Case 10 : Supporting Students with Learning Disabilities: Reducing Secondary School Non-Completion and its Associated Health Disparities

Shelley van Dam needs to write a policy brief for her board on the effects of learning disabilities. This policy brief is a response to the upcoming termination of some of the funding for the Learning Disabilities Association of Sudbury (LDAS). The LDAS provides various programs that support students and families that have, or are in the process of being identified as having, a learning disability. The LDAS is a not-for-profit organization that operates throughout Greater Sudbury and across Northeastern Ontario to provide assistance to students who have learning disabilities so they attain the highest level of education possible. Some funding sources are available in the short term, including an Ontario Trillium Foundation grant that allows the LDAS to hire part-time employees in the organization’s satellite offices located in North Bay, Sault Ste. Marie, and Timmins. With this Ontario Trillium Foundation grant ending soon, Shelley realized that the LDAS would not be able to continue employing these people, and ultimately knew that the students in those regions who have learning disabilities would suffer. Shelley identified one possible solution that may help the organization avoid having to cease operations at their satellite offices—looking for more sustainable sources of funding from provincial ministries such as the Ministry of Children, Community and Social Services. In order to develop the appropriate policy brief required by her board, she needed to gather and present the most up-to-date information available. Students who have learning disabilities have average- to above-average intelligence but require different teaching methods in order to successfully complete school. Shelley found recent data from Statistics Canada (2015) showing the proportion of students who have learning disabilities dropping out of high school was 33% of total students with learning disabilities, compared with 13.1% of the total student population without learning disabilities. She wanted to discuss how socioeconomic status plays a role in one’s health and the importance of successfully obtaining at least a high school education, as that opens the door for other post-secondary educational opportunities. Graduating from high school is necessary in today’s job market because many jobs require post-secondary credentials as a prerequisite to employment. Shelley found research discussing how a lack of education for Canadians who have learning disabilities can fuel further inequities including food scarcity, incarceration and crime. The ecological systems theory, social learning theory, and strain theory were all discussed to better understand some underlying problems that may contribute to the continued, cyclical health disparities faced by Canadians who do not have a high school diploma. By including this information, Shelley hopes to highlight the effects of inadequate support for students who have learning disabilities throughout their academic careers, and how this can increase health care costs not only to the individual, but to society as a whole

Towards Efficient Detection of Small Near-Earth Asteroids Using the Zwicky Transient Facility (ZTF)

We describe ZStreak, a semi-real-time pipeline specialized in detecting small, fast-moving near-Earth asteroids (NEAs) that is currently operating on the data from the newly-commissioned Zwicky Transient Facility (ZTF) survey. Based on a prototype originally developed by Waszczak et al. (2017) for the Palomar Transient Factory (PTF), the predecessor of ZTF, ZStreak features an improved machine-learning model that can cope with the $10\times$ data rate increment between PTF and ZTF. Since its first discovery on 2018 February 5 (2018 CL), ZTF/ZStreak has discovered $45$ confirmed new NEAs over a total of 232 observable nights until 2018 December 31. Most of the discoveries are small NEAs, with diameters less than $\sim100$ m. By analyzing the discovery circumstances, we find that objects having the first to last detection time interval under 2 hr are at risk of being lost. We will further improve real-time follow-up capabilities, and work on suppressing false positives using deep learning.Comment: PASP in pres

p63 is an alternative p53 repressor in melanoma that confers chemoresistance and a poor prognosis.

The role of apoptosis in melanoma pathogenesis and chemoresistance is poorly characterized. Mutations in TP53 occur infrequently, yet the TP53 apoptotic pathway is often abrogated. This may result from alterations in TP53 family members, including the TP53 homologue TP63. Here we demonstrate that TP63 has an antiapoptotic role in melanoma and is responsible for mediating chemoresistance. Although p63 was not expressed in primary melanocytes, up-regulation of p63 mRNA and protein was observed in melanoma cell lines and clinical samples, providing the first evidence of significant p63 expression in this lineage. Upon genotoxic stress, endogenous p63 isoforms were stabilized in both nuclear and mitochondrial subcellular compartments. Our data provide evidence of a physiological interaction between p63 with p53 whereby translocation of p63 to the mitochondria occurred through a codependent process with p53, whereas accumulation of p53 in the nucleus was prevented by p63. Using RNA interference technology, both isoforms of p63 (TA and ΔNp63) were demonstrated to confer chemoresistance, revealing a novel oncogenic role for p63 in melanoma cells. Furthermore, expression of p63 in both primary and metastatic melanoma clinical samples significantly correlated with melanoma-specific deaths in these patients. Ultimately, these observations provide a possible explanation for abrogation of the p53-mediated apoptotic pathway in melanoma, implicating novel approaches aimed at sensitizing melanoma to therapeutic agents

Towards Efficient Detection of Small Near-Earth Asteroids Using the Zwicky Transient Facility (ZTF)

We describe ZStreak, a semi-real-time pipeline specialized in detecting small, fast-moving, near-Earth asteroids (NEAs), which is currently operating on the data from the newly commissioned Zwicky Transient Facility (ZTF) survey. Based on a prototype originally developed by Waszczak et al. (2017) for the Palomar Transient Factory (PTF), the predecessor of ZTF, ZStreak features an improved machine-learning model that can cope with the 10× data rate increment between PTF and ZTF. Since its first discovery on 2018 February 5 (2018 CL), ZTF/ZStreak has discovered 45 confirmed new NEAs over a total of 232 observable nights until 2018 December 31. Most of the discoveries are small NEAs, with diameters less than ~100 m. By analyzing the discovery circumstances, we find that objects having the first to last detection time interval under 2 hr are at risk of being lost. We will further improve real-time follow-up capabilities, and work on suppressing false positives using deep learning

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

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