A broad-host-range lytic phage vB_VhaS-R18L as a candidate against vibriosis

Vibriosis is one of the most common bacterial diseases that cause high rates of mortality and considerable economic losses in aquaculture. Phage therapy has been considered as a promising alternative method to antibiotics in the biocontrol of infectious diseases. Genome sequencing and characterization of the phage candidates are prerequisites before field applications to ensure environmental safety. In this study, a lytic phage, named vB_VhaS-R18L (R18L), was isolated from the coastal seawater of Dongshan Island, China. The phage was characterized in terms of morphology, genetic content, infection kinetics, lytic profile, and virion stability. Transmission electronic microscopy indicated that R18L is siphovirus-like, comprising an icosahedral head (diameter 88.6 ± 2.2 nm) and a long noncontractile tail (225 × 11 nm). Genome analysis indicated R18L to be a double-stranded DNA virus with a genome size of 80,965 bp and a G + C content of 44.96%. No genes that encode known toxins or genes implicated in lysogeny control were found in R18L. A one-step growth experiment showed that R18L had a latent period of approximately 40 min and a burst size of 54 phage particles per infected cell. R18L showed lytic activity against a wide range of at least five Vibrio species (V. alginolyticus, V. cholerae, V. harveyi, V. parahemolyticus, and V. proteolyticus). R18L was relatively stable at pH 6–11 and at temperatures ranging from 4°C to 50°C. The broad lytic activity across Vibrio species and the stability in the environment make R18L a potential candidate for phage therapy in controlling vibriosis in aquaculture systems

Spatiotemporal variations and overflow risk analysis of the Salt Lake in the Hoh Xil Region using machine learning methods

Global warming is inducing dramatic changes in fluvial geomorphology and reshaping the hydrological connections between rivers and lakes. The water level and area of the Salt Lake have increased rapidly since the outburst of the Zonag Lake in the Hoh Xil region of the Qinghai–Tibet Plateau in 2011, threatening the downstream infrastructure. However, fewer studies have focused on its spatiotemporal variation and overflow risk over long time series. Here, we used three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) to extract the area of the Salt Lake for a long time series, analyzed its spatiotemporal variation from 1973 to 2021, and finally assessed the overflow risk. The Kappa coefficient (KAPPA) and the overall accuracy (OA) were used to evaluate the performance of the models. The results showed that Random Forest performs superior in lake extraction (KAPPA = 0.98, overall accuracy = 0.99), followed by Classification and Regression Trees and Support Vector Machine. normalized difference water index is the relatively important feature variable in both RF and CART. Before the outburst event, the area change of the Salt Lake was consistent with the variation in precipitation; after that, it showed a remarkable area increase (circa 350%) in all orientations, and the main direction was the southeast. Without the construction of the emergency drainage channel, the simulation result indicated that the earliest and latest times of the Salt Lake overflow event are predicted to occur in 2020 and 2031, respectively. The results of this paper not only demonstrate that RF is more suitable for water extraction and help understand the water system reorganization event

A newly isolated roseophage represents a distinct member of Siphoviridae family.

BACKGROUND(#br)Members of the Roseobacter lineage are a major group of marine heterotrophic bacteria because of their wide distribution, versatile lifestyles and important biogeochemical roles. Bacteriophages, the most abundant biological entities in the ocean, play important roles in shaping their hosts’ population structures and mediating genetic exchange between hosts. However, our knowledge of roseophages (bacteriophages that infect Roseobacter) is far behind that of their host counterparts, partly reflecting the need to isolate and analyze the phages associated with this ecologically important bacterial clade.(#br)METHODS(#br)vB_DshS-R4C (R4C), a novel virulent roseophage that infects Dinoroseobacter shibae DFL12T, was isolated with the double-layer agar method. The phage morphology was visualized with transmission electron microscopy. We characterized R4C in-depth with a genomic analysis and investigated the distribution of the R4C genome in different environments with a metagenomic recruitment analysis.(#br)RESULTS(#br)The double-stranded DNA genome of R4C consists of 36,291 bp with a high GC content of 66.75%. It has 49 genes with low DNA and protein homologies to those of other known phages. Morphological and phylogenetic analyses suggested that R4C is a novel member of the family Siphoviridae and is most closely related to phages in the genus Cronusvirus. However, unlike the Cronusvirus phages, R4C encodes an integrase, implying its ability to establish a lysogenic life cycle. A terminal analysis shows that, like that of λ phage, the R4C genome utilize the ’cohesive ends’ DNA-packaging mechanism. Significantly, homologues of the R4C genes are more prevalent in coastal areas than in the open ocean.(#br)CONCLUSIONS(#br)Information about this newly discovered phage extends our understanding of bacteriophage diversity, evolution, and their roles in different environments

Impacts of Freshwater and Seawater Mixing on the Production and Decay of Virioplankton in a Subtropical Estuary

Abstract(#br)Virioplankton is an important component of the aquatic ecosystem and plays multiple ecological and biogeochemical roles. Although the spatial and temporal distributions and dynamics of virioplankton have been well investigated in riverine and marine environments, little is known about the dynamics and environmental controlling mechanisms of virioplankton in estuaries. In this study, viral abundance, production and decay were examined in the Pearl River Estuary (PRE), one of the largest estuaries in China. The influences of freshwater and seawater mixing on viral ecological dynamics were evaluated with several cross-transplant experiments. In PRE, viral abundance, production and decay rates varied from 2.72 ± 0.09 to 27.5 ± 1.07 × 10 6 viruses ml −1 , 7.98 ± 2.33 to 16.27 ±..

Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate

Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years

Viruses are ubiquitous and cause significant mortality in marine bacterial and archaeal communities. Little is known about the role of viruses in the sub-seafloor biosphere, which hosts a large fraction of all microbes on Earth. We quantified and characterized viruses in sediments from the Baltic Sea. The results show that the Baltic Sea sub-seafloor biosphere harbors highly abundant viruses with densities up to 1.8 × 1010 viruses cm−3. High potential viral production down to 37 meters below seafloor in ca. 6000-years-old sediments and infected prokaryotic cells visible by transmission electron microscopy demonstrate active viral infection. Morphological and molecular data indicate that the highly diverse community of viruses includes both allochthonous input from the overlying seawater and autochthonous production. The detection of cyanophage-like sequences showed that viruses of phototrophic hosts may persist in marine sediments for thousands of years. Our results imply that viruses influence sub-seafloor microbial community dynamics and thereby affect biogeochemical processes in the sub-seafloor biosphere

Disentangling the effects of vapor pressure deficit on northern terrestrial vegetation productivity

The impact of atmospheric vapor pressure deficit (VPD) on plant photosynthesis has long been acknowledged, but large interactions with air temperature (T) and soil moisture (SM) still hinder a complete understanding of the influence of VPD on vegetation production across various climate zones. Here, we found a diverging response of productivity to VPD in the Northern Hemisphere by excluding interactive effects of VPD with T and SM. The interactions between VPD and T/SM not only offset the potential positive impact of warming on vegetation productivity but also amplifies the negative effect of soil drying. Notably, for high-latitude ecosystems, there occurs a pronounced shift in vegetation productivity\u27s response to VPD during the growing season when VPD surpasses a threshold of 3.5 to 4.0 hectopascals. These results yield previously unknown insights into the role of VPD in terrestrial ecosystems and enhance our comprehension of the terrestrial carbon cycle\u27s response to global warming

Herpes Simplex Virus Type 2 Infection-Induced Expression of CXCR3 Ligands Promotes CD4(+) T Cell Migration and Is Regulated by the Viral Immediate-Early Protein ICP4

HSV-2 infection-induced CXCR3 ligands are important for the recruitment of virus-specific CD8+ T cells, but their impact on CD4+ T cell trafficking remains to be further determined. Given that recruitment of CD4+ T cells to infection areas may be one of the mechanisms that account for HSV-2 infection-mediated enhancement of HIV-1 sexual transmission, here we investigated the functionality of HSV-2 infection-induced CXCR3 ligands CXCL9, CXCL10, and CXCL11 in vivo and in vitro, and determined the viral components responsive for such induction and the underlying mechanisms. We first found that the expression of CXCR3 ligands CXCL9, CXCL10, and CXCL11 was increased in mice following vaginal challenge with HSV-2, while CXCL9 played a predominant role in the recruitment of CD4+ T cells to the vaginal foci of infected mice. HSV-2 infection also induced the production of CXCL9, CXCL10, and CXCL11 in human cervical epithelial cells. Of note, although HSV-2 induced the expression of all the three CXCR3 ligands, the induced CXCL9 appeared to play a predominant role in promoting CD4+ T cell migration, reflecting that the concentrations of CXCL10 and CXCL11 required for CD4+ T cell migration are higher than that of CXCL9. We further revealed that, ICP4, an immediate-early protein of HSV-2, is crucial in promoting CXCR3 ligand expression through the activation of p38 MAPK pathway. Mechanistically, ICP4 binds to corresponding promoters of CXCR3 ligands via interacting with the TATA binding protein (TBP), resulting in the transcriptional activation of the corresponding promoters. Taken together, our study highlights HSV-2 ICP4 as a vital viral protein in promoting CXCR3 ligand expression and CXCL9 as the key induced chemokine in mediating CD4+ T cell migration. Findings in this study have shed light on HSV-2 induced leukocyte recruitment which may be important for understanding HSV-2 infection-enhanced HIV-1 sexual transmission and the development of intervention strategies

Semisupervised Graph Neural Networks for Traffic Classification in Edge Networks

Edge networking brings computation and data storage as close to the point of request as possible. Various intelligent devices are connected to the edge nodes where traffic packets flow. Traffic classification tasks are thought to be a keystone for network management; researchers can analyze packets captured to understand the traffic as it hits their network. However, the existing traffic classification framework needs to conduct a unified analysis, which leads to the huge bandwidth resources required in the process of transferring all captured packet files to train a global classifier. In this paper, a semisupervised graph neural network traffic classifier is proposed for cloud-edge architecture so that cloud servers and edge nodes could cooperate to perform the traffic classification tasks in order to deliver low latency and save bandwidth on the edge nodes. To preserve the structural information and interrelationships conveyed in packets within a session, we transform traffic sessions into graphs. We segment the frequently combined consecutive packets into granules, which are later transformed into the nodes in graphs. Edges could extract the adjacency of the granules in the sessions; the edge node side then selects the highly representative samples and sends them to the cloud server; the server side uses graph neural networks to perform semisupervised classification tasks on the selected training set. Our method has been trained and tested on several datasets, such as the VPN-nonVPN dataset, and the experimental results show good performance on accuracy, recall, and F-score