What Is The Impact Of Collaborative Exams On Learning And Attitudes In Introductory Astronomy Classes?

We present results of a two-semester study to gauge the impact of collaborative two-stage exams on student learning and attitudes in university-level introductory astronomy classes for non-science majors. In the collaborative two-stage exam setting, students first completed an exam individually, and then they reconsidered a subset of exam questions within their previously established groups, discussing the questions with their peers to arrive at a common answer.Students took three to four exams during the semester using this format. At mid-semester, we surveyed the students to gauge their attitudes about collaborative work and its perceived influence on their exam preparation and performance. At the end of the semester, students sat an individual-only final exam, which contained all previous collaborative-phase questions, as well as a subset of questions seen only on the individual portions of the exams. When we compare the normalized gain on final exam questions that were included in the collaborative portions to that on questions found in only the individual portions, we find higher normalized gains in general for questions encountered on the collaborative portions of the exams. These gains are accompanied by a statistically significant effect size (Cohen’s d). We note, however, that this improved performance appears to be dependent upon several factors. Those factors might include diminished retention over time, the assessment of overly complex concepts, and concept saturation. Our mid-semester survey indicates that the collaborative experience appears have a positive influence on their overall attitudes and their study habits

Generative Benchmark Creation for Table Union Search

Data management has traditionally relied on synthetic data generators to generate structured benchmarks, like the TPC suite, where we can control important parameters like data size and its distribution precisely. These benchmarks were central to the success and adoption of database management systems. But more and more, data management problems are of a semantic nature. An important example is finding tables that can be unioned. While any two tables with the same cardinality can be unioned, table union search is the problem of finding tables whose union is semantically coherent. Semantic problems cannot be benchmarked using synthetic data. Our current methods for creating benchmarks involve the manual curation and labeling of real data. These methods are not robust or scalable and perhaps more importantly, it is not clear how robust the created benchmarks are. We propose to use generative AI models to create structured data benchmarks for table union search. We present a novel method for using generative models to create tables with specified properties. Using this method, we create a new benchmark containing pairs of tables that are both unionable and non-unionable but related. We thoroughly evaluate recent existing table union search methods over existing benchmarks and our new benchmark. We also present and evaluate a new table search methods based on recent large language models over all benchmarks. We show that the new benchmark is more challenging for all methods than hand-curated benchmarks, specifically, the top-performing method achieves a Mean Average Precision of around 60%, over 30% less than its performance on existing manually created benchmarks. We examine why this is the case and show that the new benchmark permits more detailed analysis of methods, including a study of both false positives and false negatives that were not possible with existing benchmarks

Brucella abortus Infection of Placental Trophoblasts Triggers Endoplasmic Reticulum Stress-Mediated Cell Death and Fetal Loss via Type IV Secretion System-Dependent Activation of CHOP.

Subversion of endoplasmic reticulum (ER) function is a feature shared by multiple intracellular bacteria and viruses, and in many cases this disruption of cellular function activates pathways of the unfolded protein response (UPR). In the case of infection with Brucella abortus, the etiologic agent of brucellosis, the unfolded protein response in the infected placenta contributes to placentitis and abortion, leading to pathogen transmission. Here we show that B. abortus infection of pregnant mice led to death of infected placental trophoblasts in a manner that depended on the VirB type IV secretion system (T4SS) and its effector VceC. The trophoblast death program required the ER stress-induced transcription factor CHOP. While NOD1/NOD2 expression in macrophages contributed to ER stress-induced inflammation, these receptors did not play a role in trophoblast death. Both placentitis and abortion were independent of apoptosis-associated Speck-like protein containing a caspase activation and recruitment domain (ASC). These studies show that B. abortus uses its T4SS to induce cell-type-specific responses to ER stress in trophoblasts that trigger placental inflammation and abortion. Our results suggest further that in B. abortus the T4SS and its effectors are under selection as bacterial transmission factors.IMPORTANCE Brucella abortus infects the placenta of pregnant cows, where it replicates to high levels and triggers abortion of the calf. The aborted material is highly infectious and transmits infection to both cows and humans, but very little is known about how B. abortus causes abortion. By studying this infection in pregnant mice, we discovered that B. abortus kills trophoblasts, which are important cells for maintaining pregnancy. This killing required an injected bacterial protein (VceC) that triggered an endoplasmic reticulum (ER) stress response in the trophoblast. By inhibiting ER stress or infecting mice that lack CHOP, a protein induced by ER stress, we could prevent death of trophoblasts, reduce inflammation, and increase the viability of the pups. Our results suggest that B. abortus injects VceC into placental trophoblasts to promote its transmission by abortion

