Top quark forward-backward asymmetry in R-parity violating supersymmetry

The interaction of bottom squark-mediated top quark pair production, occurring in the R-parity violating minimal supersymmetric standard model (MSSM), is proposed as an explanation of the anomalously large $t\bar{t}$ forward-backward asymmetry (FBA) observed at the Tevatron. We find that this model can give a good fit to top quark data, both the inclusive and invariant mass-dependent asymmetries, while remaining consistent (at the 2-$\sigma$ level) with the total and differential production cross-sections. The scenario is challenged by strong constraints from atomic parity violation (APV), but we point out an extra diagram for the effective down quark-Z vertex, involving the same coupling constant as required for the FBA, which tends to weaken the APV constraint, and which can nullify it for reasonable values of the top squark masses and mixing angle. Large contributions to flavor-changing neutral currents can be avoided if only the third generation of sparticles is light.Comment: 24 pages, 7 figures. v3: included LHC top production cross section data; model still consistent at 2 sigma leve

Spatial patterns of extensively drug-resistant tuberculosis transmission in KwaZulu-Natal, South Africa

BACKGROUND : Transmission is driving the global drug-resistant tuberculosis (TB) epidemic; nearly three-quarters of drug-resistant TB cases are attributable to transmission. Geographic patterns of disease incidence, combined with information on probable transmission links, can define the spatial scale of transmission and generate hypotheses about factors driving transmission patterns. METHODS : We combined whole-genome sequencing data with home Global Positioning System coordinates from 344 participants with extensively drug-resistant (XDR) TB in KwaZulu-Natal, South Africa, diagnosed from 2011 to 2014. We aimed to determine if genomically linked (difference of ≤5 single-nucleotide polymorphisms) cases lived close to one another, which would suggest a role for local community settings in transmission. RESULTS : One hundred eighty-two study participants were genomically linked, comprising 1084 case-pairs. The median distance between case-pairs’ homes was 108 km (interquartile range, 64–162 km). Between-district, as compared to within-district, links accounted for the majority (912/1084 [84%]) of genomic links. Half (526 [49%]) of genomic links involved a case from Durban, the urban center of KwaZulu-Natal. CONCLUSIONS : The high proportions of between-district links with Durban provide insight into possible drivers of province-wide XDR-TB transmission, including urban–rural migration. Further research should focus on characterizing the contribution of these drivers to overall XDR-TB transmission in KwaZulu-Natal to inform design of targeted strategies to curb the drug-resistant TB epidemic.Grants from the US National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH): R01AI089349 (PI Gandhi and R01AI087465 (PI Gandhi). It was also supported in part by NIH/NIAID grants: K23AI083088 (PI Brust), K24AI114444 (PI Gandhi), K23AI134182 (PI Auld), Emory CFAR P30AI050409 (PI Curran), Einstein CFAR P30AI051519 (PI Goldstein), by Einstein/Montefiore ICTR UL1 TR001073 (PI Shamoon).https://academic.oup.com/jid2019-12-15hj2019Medical Microbiolog

Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

Patterns of transmission of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases worldwide in 2017. Modeling TB transmission networks can provide insight into drivers of transmission, but incomplete sampling of TB cases can pose challenges for inference from individual epidemiologic and molecular data. We assessed the effect of missing cases on a transmission network inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases in KwaZulu-Natal, South Africa, diagnosed in 2011–2014. We tested scenarios in which cases were missing at random, missing differentially by clinical characteristics, or missing differentially by transmission (i.e., cases with many links were under- or oversampled). Under the assumption that cases were missing randomly, the mean number of transmissions per case in the complete network needed to be larger than 20, far higher than expected, to reproduce the observed network. Instead, the most likely scenario involved undersampling of high-transmitting cases, and models provided evidence for super-spreading. To our knowledge, this is the first analysis to have assessed support for different mechanisms of missingness in a TB transmission study, but our results are subject to the distributional assumptions of the network models we used. Transmission studies should consider the potential biases introduced by incomplete sampling and identify host, pathogen, or environmental factors driving super-spreading.This work was presented at the Seventh International Conference on Infectious Disease Dynamics (Epidemics7), Charleston, South Carolina, December 3–6, 2019.The National Institute of Allergy and Infectious Diseases, US National Institutes of Health, the National Institute of Allergy and Infectious Diseases, the Emory Center for AIDS Research, the Einstein Center for AIDS Research and the Einstein/Montefiore Institute for Clinical and Translational Research.https://academic.oup.com/ajehj2021Medical Microbiolog