Video materials require learners to manage concurrent verbal and pictorial processing. To facilitate second language (L2) learners’ video comprehension, the amount of presented information should thus be compatible with human beings’ finite cognitive capacity. In light of this, the current study explored whether a reduction in multimodal comprehension scaffolding would lead to better L2 comprehension gain when viewing captioned videos and, if so, which type of reduction (verbal vs. nonverbal) is more beneficial. A total of 62 L2 learners of English were randomly assigned to one of the following viewing conditions: (1) full captions + animation, (2) full captions + static key frames, (3) partial captions + animation, and (4) partial captions + static key frames. They then completed a comprehension test and cognitive load questionnaire. The results showed that while viewing the video with reduced nonverbal visual information (static key frames), the participants had well-rounded performance in all aspects of comprehension. However, their local comprehension (extraction of details) was particularly enhanced after viewing a key-framed video with full captions. Notably, this gain in local comprehension was not as manifest after viewing animated video content with full captions. The qualitative data also revealed that although animation may provide a perceptually stimulating viewing experience, its transient feature most likely taxed the participants’ attention, thus impacting their comprehension outcomes. These findings underscore the benefit of a reduction in nonverbal input and the interplay between verbal and nonverbal input. The findings are discussed in relation to the use of verbal and nonverbal input for different pedagogical purposes.

Political actors often interact spatially, and move around. However, with a few exceptions, existing political research has analyzed spatial dependence among actors with fixed geographic locations. Focusing on fixated geographic units prevents us from probing dependencies in spatial interaction between spatially dynamic actors, which are common in some areas of political science, such as sub-national conflict studies. In this note, we propose a method to account for spatial dependence in dyadic interactions between moving actors. Our method uses the spatiotemporal histories of dyadic interactions to project locations of future interactions—projected actor locations (PALs). PALs can, in turn, be used to model the likelihood of future dyadic interactions. In a replication and extension of a recent study of subnational conflict, we find that using PALs improves the predictive performance of the model and indicates that there is a clear relationship between actors’ past conflict locations and the likelihood of future conflicts.

International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses on dyadic data.

ABSTRACT IMPACT: Screening the effect of thousands of non-coding genetic variants will help identify variants important in the etiology of diseases OBJECTIVES/GOALS: Massively parallel reporter assays (MPRAs) can experimentally evaluate the impact of genetic variants on gene expression. In this study, our objective was to systematically evaluate the functional activity of 3’-UTR SNPs associated with neurological disorders and use those results to help understand their contributions to disease etiology. METHODS/STUDY POPULATION: To choose variants to evaluate with the MPRA, we first gathered SNPs from the GWAS Catalog that were associated with any neurological disorder trait with p-value < 10-5. For each SNP, we identified the region that was in linkage disequilibrium (r2 > 0.8) and retrieved all the common 3’-UTR SNPs (allele-frequency > 0.05) within that region. We used an MPRA to measure the impact of these 3’-UTR variants in SH-SY5Y neuroblastoma cells and a microglial cell line. These results were then used to train a deep-learning model to predict the impact of variants and identify features that contribute to the predictions. RESULTS/ANTICIPATED RESULTS: Of the 13,515 3’-UTR SNPs tested, 400 and 657 significantly impacted gene expression in SH-SY5Y and microglia, respectively. Of the 84 SNPs significantly impacted in both cells, the direction of impact was the same in 81. The direction of eQTL in GTEx tissues agreed with the assay SNP effect in SH-SY5Y cells but not microglial cells. The deep-learning model predicted sequence activity level correlated with the experimental activity level (Spearman’s corr = 0.45). The deep-learning model identified several predictive motifs similar to motifs of RNA-binding proteins. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study demonstrates that MPRAs can be used to evaluate the effect of non-coding variants, and the results can be used to train a machine learning model and interpret its predictions. Together, these can help identify causal variants and further understand the etiology of diseases.

Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced by the presence of tides, which depend on the neutron star equation of state. Neutron star mergers are expected to often produce rapidly rotating remnant neutron stars that emit gravitational waves. These will provide clues to the extremely hot post-merger environment. This signature of nuclear matter in gravitational waves contains most information in the 2–4 kHz frequency band, which is outside of the most sensitive band of current detectors. We present the design concept and science case for a Neutron Star Extreme Matter Observatory (NEMO): a gravitational-wave interferometer optimised to study nuclear physics with merging neutron stars. The concept uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves. Above 1 kHz, the proposed strain sensitivity is comparable to full third-generation detectors at a fraction of the cost. Such sensitivity changes expected event rates for detection of post-merger remnants from approximately one per few decades with two A+ detectors to a few per year and potentially allow for the first gravitational-wave observations of supernovae, isolated neutron stars, and other exotica.

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

Hippocampal neurogenesis continues throughout adult life and potentially plays a crucial role in mood and cognitive disorders. We summarise the preclinical insights and potential translational steps that could be taken to investigate the role and importance of this phenomenon in disease and health in humans.

The drainage of a viscous gravity current into a deep porous medium driven by both the gravitational and capillary forces is considered in two steps. We first study the one-dimensional case where a layer of fluid drains vertically into an infinitely deep porous medium. We determine a transition from the capillary-driven regime to the gravity-driven regime as time proceeds. Second, we solve the coupled spreading and drainage problem. There are no self-similar solutions of the problem for the entire time period, so asymptotic analyses are developed for the height, depth and front location in both the early-time and the late-time periods. In addition, we present numerical results of the governing partial differential equations, which agree well with the self-similar solutions in the appropriate asymptotic limits.

Metal oxide-based transistors can be fabricated by low-cost, large-area solution processing methods, but involve a trade-off between low processing temperature, facile charge transport and high-capacitance/low-voltage transistor gates. We achieve these simultaneously by fabricating zinc oxide and sodium-incorporated alumina (SA) thin films with temperature not exceeding 200 to 250 °C using aqueous and combustion precursors, respectively. X-ray reflectivity shows a compositionally distinct SA boundary layer forming near the substrate and that a portion of the SA is chemically removed during the subsequent semiconductor deposition. Improved etch resistance and reduced dielectric leakage was obtained when (3-glycidoxypropyl) trimethoxysilane was included in the SA precursor.

In this paper, the kinematic workspace characteristics of a crab-like legged vehicle are investigated using a 2-D model. The alternative kinematic configurations and their corresponding workspace constraints are discussed, and the vehicle configuration of most interest identified. It is shown that, for constant vehicle body attitude, only two parameters affect the kinematic workspace, foot overlap and thigh length. Analytical methods for calculating the workspace characteristics are presented and, using these methods, the effects of the design geometry on the kinematic workspace are investigated.

Interest in the synthesis of semiconductor nanoparticles has been generated by their unusual optical and electronic properties arising from quantum confinement effects. We have synthesized silicon and germanium nanoclusters by reacting Zintl phase precursors with either silicon or germanium tetrachloride in various solvents. Strategies have been investigated to stabilize the surface, including reactions with RLi and MgBrR (R = alkyl). This synthetic method produces group IV nanocrystals with passivated surfaces. These nanoparticle emit over a very large range in the visible region. These particles have been characterized using HRTEM, FTIR, UV-Vis, solid state NMR, and fluorescence. The synthesis and characterization of these nanoclusters will be presented.