Latinos in Massachusetts Public Schools: Framingham

This report provides a snapshot of current educational outcomes for Latino students in the city of Framingham. It is based on publicly available data from the Massachusetts Department of Elementary and Secondary Education (MADESE) that have been analyzed for the community by the Gastón Institute. Using the ethno-racial categories assigned by MADESE, the report focuses on demographic trends and the most recent educational outcomes of Latino students relative to other ethno-racial groups in the school district and to students statewide. The report has three sections: The first section illustrates the demographic shift occurring in the Framingham Public Schools. The number of White students in the district has been steadily declining, while the number of Latino and students has increased markedly. The second section compares the performance of Latino students in Framingham on the Massachusetts Comprehensive Assessment System (MCAS) tests with the performance of all students statewide and other ethno-racial groups in Framingham. While disparities remain, the achievement gap between Latino and White students has been shrinking substantially in recent years. Latino students in Framingham have made especially large improvements on the Grade 10 English Language Arts test. The third section shows Latino graduation, dropout, and college enrollment rates, relative to other students in the district and to all students statewide. Here too, the data show marked discrepancies between Latino and White students in Framingham

Dendrobeaniamine A, a new alkaloid from the Arctic marine bryozoan Dendrobeania murrayana

Chemistry guided purification of the organic extract of the Arctic marine bryozoan Dendrobeania murrayana yielded one new compound, Dendrobeaniamine A, and it was present in abundant amounts in the organic extract. Dendrobeaniamine A is a fusion of long chain aliphatic fatty acid and the cationic amino acid residue arginine.The bioactivity and natural function of this compound thus remain to be elucidated

Rural Life Census Data Center Newsletter: Private Industry Change in South Dakota

South Dakota leaders continue to emphasize the importance of economic development. One aspect of assessing economic development is private industry growth. The South Dakota Chamber of Commerce states:Economic development is not a choice but rather a necessity. Economies that are advancing create jobs with higher pay, offer people amenities and necessities, and provide the tax base for schools, infrastructure, and law enforcement plus many of the intangibles known as “quality of life.” (South Dakota Chamber of Commerce 2006). Private sector jobs make up a large percentage of South Dakota’s jobs. Capitalistic economies, like the United States’, depend on the private sector. The taxes paid by private sectors provide better services for all citizens (Stover, Lichty, and Stover 1999). We used Quarterly Workforce Indicator (QWI) data produced by the Census Bureau to assess the most current private sector figures and trends. Data for this report were obtained at http://lehd.did.census.gov/led/datatools/qwiapp.html. These data also give us insight into both where South Dakota’s private sector growth is occurring and where private sector growth lags

BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion

We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-trivial and we show how to do this for full bodies in motion. Specifically, our BEDLAM dataset contains monocular RGB videos with ground-truth 3D bodies in SMPL-X format. It includes a diversity of body shapes, motions, skin tones, hair, and clothing. The clothing is realistically simulated on the moving bodies using commercial clothing physics simulation. We render varying numbers of people in realistic scenes with varied lighting and camera motions. We then train various HPS regressors using BEDLAM and achieve state-of-the-art accuracy on real-image benchmarks despite training with synthetic data. We use BEDLAM to gain insights into what model design choices are important for accuracy. With good synthetic training data, we find that a basic method like HMR approaches the accuracy of the current SOTA method (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes. Additionally, we provide detailed information about our synthetic data generation pipeline, enabling others to generate their own datasets. See the project page: https://bedlam.is.tue.mpg.de/

R19. Investigation of Taste Masking Efficiency of Caffeine Citrate by Lipids Utilizing Hot Melt Extrusion Technology

Corresponding author (Pharmaceutics and Drug delivery): Priyanka Srinivasan, [email protected]://egrove.olemiss.edu/pharm_annual_posters/1018/thumbnail.jp

Recognizing and Extracting Cybersecurtity-relevant Entities from Text

Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong need to develop community-accessible datasets to train existing AI-based cybersecurity pipelines to efficiently and accurately extract meaningful insights from CTI. We have created an initial unstructured CTI corpus from a variety of open sources that we are using to train and test cybersecurity entity models using the spaCy framework and exploring self-learning methods to automatically recognize cybersecurity entities. We also describe methods to apply cybersecurity domain entity linking with existing world knowledge from Wikidata. Our future work will survey and test spaCy NLP tools and create methods for continuous integration of new information extracted from text

Human GUCY2C-Targeted Chimeric Antigen Receptor (CAR)-Expressing T Cells Eliminate Colorectal Cancer Metastases.

One major hurdle to the success of adoptive T-cell therapy is the identification of antigens that permit effective targeting of tumors in the absence of toxicities to essential organs. Previous work has demonstrated that T cells engineered to express chimeric antigen receptors (CAR-T cells) targeting the murine homolog of the colorectal cancer antigen GUCY2C treat established colorectal cancer metastases, without toxicity to the normal GUCY2C-expressing intestinal epithelium, reflecting structural compartmentalization of endogenous GUCY2C to apical membranes comprising the intestinal lumen. Here, we examined the utility of a human-specific, GUCY2C-directed single-chain variable fragment as the basis for a CAR construct targeting human GUCY2C-expressing metastases. Human GUCY2C-targeted murine CAR-T cells promoted antigen-dependent T-cell activation quantified by activation marker upregulation, cytokine production, and killing of GUCY2C-expressing, but not GUCY2C-deficient, cancer cells in vitro. GUCY2C CAR-T cells provided long-term protection against lung metastases of murine colorectal cancer cells engineered to express human GUCY2C in a syngeneic mouse model. GUCY2C murine CAR-T cells recognized and killed human colorectal cancer cells endogenously expressing GUCY2C, providing durable survival in a human xenograft model in immunodeficient mice. Thus, we have identified a human GUCY2C-specific CAR-T cell therapy approach that may be developed for the treatment of GUCY2C-expressing metastatic colorectal cancer

Purification and Identification of a 7.6-kDa Protein in Media Conditioned by Superinvasive Cancer Cells

Background: Selection of the human drug sensitive and invasive cell line (MDA-MB-435S-F) with the chemotherapeutic agent paclitaxel, resulted in the development of drug resistant cell lines displaying enhanced invasion-related characteristics. Materials and Methods: Serum-free conditioned media from the human cancer drug-sensitive and invasive cell line (MDA-MB-435S-F) and its paclitaxel-resistant superinvasive variant (MDA-MB-435S-F/Taxol10p4pSI) were analyzed using Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Results: A differentially expressed protein was observed at 7.6 kDa, which was 4-fold upregulated in MDA-MB-435S-F/Taxol10p4pSI. The differentially expressed protein was identified using matrix-assisted laser desorption ionization tandem time-of-flight mass spectrometry (MALDI-TOF/TOF MS), as a fragment of bovine transferrin. The transferrin receptor was also found to be overexpressed in the superinvasive cell line. Conclusion: Cleavage of serum proteins such as transferrin could provide a valuable source of markers for malignant tumours and could also play a role in aspects of cancer pathogenesis, such as tumour cachexia