A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network

Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.     

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model.     

APOBEC3G cytosine deamination hotspots are defined by both sequence context and single-stranded DNA secondary structure

Apolipoprotein B mRNA-editing, enzyme-catalytic, polypeptide-like 3G (i.e., APOBEC3G or A3G) is an evolutionarily conserved cytosine deaminase that potently restricts human immunodeficiency virus type 1 (HIV-1), retrotransposons and other viruses. A3G has a nucleotide target site specificity for cytosine dinucleotides, though only certain cytosine dinucleotides are ‘hotspots’ for cytosine deamination, and others experience little or no editing by A3G. The factors that define these critical A3G hotspots are not fully understood. To investigate how A3G hotspots are defined, we used an in vitro fluorescence resonance energy transfer-based oligonucleotide assay to probe the site specificity of A3G. Our findings strongly suggest that the target single-stranded DNA (ssDNA) secondary structure as well as the bases directly 3′ and 5′ of the cytosine dinucleotide are critically important A3G recognition. For instance, A3G cannot readily deaminate a cytosine dinucleotide in ssDNA stem structures or in nucleotide base loops composed of three bases. Single-stranded nucleotide loops up to seven bases in length were poor targets for A3G activity unless cytosine residues flanked the cytosine dinucleotide. Furthermore, we observed that A3G favors adenines, cytosines and thymines flanking the cytosine dinucleotide target in unstructured regions of ssDNA. Low cytosine deaminase activity was detected when guanines flanked the cytosine dinucleotide. Taken together, our findings provide the first demonstration that A3G cytosine deamination hotspots are defined by both the sequence context of the cytosine dinucleotide target as well as the ssDNA secondary structure. This knowledge can be used to better trace the origins of mutations to A3G activity, and illuminate its impact on processes such as HIV-1 genetic variation.     

Compensatory Hemagglutinin Mutations Alter Antigenic Properties of Influenza Viruses

Influenza viruses routinely acquire mutations in antigenic sites on the globular head of the hemagglutinin (HA) protein. Since these antigenic sites are near the receptor binding pocket of HA, many antigenic mutations simultaneously alter the receptor binding properties of HA. We previously reported that a K165E mutation in the Sa antigenic site of A/Puerto Rico/8/34 (PR8) HA is associated with secondary neuraminidase (NA) mutations that decrease NA activity. Here, using reverse genetics, we show that the K165E HA mutation dramatically decreases HA binding to sialic acid receptors on cell surfaces. We sequentially passaged reverse-genetics-derived PR8 viruses with the K165E antigenic HA mutation in fertilized chicken eggs, and to our surprise, viruses with secondary NA mutations did not emerge. Instead, viruses with secondary HA mutations emerged in 3 independent passaging experiments, and each of these mutations increased HA binding to sialic acid receptors. Importantly, these compensatory HA mutations were located in the Ca antigenic site and prevented binding of Ca-specific monoclonal antibodies. Taken together, these data indicate that HA antigenic mutations that alter receptor binding avidity can be compensated for by secondary HA or NA mutations. Antigenic diversification of influenza viruses can therefore occur irrespective of direct antibody pressure, since compensatory HA mutations can be located in distinct antibody binding sites.     

2G12-Expressing B Cell Lines May Aid in HIV Carbohydrate Vaccine Design Strategies

The highly conserved cluster of high-mannose glycans on the HIV-1 envelope glycoprotein, gp120, has been highlighted as a target for neutralizing antibodies. 2G12, the first HIV-1 antiglycan neutralizing antibody described, binds with an unusual domain-exchanged structure that creates a high-affinity multivalent binding surface. It is an interesting challenge for rational vaccine design to generate immunogens capable of eliciting domain-exchanged 2G12-like responses. We recently showed that di-mannose recognition by the variable domains of 2G12 is independent of domain exchange but that exchange is critical for virus neutralization. Carbohydrate-based immunogens aimed at inducing 2G12-like antibodies may need to drive both di-mannose recognition and domain exchange through interactions with B cell receptors. Here we assessed the ability of such immunogens to activate mouse B cell lines displaying domain-exchanged wild-type 2G12 (2G12 WT), a non-domain-exchanged Y-shaped variant (2G12 I19R), and germ line 2G12 (2G12 gl). We show that several immunogens, including heat-killed yeast and bacteria, can activate both 2G12 WT and 2G12 I19R B cells. However, only discrete clusters of high-mannose glycans, as on recombinant forms of the HIV-1 envelope trimer and oligodendrons, activate 2G12 WT B cells. Furthermore, no immunogen tested activated 2G12 gl cells. Our results support the hypothesis that in order to drive domain exchange of an antimannose antibody response, a boost with an immunogen displaying discrete clusters of high-mannose glycans not recognized by conventional Y-shaped antibodies will be required. Additionally, a molecule capable of activating 2G12 gl cells might also be required. The results highlight broadly neutralizing antibody-expressing mouse B cells as potentially useful tools for carbohydrate immunogen screening.     

A comparison of bats and rodents as reservoirs of zoonotic viruses: are bats special?

