A Psychometric Investigation of the Young Adult Social Behavior Scale (YASB)

Aggressive behavior is a serious public health concern that has resulted in several problems in contemporary society. Despite a considerable body of literature on human aggression, both popular and scientific, a focus on overt physical aggression has obscured other forms of aggression. As a result, considerably less is known about other, more subtle forms of aggression, such as relational aggression. Moreover, research on relational aggression, particularly among older adolescents and adults, has been hindered by the lack of psychometrically sound measures. Research in this area would be enhanced by the availability of such a measure, facilitating comparison of data across studies and reducing ambiguity over definitions of relational aggression and similar constructs. The present study involved a psychometric evaluation of the Young Adult Behavior Scale (YASB; Crothers, Schreiber, Field, & Kolbert, 2008), a self-report measure of relational aggression. College student volunteers completed the YASB and several other measures of similar and dissimilar constructs selected to evaluate construct validity. Confirmatory factor analysis was used to test the proposed 3-factor structure, which was confirmed in two separate analyses. The three subscales were internally consistent, and evidence of construct validity and concurrent criterion validity was provided. The clinical and research implications of these findings are discussed

Forewarned is forearmed : harmonized approaches for early detection of potentially invasive pests and pathogens in sentinel plantings

This work was supported by COST Action Global Warning (FP1401). DLM and YB contribution was also supported by the Russian Foundation for Basic Research (Grant No. 17-04-01486). MG was supported by Ministry of Education, Science and Technological Development of the Republic of Serbia, Grant III43002. MKA was supported by the Ministry of Science and Higher Education of the Republic of Poland. NK was supported by Le Studium foundation (France) and RFBR (Grant No. 19-04-01029). RE, IF and MK contribution was also supported by CABI with core financial support from its member countries (see http://www.cabi.org/about-cabi/who-we-work-with/key-donors/ for details). IF contribution was further supported through a grant from the Swiss State Secretariat for Science, Education and Research (Grant C15.0081, awarded to RE).Peer reviewedPublisher PD

LRG1 destabilizes tumor vessels and restricts immunotherapeutic potency

Background: A poorly functioning tumor vasculature is pro-oncogenic and may impede the delivery of therapeutics. Normalizing the vasculature, therefore, may be beneficial. We previously reported that the secreted glycoprotein leucine-rich α-2-glycoprotein 1 (LRG1) contributes to pathogenic neovascularization. Here, we investigate whether LRG1 in tumors is vasculopathic and whether its inhibition has therapeutic utility. Methods: Tumor growth and vascular structure were analyzed in subcutaneous and genetically engineered mouse models in wild-type and Lrg1 knockout mice. The effects of LRG1 antibody blockade as monotherapy, or in combination with co-therapies, on vascular function, tumor growth, and infiltrated lymphocytes were investigated. Findings: In mouse models of cancer, Lrg1 expression was induced in tumor endothelial cells, consistent with an increase in protein expression in human cancers. The expression of LRG1 affected tumor progression as Lrg1 gene deletion, or treatment with a LRG1 function-blocking antibody, inhibited tumor growth and improved survival. Inhibition of LRG1 increased endothelial cell pericyte coverage and improved vascular function, resulting in enhanced efficacy of cisplatin chemotherapy, adoptive T cell therapy, and immune checkpoint inhibition (anti-PD1) therapy. With immunotherapy, LRG1 inhibition led to a significant shift in the tumor microenvironment from being predominantly immune silent to immune active. Conclusions: LRG1 drives vascular abnormalization, and its inhibition represents a novel and effective means of improving the efficacy of cancer therapeutics. Funding: Wellcome Trust (206413/B/17/Z), UKRI/MRC (G1000466, MR/N006410/1, MC/PC/14118, and MR/L008742/1), BHF (PG/16/50/32182), Health and Care Research Wales (CA05), CRUK (C42412/A24416 and A17196), ERC (ColonCan 311301 and AngioMature 787181), and DFG (CRC1366)

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs

Chemical characterization of an aqueous extract and the essential oil of Tithonia diversifolia and their biocontrol activity against seed-borne pathogens of rice.

The high cost of chemical pesticides and their negative impact on the environment prompted the search for natural pesticides from plants. The objective of our study was to control rice seed pathogenic fungi and bacteria using aqueous extract and essential oil from Tithonia diversifolia leaves. We obtained aqueous extract and essential oil, respectively, by maceration and hydrodistillation; the antimicrobial activities were determined in vitro on a solid medium by the food poisoning method. The secondary metabolites were determined by qualitative and quantitative assays; the chemical composition of the essential oil obtained from Titonia diversifolia was studied using gas-chromatography coupled with mass spectrometry. The results showed that phenols, tannins, flavonoids, alkaloids, terpenoids, sugars and saponins were present in the aqueous extract. The essential oil contained mainly hydrocarbonated, oxygenated monoterpenes, terpenoids and sesquiterpenes. α-terpineol (20.3%), eucalyptol (14.6%), camphor (14.3%) and α-pinene (13.5%) as the main compounds. Regarding the antimicrobial activity, all tested bacteria were sensitive to aqueous extract and essential oil. The activity of the aqueous extract on the tested fungi showed an inhibitory concentration 50 (IC50) of 50 mg/mL against Bipolaris oryzae and Fusarium moniliforme. The activity of the essential oil on bacteria and fungi showed MIC of 125 μg/mL (Xanthomonas oryzae pv. oryzae and Pseudomonas fuscovaginae) and MFC of 5000 μg/mL (Bipolaris oryzae and Fusarium moniliforme). These results allow us to consider Tithonia diversifolia as a potential source of natural biopesticides against rice seed-borne pathogens

How to identify and estimate the largest traffic matrix elements in a dynamic environment

In this paper we investigate a new idea for traffic matrix estimation that makes the basic problem less under-constrained, by deliberately changing the routing to obtain additional measurements. Because all these measurements are collected over disparate time intervals, we need to establish models for each Origin-Destination (OD) pair to capture the complex behaviours of internet traffic. We model each OD pair with two components: the diurnal pattern and the fluctuation process. We provide models that incorporate the two components above, to estimate both the first and second order moments of traffic matrices. We do this for both stationary and cyclostationary traffic scenarios. We formalize the problem of estimating the second order moment in a way that is completely independent from the first order moment. Moreover, we can estimate the second order moment without needing any routing changes (i.e., without explicit changes to IGP link weights). We prove for the first time, that such a result holds for any realistic topology under the assumption of minimum cost routing and strictly positive link weights. We highlight how the second order moment helps the identification of the top largest OD flows carrying the most significant fraction of network traffic. We then propose a refined methodology consisting of using our variance estimator (without routing changes) to identify the top largest flows, and estimate only these flows. The benefit of this method is that it dramatically reduces the number of routing changes needed. We validate the effectiveness of our methodology and the intuitions behind it by using real aggregated sampled netflow data collected from a commercial Tier-1 backbone