Social Measurement and Causal Inference with Text

The digital age has dramatically increased access to large-scale collections of digitized text documents. These corpora include, for example, digital traces from social media, decades of archived news reports, and transcripts of spoken interactions in political, legal, and economic spheres. For social scientists, this new widespread data availability has potential for improved quantitative analysis of relationships between language use and human thought, actions, and societal structure. However, the large-scale nature of these collections means that traditional manual approaches to analyzing content are extremely costly and do not scale. Furthermore, incorporating unstructured text data into quantitative analysis is difficult due to texts’ high-dimensional nature and linguistic complexity. This thesis blends (a) the computational strengths of natural language processing (NLP) and machine learning to automate and scale-up quantitative text analysis with (b) two themes central to social scientific studies but often under-addressed in NLP: measurement—creating quantifiable summaries of empirical phenomena—and causal inference—estimating the effects of interventions. First, we address measuring class prevalence in document collections; we contribute a generative probabilistic modeling approach to prevalence estimation and show empirically that our model is more robust to shifts in class priors between training and inference. Second, we examine cross- document entity-event measurement; we contribute an empirical pipeline and a novel latent disjunction model to identify the names of civilians killed by police from our corpus of web-scraped news reports. Third, we gather and categorize applications that use text to reduce confounding from causal estimates and contribute a list of open problems as well as guidance about data processing and evaluation decisions in this area. Finally, we contribute a new causal research design to estimate the natural indirect and direct effects of social group signals (e.g. race or gender) on conversational outcomes with separate aspects of language as causal mediators; this chapter is motivated by a theoretical case study of U.S. Supreme Court oral arguments and the effect of an advocate’s gender on interruptions from justices. We conclude by discussing the relationship between measurement and causal inference with text and future work at this intersection

Microwave Dielectric Heating of Drops in Microfluidic Devices

We present a technique to locally and rapidly heat water drops in microfluidic devices with microwave dielectric heating. Water absorbs microwave power more efficiently than polymers, glass, and oils due to its permanent molecular dipole moment that has a large dielectric loss at GHz frequencies. The relevant heat capacity of the system is a single thermally isolated picoliter drop of water and this enables very fast thermal cycling. We demonstrate microwave dielectric heating in a microfluidic device that integrates a flow-focusing drop maker, drop splitters, and metal electrodes to locally deliver microwave power from an inexpensive, commercially available 3.0 GHz source and amplifier. The temperature of the drops is measured by observing the temperature dependent fluorescence intensity of cadmium selenide nanocrystals suspended in the water drops. We demonstrate characteristic heating times as short as 15 ms to steady-state temperatures as large as 30 degrees C above the base temperature of the microfluidic device. Many common biological and chemical applications require rapid and local control of temperature, such as PCR amplification of DNA, and can benefit from this new technique.Comment: 6 pages, 4 figure

Transcriptional responses in Honey Bee larvae infected with chalkbrood fungus

<p>Abstract</p> <p>Background</p> <p>Diseases and other stress factors working synergistically weaken honey bee health and may play a major role in the losses of bee populations in recent years. Among a large number of bee diseases, chalkbrood has been on the rise. We present here the experimental identification of honey bee genes that are differentially expressed in response to infection of honey bee larvae with the chalkbrood fungus, <it>Ascosphaera apis</it>.</p> <p>Results</p> <p>We used cDNA-AFLP ^®Technology to profile transcripts in infected and uninfected bee larvae. From 64 primer combinations, over 7,400 transcriptionally-derived fragments were obtained A total of 98 reproducible polymorphic cDNA-AFLP fragments were excised and sequenced, followed by quantitative real-time RT-PCR (qRT-PCR) analysis of these and additional samples.</p> <p>We have identified a number of differentially-regulated transcripts that are implicated in general mechanisms of stress adaptation, including energy metabolism and protein transport. One of the most interesting differentially-regulated transcripts is for a chitinase-like enzyme that may be linked to anti-fungal activities in the honey bee larvae, similarly to gut and fat-body specific chitinases found in mosquitoes and the red flour beetle. Surprisingly, we did not find many components of the well-characterized NF-κB intracellular signaling pathways to be differentially-regulated using the cDNA-AFLP approach. Therefore, utilizing qRT-PCR, we probed some of the immune related genes to determine whether the lack of up-regulation of their transcripts in our analysis can be attributed to lack of immune activation or to limitations of the cDNA-AFLP approach.</p> <p>Conclusions</p> <p>Using a combination of cDNA-AFLP and qRT-PCR analyses, we were able to determine several key transcriptional events that constitute the overall effort in the honey bee larvae to fight natural fungal infection. Honey bee transcripts identified in this study are involved in critical functions related to transcriptional regulation, apoptotic degradation of ubiquitinated proteins, nutritional regulation, and RNA processing. We found that immune regulation of the anti-fungal responses in honey bee involves highly coordinated activation of both NF-κB signaling pathways, leading to production of anti-microbial peptides. Significantly, activation of immune responses in the infected bee larvae was associated with down-regulation of major storage proteins, leading to depletion of nutritional resources.</p

RCT Rejection Sampling for Causal Estimation Evaluation

Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics, or the behavioral social sciences -- researchers have proposed methods to adjust for confounding by adapting machine learning methods to the goal of causal estimation. However, empirical evaluation of these adjustment methods has been challenging and limited. In this work, we build on a promising empirical evaluation strategy that simplifies evaluation design and uses real data: subsampling randomized controlled trials (RCTs) to create confounded observational datasets while using the average causal effects from the RCTs as ground-truth. We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT. Using synthetic data, we show our algorithm indeed results in low bias when oracle estimators are evaluated on the confounded samples, which is not always the case for a previously proposed algorithm. In addition to this identification result, we highlight several finite data considerations for evaluation designers who plan to use RCT rejection sampling on their own datasets. As a proof of concept, we implement an example evaluation pipeline and walk through these finite data considerations with a novel, real-world RCT -- which we release publicly -- consisting of approximately 70k observations and text data as high-dimensional covariates. Together, these contributions build towards a broader agenda of improved empirical evaluation for causal estimation.Comment: Code and data at https://github.com/kakeith/rct_rejection_samplin

Inserting Pharmacists in Primary Care Roles in an Ambulatory Care Setting

In this report, we suggest how pharmacy personnel may be used to alleviate some of the pressures currently impacting health system administrators. We look back to the role(s) of the hospital pharmacy and the hospital pharmacist historically and outline changes that have occurred and how these changes may be helpful to address several problem areas in the ambulatory care venue

Preparation and catalytic evaluation of ruthenium–nickel dendrimer encapsulated nanoparticles via intradendrimer redox displacement of nickel nanoparticles

Ru and Ru_xNi_(30) dendrimer encapsulated nanoparticles (DENs) were synthesized using a redox-displacement method. DEN catalytic activity for the reduction of p-nitrophenol was evaluated and found to be dependent on the ratio of metals present