Bats are the natural reservoirs of a number of high-impact viral zoonoses. We present a quantitative analysis to address the hypothesis that bats are unique in their propensity to host zoonotic viruses based on a comparison with rodents, another important host order. We found that bats indeed host more zoonotic viruses per species than rodents, and we identified life-history and ecological factors that promote zoonotic viral richness. More zoonotic viruses are hosted by species whose distributions overlap with a greater number of other species in the same taxonomic order (sympatry). Specifically in bats, there was evidence for increased zoonotic viral richness in species with smaller litters (one young), greater longevity and more litters per year. Furthermore, our results point to a new hypothesis to explain in part why bats host more zoonotic viruses per species: the stronger effect of sympatry in bats and more viruses shared between bat species suggests that interspecific transmission is more prevalent among bats than among rodents. Although bats host more zoonotic viruses per species, the total number of zoonotic viruses identified in bats (61) was lower than in rodents (68), a result of there being approximately twice the number of rodent species as bat species. Therefore, rodents should still be a serious concern as reservoirs of emerging viruses. These findings shed light on disease emergence and perpetuation mechanisms and may help lead to a predictive framework for identifying future emerging infectious virus reservoirs.     

Breast tumor and stromal cell responses to TGF-β and hypoxia in matrix deposition

The components that comprise the extracellular matrix (ECM) are integral to normal tissue homeostasis as well as the development and progression of breast tumors. The secretion, construction, and remodeling of the ECM are each regulated by a complex interplay between tumor cells, fibroblasts and macrophages. Transforming growth factor-β (TGF-β) is an essential molecule in regulating the cellular production of ECM molecules and the adhesive interactions of cells with the ECM. Additionally, hypoxic cell signals, initiated by oxygen deprivation, additional metabolic factors or receptor activation, are associated with ECM formation and the progression of breast cancer. Both TGF-β and hypoxic cell signals are implicated in the functional and morphological changes of cancer-associated-fibroblasts and tumor-associated-macrophages. Moreover, the enhanced recruitment of tumor and stromal cells in response to hypoxia-induced chemokines leads to increased ECM deposition and remodeling, increased blood vessel formation, and enhanced tumor migration. Thus, elucidation of the collaborative networks between tumor and stromal cells in response to the combined signals of TGF-β and hypoxia may yield insight into treatment parameters that target both tumor and stromal cells.     

A randomized,phase 3 trial of naltrexone SR/bupropion SR on weight and obesity‐related risk factors (COR‐II)

Objective:

To examine the effects of naltrexone/bupropion (NB) combination therapy on weight and weight‐related risk factors in overweight and obese participants.

Design and Methods:

CONTRAVE Obesity Research‐II (COR‐II) was a double‐blind, placebo‐controlled study of 1,496 obese (BMI 30‐45 kg/m²) or overweight (27‐45 kg/m² with dyslipidemia and/or hypertension) participants randomized 2:1 to combined naltrexone sustained‐release (SR) (32 mg/day) plus bupropion SR (360 mg/day) (NB32) or placebo for up to 56 weeks. The co‐primary endpoints were percent weight change and proportion achieving ≥5% weight loss at week 28.

Results:

Significantly (P < 0.001) greater weight loss was observed with NB32 versus placebo at week 28 (?6.5% vs. ?1.9%) and week 56 (?6.4% vs. ?1.2%). More NB32‐treated participants (P < 0.001) experienced ≥5% weight loss versus placebo at week 28 (55.6% vs. 17.5%) and week 56 (50.5% vs. 17.1%). NB32 produced greater improvements in various cardiometabolic risk markers, participant‐reported weight‐related quality of life, and control of eating. The most common adverse event with NB was nausea, which was generally mild to moderate and transient. NB was not associated with increased events of depression or suicidality versus placebo.

Conclusion:

NB represents a novel pharmacological approach to the treatment of obesity, and may become a valuable new therapeutic option.

     

Geographic distribution of genetic diversity in populations of Rio Grande Chub <Emphasis Type="Italic">Gila pandora</Emphasis>

In the southwestern United States (US), the Rio Grande chub (Gila pandora) is state-listed as a fish species of greatest conservation need and federally listed as sensitive due to habitat alterations and competition with non-native fishes. Characterizing genetic diversity, genetic population structure, and effective number of breeders will assist with conservation efforts by providing a baseline of genetic metrics. Genetic relatedness within and among G. pandora populations throughout New Mexico was characterized using 11 microsatellite loci among 15 populations in three drainage basins (Rio Grande, Pecos, Canadian). Observed heterozygosity (H_O) ranged from 0.71–0.87 and was similar to expected heterozygosity (0.75–0.87). Rio Ojo Caliente (Rio Grande) had the highest allelic richness (A_R = 15.09), while Upper Rio Bonito (Pecos) had the lowest allelic richness (A_R = 6.75). Genetic differentiation existed among all populations with the lowest genetic variation occurring within the Pecos drainage. STRUCTURE analysis revealed seven genetic clusters. Populations of G. pandora within the upper Rio Grande drainage (Rio Ojo Caliente, Rio Vallecitos, Rio Pueblo de Taos) had high levels of admixture with Q-values ranging from 0.30–0.50. In contrast, populations within the Pecos drainage (Pecos River and Upper Rio Bonito) had low levels of admixture (Q = 0.94 and 0.87, respectively). Estimates of effective number of breeders (N _b ) varied from 6.1 (Pecos: Upper Rio Bonito) to 109.7 (Rio Grande: Rio Peñasco) indicating that populations in the Pecos drainage are at risk of extirpation. In the event that management actions are deemed necessary to preserve or increase genetic diversity of G. pandora, consideration must be given as to which populations are selected for translocation.