Modeling the execution semantics of stream processing engines with SECRET

There are many academic and commercial stream processing engines (SPEs) today, each of them with its own execution semantics. This variation may lead to seemingly inexplicable differences in query results. In this paper, we present SECRET, a model of the behavior of SPEs. SECRET is a descriptive model that allows users to analyze the behavior of systems and understand the results of window-based queries (with time- and tuple-based windows) for a broad range of heterogeneous SPEs. The model is the result of extensive analysis and experimentation with several commercial and academic engines. In the paper, we describe the types of heterogeneity found in existing engines and show with experiments on real systems that our model can explain the key differences in windowing behavio

Local Embeddings for Relational Data Integration

Deep learning based techniques have been recently used with promising results for data integration problems. Some methods directly use pre-trained embeddings that were trained on a large corpus such as Wikipedia. However, they may not always be an appropriate choice for enterprise datasets with custom vocabulary. Other methods adapt techniques from natural language processing to obtain embeddings for the enterprise's relational data. However, this approach blindly treats a tuple as a sentence, thus losing a large amount of contextual information present in the tuple. We propose algorithms for obtaining local embeddings that are effective for data integration tasks on relational databases. We make four major contributions. First, we describe a compact graph-based representation that allows the specification of a rich set of relationships inherent in the relational world. Second, we propose how to derive sentences from such a graph that effectively "describe" the similarity across elements (tokens, attributes, rows) in the two datasets. The embeddings are learned based on such sentences. Third, we propose effective optimization to improve the quality of the learned embeddings and the performance of integration tasks. Finally, we propose a diverse collection of criteria to evaluate relational embeddings and perform an extensive set of experiments validating them against multiple baseline methods. Our experiments show that our framework, EmbDI, produces meaningful results for data integration tasks such as schema matching and entity resolution both in supervised and unsupervised settings.Comment: Accepted to SIGMOD 2020 as Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks. Code can be found at https://gitlab.eurecom.fr/cappuzzo/embd

Identification of a Putative Salmonella enterica Serotype Typhimurium Host Range Factor with Homology to IpaH and YopM by Signature-Tagged Mutagenesis

The genetic basis for the host adaptation of Salmonella serotypes is currently unknown. We have explored a new strategy to identify Salmonella enterica serotype Typhimurium (S. typhimurium) genes involved in host adaptation, by comparing the virulence of 260 randomly generated signature-tagged mutants during the oral infection of mice and calves. This screen identified four mutants, which were defective for colonization of only one of the two host species tested. One mutant, which only displayed a colonization defect during the infection of mice, was further characterized. During competitive infection experiments performed with the S. typhimurium wild type, the mutant was defective for colonization of murine Peyer's patches but colonized bovine Peyer's patches at the wild-type level. No difference in virulence between wild type and mutant was observed when calves were infected orally with 10(10) CFU/animal. In contrast, the mutant possessed a sixfold increase in 50% lethal morbidity dose when mice were infected orally. The transposon in this mutant was inserted in a 2.9-kb pathogenicity islet, which is located between uvrB and yphK on the S. typhimurium chromosome. This pathogenicity islet contained a single gene, termed slrP, with homology to ipaH of Shigella flexneri and yopM of Yersinia pestis. These data show that comparative screening of signature-tagged mutants in two animal species can be used for scanning the S. typhimurium genome for genes involved in host adaptation

Evaluation of the Edinburgh Post Natal Depression Scale using Rasch analysis

BACKGROUND: The Edinburgh Postnatal Depression Scale (EPDS) is a 10 item self-rating post-natal depression scale which has seen widespread use in epidemiological and clinical studies. Concern has been raised over the validity of the EPDS as a single summed scale, with suggestions that it measures two separate aspects, one of depressive feelings, the other of anxiety. METHODS: As part of a larger cross-sectional study conducted in Melbourne, Australia, a community sample (324 women, ranging in age from 18 to 44 years: mean = 32 yrs, SD = 4.6), was obtained by inviting primiparous women to participate voluntarily in this study. Data from the EPDS were fitted to the Rasch measurement model and tested for appropriate category ordering, for item bias through Differential Item Functioning (DIF) analysis, and for unidimensionality through tests of the assumption of local independence. RESULTS: Rasch analysis of the data from the ten item scale initially demonstrated a lack of fit to the model with a significant Item-Trait Interaction total chi-square (chi Square = 82.8, df = 40; p < .001). Removal of two items (items 7 and 8) resulted in a non-significant Item-Trait Interaction total chi-square with a residual mean value for items of -0.467 with a standard deviation of 0.850, showing fit to the model. No DIF existed in the final 8-item scale (EPDS-8) and all items showed fit to model expectations. Principal Components Analysis of the residuals supported the local independence assumption, and unidimensionality of the revised EPDS-8 scale. Revised cut points were identified for EPDS-8 to maintain the case identification of the original scale. CONCLUSION: The results of this study suggest that EPDS, in its original 10 item form, is not a viable scale for the unidimensional measurement of depression. Rasch analysis suggests that a revised eight item version (EPDS-8) would provide a more psychometrically robust scale. The revised cut points of 7/8 and 9/10 for the EPDS-8 show high levels of agreement with the original case identification for the EPDS-10