal bacterial meningitis (NBM) can seriously affect their health and growth. However, although metagenomic approaches are being applied in clinical diagnostic practice, there are some limitations for whole metagenome sequencing and amplicon sequencing in handling low microbial biomass samples. Through a newly developed ultra-sensitive metagenomic sequencing method named 2bRAD-M, we investigated the microbial signatures of central nervous system infections in neonates admitted to the neonatal intensive care unit. Particularly, we recruited a total of 23 neonates suspected of having NBM and collected their blood, cerebrospinal fluid, and skin samples for 2bRAD-M sequencing. Then we developed a novel decontamination method (Reads Level Decontamination, RLD) for 2bRAD-M by which we efficiently denoised the sequencing data and found some potential biomarkers that have significantly different relative abundance between 12 patients that were diagnosed as NBM and 11 Non-NBM based on their cerebrospinal fluid (CSF) examination results. Specifically, we discovered 11 and 8 potential biomarkers for NBM in blood and CSF separately and further identified 16 and 35 microbial species that highly correlated with the physiological indicators in blood and CSF. Our study not only provide microbiological evidence to aid in the diagnosis of NBM but also demonstrated the application of an ultra-sensitive metagenomic sequencing method in pathogenesis study

Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Background and Aims: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and cheaper than molecular assays. But clinical application of this technology requires high performance and multisite validation, which have not yet been performed. Methods: We collected hematoxylin and eosin-stained slides, and findings from molecular analyses for MSI and dMMR, from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (n=6406 specimens) and validated in an external cohort (n=771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). Results: The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound 0.91, upper bound 0.93) and an AUPRC of 0.63 (range, 0.59–0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC curve of 0.95 (range, 0.92–0.96) without image-preprocessing and an AUROC curve of 0.96 (range, 0.93–0.98) after color normalization. Conclusions: We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using hematoxylin and eosin-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens

Necessary and sufficient conditions for the existence of periodic solutions in a predator-prey model on time scales

This article explores the existence of periodic solutions for non-autonomous impulsive semi-ratio-dependent predator-prey systems on time scales. Based on a continuous theorem in coincidence degree theory, sharp sufficient and necessary conditions are derived in which most popular monotonic, non-monotonic and predator functional responses are applicable. This article extends the work in [6,10,12,13,134,18,25]

How to Carry Out Epidemic Prevention and Control After School Starts with the COVID-19 Epidemic Mitigated? A Case Study of Experimental High School in Wangmo County, Guizhou Province, China

In order to cope with the impact of the COVID-19 epidemic on student learning, the Chinese government has launched a “School’s Out, But Class’s On” strategy. With the further control of the epidemic, schools in some provinces have begun to resume classes, but the epidemic prevention and control work should not be lax after the class is resumed. Guizhou is one of the provinces where the middle schools and senior high schools resumed classes earlier. The epidemic prevention and control is still very critical even the school was resumed. I herein used the Experimental High School in Wangmo County, Guizhou Province as an example to introduce the epidemic prevention and control strategies after the school resumes classes

Fourier ptychographic layer-based imaging of hazy environments

Macroscopic Fourier ptychographic imaging method can significantly improve the resolution of imaging systems. Nevertheless, under far-field complex environment conditions, such as strong scattering medium (e.g.,hazy), the inhomogeneous distribution of the particles inside the hazy cause random interference to the propagation of the photons carrying the target information, resulting in distortion and blurring of the wavefront information. This severely impacts the quality of the final reconstructed image. In this paper, we propose an improved Resnet network to extract and enhance the features from distorted intensity images in hazy environment, and effectively merge information through multi-scale feature fusion. We successfully recover the target intensity information sequences hidden by different concentrations of hazy and even dynamic non-uniform hazy, and present the final Fourier ptychographic reconstruction results. To the best of our knowledge, we are the first to demonstrate the feasibility of far-field Fourier ptychographic imaging through hazy environments using deep learning methods. Experimental results indicate that it can effectively overcome the images blurring and distortion problems caused by the scattering of hazy environment, significantly improving the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) of final macroscopic Fourier ptychographic reconstructed images under different hazy levels. This study not only reveals the great value of deep learning in solving the imaging problems of complex scenes such as dynamic non-uniform strong scattering media, but also effectively suppresses the adverse effect of hazy scattering environment on macroscopic Fourier ptychographic imaging, which greatly promotes the progress and development of the application of the Fourier ptychographic imaging technology in the field of imaging complex environments in the long distance