Plant immunity and beyond: Signals from proteins & peptides

Plants are the primary and most important source of food for human consumption, besides their ecological importance, since they define the diverse ecosystems worldwide. Because of their position in the ecological chain as primary supplier of biomass and food for other organisms, plants are also fundamental for animals, fungi and microorganisms, and some of them are also beneficial to other plants. Humans rely mostly on plant products for food, and many plants provide important non-food products, including wood, textiles, medicines, cosmetics, soaps, rubber, plastics, paints and other industrial chemicals. Moreover, plants are also fundamental for animal feeding, including not only mammals (e.g. cattle, sheep and goats), but also poultry and aquaculture (e.g., fish and shrimp farming)

Going the distance for protein function prediction: a new distance metric for protein interaction networks

Due to an error introduced in the production process, the x-axes in the first panels of Figure 1 and Figure 7 are not formatted correctly. The correct Figure 1 can be viewed here: http://dx.doi.org/10.1371/annotation/343bf260-f6ff-48a2-93b2-3cc79af518a9In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.MC, HZ, NMD and LJC were supported in part by National Institutes of Health (NIH) R01 grant GM080330. JP was supported in part by NIH grant R01 HD058880. This material is based upon work supported by the National Science Foundation under grant numbers CNS-0905565, CNS-1018266, CNS-1012910, and CNS-1117039, and supported by the Army Research Office under grant W911NF-11-1-0227 (to MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A multi-species functional embedding integrating sequence and network structure

A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment. In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HANDL (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HANDL embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in HANDL space, and the direction of separation is conserved across species. Software for the HANDL algorithm is available at http://bit.ly/lrgr-handl.Published versio

A review of research on tourism industry, economic crisis and mitigation process of the loss: Analysis on pre, during and post pandemic situation

Throughout time, the global tourism industry and economy have been significantly affected by disasters and crises. At present, COVID-19 represents one of these disasters as it has been causing a serious economic downturn with huge implications in tourism. In this review paper, we have analysed more than 100 papers regarding the effect and consequences of a pandemic on tourism and related industries, the economic situation in countries and areas, and mitigation of the loss incurred due to pandemic situations. The article (1) is based on past research on tourism and economy, (2) examines the effects of a pandemic on listed sectors and mitigation processes, and (3) suggests future research and approaches to help progress the field. We have gathered and categorised the literature reviews into several parts. In addition, we have listed the name of authors, journal names, books, websites, and relevant data

SRPT Scheduling for Web Servers

This note briey summarizes some results from two papers: [4] and [23]. These papers pose the following question: Is it possible to reduce the expected response time of every request at a web server, simply by changing the order in which we schedule the requests? In [4] we approach this question analytically via an M/G/1 queue. In [23] we approach the same question via implementation involving an Apache web server running on Linux

Runtime Distributions and Criteria for Restarts

Randomized algorithms sometimes employ a restart strategy. After a certain number of steps, the current computation is aborted and restarted with a new, independent random seed. In some cases, this results in an improved overall expected runtime. This work introduces properties of the underlying runtime distribution which determine whether restarts are advantageous. The most commonly used probability distributions admit the use of a scale and a location parameter. Location parameters shift the density function to the right, while scale parameters affect the spread of the distribution. It is shown that for all distributions scale parameters do not influence the usefulness of restarts and that location parameters only have a limited influence. This result simplifies the analysis of the usefulness of restarts. The most important runtime probability distributions are the log-normal, the Weibull, and the Pareto distribution. In this work, these distributions are analyzed for the usefulness of restarts. Secondly, a condition for the optimal restart time (if it exists) is provided. The log-normal, the Weibull, and the generalized Pareto distribution are analyzed in this respect. Moreover, it is shown that the optimal restart time is also not influenced by scale parameters and that the influence of location parameters is only linear

Unusual onset of a case of chronic recurrent multifocal osteomyelitis

Background: Chronic recurrent multifocal osteomyelitis (CRMO) is a rare condition that commonly affects the clavicle and pelvis. Case presentation: We report here a case a 12 years old girl with CRMO arising with recurrent episodes of left supraorbital headache, followed by the appearance of a periorbital dyschromia. Magnetic resonance imaging (MRI) of the skull and orbits revealed an important subacute inflammatory process. Few months after, the child presented a painful swelling of the left clavicle; the histological examination of the related biopsy allowed to establish the diagnosis of CRMO. Conclusion: CRMO presenting as acute headache involving neurocranium is rare; to our knowledge this is the first recognized case in the world literature. This pathological condition is frequently misdiagnosed as infection or neoplasm and needs a deep investigation for the differential diagnosis. The physical, laboratoristic and instrumental diagnostic investigations of the patient and the treatment employed are described in detail

Inflammasome activation by NLRP1 and NLRC4 in patients with coronary stenosis

Objective and design: We performed an experimental, analytical and prospective study to evaluate the systemic activation of inflammasome in atherosclerosis\u2019 patients, in order to shed light into responsible mechanisms for plaque formation. Subjects: We included sixty individuals distributed into 3 groups: 2 groups based on the report from the angiography (severe lesions - SL and primary lesions - PL) and 1 group enclosing healthy individuals (HC). Methods: The expression assays of inflammasome genes NLRP1, NLRC4, CASP-1 and IL-1\u3b2 were performed using Real Time qPCR, with specific Taqman Assays. IL-1\u3b2 serum levels were analysed by commercial kit. Were applied the Shapiro-Wilk and Student's T-test as statistical tests. Statistical significance was set to p 64 0.05. Results: Upregulation of NLRP1 (+3.47 FC, p = 0.0001), NLRC4 (+7.06 FC, p = 6.792 7 10 1209) and IL-1\u3b2 (+2.43 FC, p = 0.005) was observed in all atherosclerosis patients when compared to HC. According to stenosis severity, patients with primary lesions showed upregulation of inflammasome genes NLRP1 (+2.87 FC, p = 0.0008), NLRC4 (+6.34 FC, p = 4.134 7 10-07) and IL-1\u3b2 (+3.39 FC, p = 0.0012) with respect to the HC group. No statistical difference was found in IL-1\u3b2 serum levels according the assessed groups. Conclusions: Inflammasome activation in atherosclerosis's patients can be systemic altered and may be triggered by NLRP1 and NLRC4 receptors. IL-1\u3b2 gene expression was identified in our study as an important systemic detectable marker of plaque severity

Optimization of Anti-SARS-CoV-2 Treatments Based on Curcumin, Used Alone or Employed as a Photosensitizer.

Curcumin, the bioactive compound of the spice Curcuma longa, has already been reported as a potential COVID-19 adjuvant treatment due to its immunomodulatory and anti-inflammatory properties. In this study, SARS-CoV-2 was challenged with curcumin; moreover, curcumin was also coupled with laser light at 445 nm in a photodynamic therapy approach. Curcumin at a concentration of 10 μM, delivered to the virus prior to inoculation on cell culture, inhibited SARS-CoV-2 replication (reduction >99%) in Vero E6 cells, possibly due to disruption of the virion structure, as observed using the RNase protection assay. However, curcumin was not effective as a prophylactic treatment on already-infected Vero E6 cells. Notably, when curcumin was employed as a photosensitizer and blue laser light at 445 nm was delivered to a mix of curcumin/virus prior to the inoculation on the cells, virus inactivation was observed (>99%) using doses of curcumin that were not antiviral by themselves. Photodynamic therapy employing crude curcumin can be suggested as an antiviral option against SARS-CoV-2 infection