Hawkes binomial topic model with applications to coupled conflict-Twitter data

We consider the problem of modeling and clustering heterogeneous event data arising from coupled conflict event and social media data sets. In this setting conflict events trigger responses on social media, and, at the same time, signals of grievance detected in social media may serve as leading indicators for subsequent conflict events. For this purpose we introduce the Hawkes Binomial Topic Model (HBTM) where marks, Tweets and conflict event descriptions are represented as bags of words following a Binomial distribution. When viewed as a branching process, the daughter event bag of words is generated by randomly turning on/off parent words through independent Bernoulli random variables. We then use expectation–maximization to estimate the model parameters and branching structure of the process. The inferred branching structure is then used for topic cascade detection, short-term forecasting, and investigating the causal dependence of grievance on social media and conflict events in recent elections in Nigeria and Kenya

High-throughput in vivo vertebrate screening

We demonstrate a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. The system automatically loads zebrafish from reservoirs or multiwell plates, and positions and rotates them for high-speed confocal imaging and laser manipulation of both superficial and deep organs within 19 s without damage. We performed small-scale test screening of retinal axon guidance mutants and neuronal regeneration assays in combination with femtosecond laser microsurgery.National Institutes of Health (U.S.) (Director’s Innovator Award 1-DP2-OD002989–01)David & Lucile Packard Foundation (Award in Science and Engineering)Alfred P. Sloan Foundation (Award)Broad Institute of MIT and Harvard (Sparc Grant)National Science Foundation (U.S.) (Fellowship)Foxconn (Sponsorship

Exercise Decreases Marrow Adipose Tissue Through ß-Oxidation in Obese Running Mice: EXERCISE DECREASES MAT IN OBESE MICE

The relationship between marrow adipose tissue (MAT) and bone health is poorly understood. We used running exercise to ask whether obesity-associated MAT can be attenuated via exercise and whether this correlates with gains in bone quantity and quality. C57BL/6 mice were divided into diet-induced obesity (DIO, n = 14) versus low-fat diet (LFD, n = 14). After 3 months, 16-week-old mice were allocated to an exercise intervention (LFD-E, DIO-E) or a control group (LFD, DIO) for 6 weeks (4 groups, n = 7/group). Marrow adipocyte area was 44% higher with obesity (p<0.0001) and after exercise 33% lower in LFD (p<0.0001) and 39% lower in DIO (p<0.0001). In LFD, exercise did not affect adipocyte number; however, in DIO, the adipocyte number was 56% lower (p<0.0001). MAT was 44% higher in DIO measured by osmium-µCT, whereas exercise associated with reduced MAT (–23% in LFD, –48% in DIO, p<0.05). MAT was additionally quantified by 9.4TMRI, and correlated with osmium-µCT (r = 0.645; p<0.01). Consistent with higher lipid beta oxidation, perilipin 3 (PLIN3) rose with exercise in tibial mRNA (+92% in LFD,+60% in DIO, p<0.05). Tibial µCT-derived trabecular bone volume (BV/TV) was not influenced by DIO but responded to exercise with an increase of 19% (p<0.001). DIO was associated with higher cortical periosteal and endosteal volumes of 15% (p = 0.012) and 35% (p<0.01), respectively, but Ct. Ar/Tt.Ar was lower by 2.4% (p<0.05). There was a trend for higher stiffness (N/m) in DIO, and exercise augmented this further. In conclusion, obesity associated with increases in marrow lipid—measured by osmium-µCT and MRI—and partially due to an increase in adipocyte size, suggesting increased lipid uptake into preexisting adipocytes. Exercise associated with smaller adipocytes and less bone lipid, likely invoking increased ß-oxidation and basal lipolysis as evidenced by higher levels of PLIN3

Exercise Increases Bone in SEIPIN Deficient Lipodystrophy, Despite Low Marrow Adiposity

Exercise, typically beneficial for skeletal health, has not yet been studied in lipodystrophy, a condition characterized by paucity of white adipose tissue, with eventual diabetes, and steatosis. We applied a mouse model of global deficiency of Bscl2 (SEIPIN), required for lipid droplet formation. Male twelve-week-old B6 knockouts (KO) and wild type (WT) littermates were assigned six-weeks of voluntary, running exercise (E) versus non-exercise (N=5-8). KO weighed 14% less than WT (p=0.01) and exhibited an absence of epididymal adipose tissue; KO liver Plin1 via qPCR was 9-fold that of WT (p=0.04), consistent with steatosis. Bone marrow adipose tissue (BMAT), unlike white adipose, was measurable, although 40.5% lower in KO vs WT (p=0.0003) via 9.4T MRI/advanced image analysis. SEIPIN ablation’s most notable effect marrow adiposity was in the proximal femoral diaphysis (-56% KO vs WT, p=0.005), with relative preservation in KO-distal-femur. Bone via μCT was preserved in SEIPIN KO, though some quality parameters were attenuated. Running distance, speed, and time were comparable in KO and WT. Exercise reduced weight (-24% WT-E vs WT p<0.001) but not in KO. Notably, exercise increased trabecular BV/TV in both (+31%, KO-E vs KO, p=0.004; +14%, WT-E vs WT, p=0.006). The presence and distribution of BMAT in SEIPIN KO, though lower than WT, is unexpected and points to a uniqueness of this depot. That trabecular bone increases were achievable in both KO and WT, despite a difference in BMAT quantity/distribution, points to potential metabolic flexibility during exercise-induced skeletal anabolism

Sampling Social Media: Supporting Information Retrieval from Microblog Data Resellers with Text, Network, and Spatial Analysis

This paper presents a computationally assisted method for scaling researcher expertise to large, online social media datasets in which access is constrained and costly. Developed collaboratively between social and computer science researchers, this method is designed to be flexible, scalable, cost-effective, and to reduce bias in data collection. Online response to six case studies covering elections and election-related violence in Sub-Saharan African countries are explored using Twitter, a popular online microblogging platform. Results show: 1) automated query expansion can mitigate researcher bias, 2) machine learning models combining textual, social, temporal, and geographic features in social media data perform well in filtering data unrelated to the target event, and 3) these results are achievable while minimizing fee-based queries by bootstrapping with readily-available Twitter samples