Heterogeneity of Signaling Complex Nanostructure in T Cells Activated Via the T Cell Antigen Receptor

Activation of the T cell antigen receptor (TCR) is a key step in initiating the adaptive immune response. Single-molecule localization techniques have been used to investigate the arrangement of proteins within the signaling complexes formed around activated TCRs, but a clear picture of nanoscale organization in stimulated T cells has not emerged. Here, we have improved the examination of T cell nanostructure by visualizing individual molecules of six different proteins in a single sample of activated Jurkat T cells using the multiplexed antibody-size limited direct stochastic optical reconstruction microscopy (madSTORM) technique. We formally define irregularly shaped regions of interest, compare areas where signaling complexes are concentrated with other areas, and improve the statistical analyses of the locations of molecules. We show that nanoscale organization of proteins is mainly confined to the areas with dense concentrations of TCR-based signaling complexes. However, randomly distributed molecules are also found in some areas containing concentrated signaling complexes. These results are consistent with the view that the proteins within signaling complexes are connected by numerous weak interactions, leading to flexible, dynamic, and mutable structures which produce large variations in the nanostructure found in activated T cells.

Point Process Models for the Spread of Coccidioidomycosis in California

Coccidioidomycosis is an infectious disease of humans and other mammals that has seen a recent increase in occurrence in the southwestern United States, particularly in California. A rise in cases and risk to public health can serve as the impetus to apply newly developed methods that can quickly and accurately predict future caseloads. The recursive and Hawkes point process models with various triggering functions were fit to the data and their goodness of fit evaluated and compared. Although the point process models were largely similar in their fit to the data, the recursive point process model offered a slightly superior fit. We explored forecasting the spread of coccidioidomycosis in California from December 2002 to December 2017 using this recursive model, and we separated the training and testing portions of the data and achieved a root mean squared error of just 3.62 cases/week.

Placenta and appetite genes GDF15 and IGFBP7 are associated with hyperemesis gravidarum

Hyperemesis gravidarum (HG), severe nausea and vomiting of pregnancy, occurs in 0.3-2% of pregnancies and is associated with maternal and fetal morbidity. The cause of HG remains unknown, but familial aggregation and results of twin studies suggest that understanding the genetic contribution is essential for comprehending the disease etiology. Here, we conduct a genome-wide association study (GWAS) for binary (HG) and ordinal (severity of nausea and vomiting) phenotypes of pregnancy complications. Two loci, chr19p13.11 and chr4q12, are genome-wide significant (p < 5 × 10-8) in both association scans and are replicated in an independent cohort. The genes implicated at these two loci are GDF15 and IGFBP7 respectively, both known to be involved in placentation, appetite, and cachexia. While proving the casual roles of GDF15 and IGFBP7 in nausea and vomiting of pregnancy requires further study, this GWAS provides insights into the genetic risk factors contributing to the disease.

On measures of dissimilarity between point patterns: classification based on prototypes and multidimensional scaling

This paper presents a collection of dissimilarity measures to describe and then classify spatial point patterns when multiple replicates of different types are available for analysis. In particular, we consider a range of distances including the spike-time distance and its variants, as well as cluster-based distances and dissimilarity measures based on classical statistical summaries of point patterns. We review and explore, in the form of a tutorial, their uses, and their pros and cons. These distances are then used to summarize and describe collections of repeated realizations of point patterns via prototypes and multidimensional scaling. We also show a simulation study to evaluate the performance of multidimensional scaling with two types of selected distances. Finally, a multivariate spatial point pattern of a natural plant community is analyzed through various of these measures of dissimilarity.

Change in paternity and recurrence of hyperemesis gravidarum

OBJECTIVE

To determine whether change in paternity changes recurrence risk of hyperemesis gravidarum (HG).

STUDY DESIGN

Survey data on recurrence of HG was compared between cases who had a paternity change between pregnancies and cases who did not.

RESULTS

The percentage of HG pregnancies in women with the same partner for all pregnancies was not significantly different from the percentage of HG pregnancies in women who changed partners for at least one pregnancy (78% vs 71%, p > 0.05). Participants who did and did not change partners between their first and second pregnancies, were asked to rate their first and second pregnancy in regards to symptoms of HG. Neither the ratings nor the change in rating between pregnancies was significantly different between the two groups.

CONCLUSION

Women reported HG in over 70% of their pregnancies regardless of a paternity change. Paternal genes expressed through the fetus do not have a significant effect on incidence or recurrence of HG. This study supports a strong maternal genetic factor involved in HG. However, because the recurrence risk is not 100%, other factors play a role. Identification of the predisposing gene(s) and other factors will determine the cause of this poorly understood complication of pregnancy.

Algorithms for computing spike time distance and point process prototypes with application to feline neuronal responses to acoustic stimuli

The spike-time distance metric and the prototype of a collection of point patterns can be used to provide a metric description of repeated observations of point processes. Due to algorithmic limitations in computing spike-time distance, previous work in determining approximations of the prototype has largely been limited to single dimensional or small data sets. We develop new algorithms for each of these methods that are suitable for larger data sets. The first, an incremental matching algorithm, is a method to compute spike-time distance. The second algorithm involves the use of kernel smoothing for prototype construction. These methods readily extend to multiple dimensions and to marked point processes. We use a combination of these approaches to analyze neuronal spike data of cats in different behavioral states and across animals, and we compare our results to previous time series analyses that used averaged frequency histograms. Evidence is found for short latency responses to the acoustic CS that were sensitive to behavioral state.

A statistical analysis of santa barbara ambulance response in 2006: performance under load

Ambulance response times in Santa Barbara County for 2006 are analyzed using point process techniques, including kernel intensity estimates and K-functions. Clusters of calls result in significantly higher response times, and this effect is quantified. In particular, calls preceded by other calls within 20 km and within the previous hour are significantly more likely to result in violations. This effect appears to be especially pronounced within semi-rural neighborhoods.