Cellular tropism of vaccinia virus (VACV) isVaccinia virus (VACV) is the prototypical member of the poxvirus family of large, complex, double-stranded DNA viruses (21). VACV has a very broad host range and is capable of infecting many vertebrate animal species. Its host range, however, can be significantly narrowed by deleting from its genome some of the so-called host range genes, the most important of which are E3L, K1L, and C7L (17). VACV mutants deleted of E3L (⌬E3L) or both K1L and C7L (⌬K1L⌬C7L) replicate abortively and express only a subset of viral genes in most mammalian cell lines (3, 24). These mutants are highly attenuated in animal hosts but are capable of eliciting immune responses, making them attractive vaccine vectors for infectious diseases and cancers (27,28). NYVAC, a VACV strain derived through deletion of 18 genes, including both K1L and C7L (27), has been used as the vector for an AIDS vaccine (2).The functions of E3L and the host factors that restrict the replication of the ⌬E3L mutant have been studied extensively. E3L encodes a 20-kDa and a 25-kDa protein that bind doublestranded RNA (dsRNA) and Z form DNA (6, 15). The E3 proteins antagonize the dsRNA-dependent protein kinase PKR (5), which exists as an inactive form in the cells and undergoes autophosphorylation and activation upon binding to dsRNA. The activated PKR phosphorylates the ␣ subunit of eukaryotic initiation factor 2 (eIF2␣), resulting in a block in protein translation at the initiation step. The infection of most mammalian cells by the ⌬E3L mutant leads to the activation of PKR and a block in translating viral mRNAs (16). The replication of the ⌬E3L mutant in nonpermissive HeLa cells can be rescued by silencing PKR expression (32), while its replication in permissive Huh7 (human hepatoma) cells can be blocked by upregulating PKR expression with interferon (IFN) treatment (1). In addition to affecting PKR, E3 has also been shown to inactivate IFN-stimulated gene 15 (ISG15) (14), another IFN effector that plays a role in host defense against VACV.Like the replication of the ⌬E3L mutant, the replication of the ⌬K1L⌬C7L mutant in nonpermissive HeLa cells is blocked at the translation of viral mRNA. However, the host factors that restrict the replication of the ⌬K1L⌬C7L mutant and the molecular functions of K1L or C7L remain a mystery. K1 and C7 share no amino acid sequence homology, but either K1L or C7L can complement the replication defect of the ⌬K1L⌬C7L mutant in most cell lines. The exception is rabbit RK13 cells, where K1L but not C7L can complement (24). K1L is present in only a few orthopoxviruses, while C7L or a functional homologue of C7L is present in almost all mammalian poxviruses (18). K1 comprises multiple ankyrin repeats, a protein motif that is involved in protein-ligand interaction. It was shown to prevent the degradation of IB␣ and to thus inhibit host . C7L has no homologue outside the poxvirus family, and its molecular function remains unknown. It may play a role in inhibiting cellular apoptosis in respons...

Many populated valleys in the western United States experience increased concentrations of particulate matter with diameter of less than 2.5 μm (PM2.5) during winter stagnation conditions. Further study into the chemical components composing wintertime PM2.5 and how the composition and level of wintertime PM2.5 are related to meteorological conditions can lead to a better understanding of the causes of high PM2.5 and aid in development and application of emission controls. The results can also aid in short-term air-pollution forecasting and implementation of periodic emission controls such as burning bans. This study examines relationships between PM2.5 concentrations and wintertime atmospheric stability (defined by heat deficit) during snow-covered and snow-free conditions from 2000 to 2013 for five western U.S. urbanizations: Salt Lake City, Utah; Reno, Nevada; Boise, Idaho; Missoula, Montana; and Spokane, Washington. Radiosonde data were used where available to calculate daily heat deficit, which was compared with PM2.5 concentration for days with snow cover and days with no snow cover. Chemically speciated PM2.5 data were compared for snow-cover and snow-free days to see whether the chemical abundances varied by day category. Wintertime PM2.5 levels were highly correlated with heat deficit for all cities except Spokane, where the airport sounding does not represent the urban valley. For a given static stability, snow-cover days experienced higher PM2.5 levels than did snow-free days, mainly because of enhanced ammonium nitrate concentrations. Normalizing average PM2.5 to the heat deficit reduced year-to-year PM2.5 variability, resulting in stronger downward trends, mostly because of reduced carbonaceous aerosol concentrations. The study was limited to western U.S. cities, but similar results are expected for other urban areas in mountainous terrain with cold, snowy winters.

