Dr Carlos Riveros

Background: The onset of the term human parturition involves myometrial gene expression changes to transform the uterus from a quiescent to a contractile phenotype. It is uncertain whether the same changes occur in the uterus during preterm labor. Objective: This study aimed to compare the myometrial gene expression between term and preterm labor and to determine whether the presence of acute clinical chorioamnionitis or twin gestation affects these signatures. Study Design: Myometrial specimens were collected during cesarean delivery from the following 7 different groups of patients: term not in labor (n=31), term labor (n=13), preterm not in labor (n=21), preterm labor with acute clinical chorioamnionitis (n=6), preterm labor with no acute clinical chorioamnionitis (n=9), twin preterm not in labor (n=8), and twin preterm labor with no acute clinical chorioamnionitis (n=5). RNA was extracted, reverse transcribed and quantitative polymerase chain reactions were performed on 44 candidate genes (with evidence for differential expression in human term labor) using the Fluidigm platform. Computational analysis was performed using 2-class unpaired Wilcoxon tests and principal component analysis. Results: Computational analysis revealed that gene expression in the preterm myometrium, irrespective of whether in labor or not in labor, clustered tightly and is clearly different from the term labor and term not-in-labor groups. This was true for both singleton and twin pregnancies. Principal component analysis showed that 57% of the variation was explained by 3 principal components. These 44 genes interact in themes of prostaglandin activity and inflammatory signaling known to be important during term labor, but are not a full representation of the myometrium transcriptional activity. Conclusion: The myometrial contractions associated with preterm labor are associated with a pattern of gene expression that is distinct from term labor. Therefore, preterm labor may be initiated by a different myometrial process or processes outside the myometrium.

Background and Purpose: Long periods of daily sedentary time, particularly accumulated in long uninterrupted bouts, are a risk factor for cardiovascular disease. People with stroke are at high risk of recurrent events and prolonged sedentary time may increase this risk. We aimed to explore how people with stroke distribute their periods of sedentary behavior, which factors influence this distribution, and whether sedentary behavior clusters can be distinguished? Methods: This was a secondary analysis of original accelerometry data from adults with stroke living in the community. We conducted data-driven clustering analyses to identify unique accumulation patterns of sedentary time across participants, followed by multinomial logistical regression to determine the association between the clusters, and the total amount of sedentary time, age, gender, body mass index (BMI), walking speed, and wake time. Results: Participants in the highest quartile of total sedentary time accumulated a significantly higher proportion of their sedentary time in prolonged bouts (P < 0.001). Six unique accumulation patterns were identified, all of which were characterized by high sedentary time. Total sedentary time, age, gender, BMI, and walking speed were significantly associated with the probability of a person being in a specific accumulation pattern cluster, P < 0.001 - P = 0.002. Discussion and Conclusions: Although unique accumulation patterns were identified, there is not just one accumulation pattern for high sedentary time. This suggests that interventions to reduce sedentary time must be individually tailored. Video Abstract available for more insight from the authors (see the Video Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A343).

Background: High levels of sedentary time increases the risk of cardiovascular disease, including recurrent stroke. Objective: This study aimed to identify factors associated with high sedentary time in community-dwelling people with stroke. Methods: For this data pooling study, authors of published and ongoing trials that collected sedentary time data, using the activPAL monitor, in community-dwelling people with stroke were invited to contribute their raw data. The data was reprocessed, algorithms were created to identify sleep-wake time and determine the percentage of waking hours spent sedentary. We explored demographic and stroke-related factors associated with total sedentary time and time in uninterrupted sedentary bouts using unique, both univariable and multivariable, regression analyses. Results: The 274 included participants were from Australia, Canada, and the United Kingdom, and spent, on average, 69% (SD 12.4) of their waking hours sedentary. Of the demographic and stroke-related factors, slower walking speeds were significantly and independently associated with a higher percentage of waking hours spent sedentary (p = 0.001) and uninterrupted sedentary bouts of >30 and >60 min (p = 0.001 and p = 0.004, respectively). Regression models explained 11¿19% of the variance in total sedentary time and time in prolonged sedentary bouts. Conclusion: We found that variability in sedentary time of people with stroke was largely unaccounted for by demographic and stroke-related variables. Behavioral and environmental factors are likely to play an important role in sedentary behavior after stroke. Further work is required to develop and test effective interventions to address sedentary behavior after stroke.

In this work, we incorporate new edges from a paracliqueidentification approach to the output of theMST-kNN graph partitioning method. We present a statistical analysis of the results on a dataset originated from a computational linguistic study of 84 Indo-European languages. We also present results from a computational stylistic study of 168 plays of the Shakespearean era. For the latter, results of the Kruskal- Wallis test 1 (observed vs. all permutations) showed a p-value of a 1.62E- 11 and a Wilcoxon test a p-value of 8.1E-12. Overall, our results clearly show in both cases that the modified approach provides statistically more significant results than the use of the MST-kNN alone, thus providing a highly-scalable alternative and statistically sound approach for data clustering.