Report for the Urban Flooding Awareness Act

Illinois General AssemblyOpe

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

Big Rock and Welch Creek Flood Study: Kane County, IL

Big Rock and Welch Creeks are located in southwestern Kane County, Illinois. This area of Kane County is expected to experience development in the coming years; thus an accurate representation of local flood hazards is important. Regulatory floodplain maps now in effect for these streams show floodplain boundaries based on observations of flood events that occurred more than 30 years ago and lack engineering analyses that meet current standards and expectations. The purpose of this project is to better define flood hazards posed by streams in the Big Rock and Welch Creek watershed based on hydrologic and hydraulic analyses of existing conditions. Illinois State Water Survey (ISWS) staff worked with Kane County and community representatives to identify stream reaches for study and the level of study detail for each reach. Hydrologic and hydraulic analyses were conducted and used to delineate floodplain boundaries corresponding to the 1-percent-annual-chance flood, the base flood used by the Federal Emergency Management Agency (FEMA) for regulatory flood protection. Information was generated using spatial datasets and field data. Digital floodplain boundaries and attendant data are stored in the FEMA-prescribed Digital FIRM (DFIRM) database format for ready incorporation in the regulatory maps upon review and approval by FEMA. This study will provide information for floodplain management in both urban and rural areas.Kane County Department of Environmental Concernspublished or submitted for publicationis peer reviewe

Watershed-Specific Release Rate Analysis: Cook County, Illinois

Metropolitan Water Reclamation District of Greater Chicagopublished or submitted for publicationis peer reviewedOpe

Low copy numbers of complement and deficiency are risk factors for myositis, its subgroups and autoantibodies

BACKGROUND: Idiopathic inflammatory myopathies (IIM) are a group of autoimmune diseases characterised by myositis-related autoantibodies plus infiltration of leucocytes into muscles and/or the skin, leading to the destruction of blood vessels and muscle fibres, chronic weakness and fatigue. While complement-mediated destruction of capillary endothelia is implicated in paediatric and adult dermatomyositis, the complex diversity of complement in IIM pathology was unknown. METHODS: We elucidated the gene copy number (GCN) variations of total , and and in 1644 Caucasian patients with IIM, plus 3526 matched healthy controls using real-time PCR or Southern blot analyses. Plasma complement levels were determined by single radial immunodiffusion. RESULTS: The large study populations helped establish the distribution patterns of various GCN groups. Low GCNs of (=2+3) and deficiency (=0+1) were strongly correlated with increased risk of IIM with OR equalled to 2.58 (2.28-2.91), p=5.0×10 for , and 2.82 (2.48-3.21), p=7.0×10 for deficiency. Contingency and regression analyses showed that among patients with deficiency, the presence of became insignificant as a risk factor in IIM except for inclusion body myositis (IBM), by which 98.2% had with an OR of 11.02 (1.44-84.4). Intragroup analyses of patients with IIM for C4 protein levels and IIM-related autoantibodies showed that those with anti-Jo-1 or with anti-PM/Scl had significantly lower C4 plasma concentrations than those without these autoantibodies. CONCLUSIONS: deficiency is relevant in dermatomyositis, is important in IBM and both deficiency and contribute interactively to risk of polymyositis

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 science. © The Author(s) 2019. Published by Oxford University Press