The need for larger-scale and increasingly complex protein-protein interaction (PPI) prediction tasks demands that state-of-the-art predictors be highly efficient and adapted to inter-and cross-species predictions. Furthermore, the ability to generate comprehensive interactomes has enabled the appraisal of each PPI in the context of all predictions leading to further improvements in classification performance in the face of extreme class imbalance using the Reciprocal Perspective (RP) framework. We here describe the PIPE4 algorithm. Adaptation of the PIPE3/MP-PIPE sequence preprocessing step led to upwards of 50x speedup and the new Similarity Weighted Score appropriately normalizes for window frequency when applied to any inter-and cross-species prediction schemas. Comprehensive interactomes for three prediction schemas are generated: (1) cross-species predictions, where Arabidopsis thaliana is used as a proxy to predict the comprehensive Glycine max interactome, (2) interspecies predictions between Homo sapiens-HIV1, and (3) a combined schema involving both cross-and inter-species predictions, where both Arabidopsis thaliana and Caenorhabditis elegans are used as proxy species to predict the interactome between Glycine max (the soybean legume) and Heterodera glycines (the soybean cyst nematode). Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas. The elucidation of protein-protein interaction (PPI) networks is central to molecular biology research. Necessary to producing mechanistic models of cellular processes, PPI networks additionally contribute to challenges such as the prediction of gene function 1-3 , identification of disease genes 4 , and pharmaceutical discovery 5,6. Computational PPI prediction techniques have been developed to supplement and guide wet-laboratory experimental work. The last decade has seen increased computational demand in both scale and complexity of PPI predictors. Predicting comprehensive interactomes (the set of all possible pairwise PPIs in or between proteomes) has only recently become possible with the advent of high-performance computing infrastructure and algorithmic optimizations. While methodologically diverse in their implementation, PPI prediction tools generally exploit information from the set of known PPIs (previously confirmed using classical wet-laboratory techniques) to determine whether any two query proteins will physically interact. The utility and scalability of any one method is subject to the information it leverages. Structure-based methods, at one extreme, require the three-dimensional (3D) characterization of each protein and therefore suffer from low coverage of the proteome. While useful to determining highly specific PPI networks, many methods require template-based modelling which tend to be computationally taxing 7-9. Furthermore, even with complete 3D structural information of each protein in an organism's proteome, the computational time comp...

All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method.

This study investigates the causes of elevated PM_2.5 episodes and potential exceedences of the US National Ambient Air Quality Standards (NAAQS) in Truckee Meadows, Nevada, an urban valley of the Western US, during winter 2009/2010, an unusually cold and snowy winter. Continuous PM_2.5 mass and time-integrated chemical speciation data were acquired from a central valley monitoring site, along with meteorological measurements from nearby sites. All nine days with PM_2.5 > 35 μg m⁻³ showed 24-h average temperature inversion of 1.5–4.5 °C and snow cover of 8–18 cm. Stagnant atmospheric conditions limited wind ventilation while highly reflective snow cover reduced daytime surface heating creating persistent inversion. Elevated ammonium nitrate (NH₄NO₃) and water associated with it are found to be main reasons for the PM_2.5 exceedances. An effective-variance chemical mass balance (EV-CMB) receptor model using locally-derived geological profiles and inorganic/organic markers confirmed secondary NH₄NO₃ (27–37%), residential wood combustion (RWC; 11–51%), and diesel engine exhaust (7–22%) as the dominant PM_2.5 contributors. Paved road dust and de-icing materials were minor, but detectable contributors. RWC is a more important source than diesel for organic carbon (OC), but vice versa for elemental carbon (EC). A majority of secondary NH₄NO₃ is also attributed to RWC and diesel engines (including snow removal equipment) through oxides of nitrogen (NO_x) emissions from these sources. Findings from this study may apply to similar situations experienced by other urban valleys