Tumour necrosis factor blockade for the treatment of erosive osteoarthritis of the interphalangeal finger joints: a double blind, randomised trial on structure modification

Background Adalimumab blocks the action of tumor necrosis factor-alpha and reduces disease progression in rheumatoid arthritis and psoriatic arthritis. The effects of adalimumab in controlling progression of structural damage in erosive hand osteoarthritis (HOA) were assessed. Methods Sixty patients with erosive HOA on radiology received 40 mg adalimumab or placebo subcutaneously every two weeks during a 12-month randomized double-blind trial. Response was defined as the reduction in progression of structural damage according to the categorical anatomic phase scoring system. Furthermore, subchondral bone, bone plate erosion, and joint-space narrowing were scored according to the continuous Ghent University Score System (GUSS (TM)). Results The disease appeared to be active since 40.0% and 26,7% of patients out of the placebo and adalimumab group, respectively, showed at least one new interphalangeal (IP) joint that became erosive during the 12 months follow-up. These differences were not significant and the overall results showed no effect of adalimumab. Risk factors for progression were then identified and the presence of palpable soft tissue swelling at baseline was recognized as the strongest predictor for erosive progression. In this subpopulation at risk, statistically significant less erosive evolution on the radiological image (3.7%) was seen in the adalimumab treated group compared to the placebo group (14.5%) (P = 0.009). GUSSTM scoring confirmed a less rapid rate of mean increase in the erosion scores during the first 6 months of treatment in patients in adalimumab-treated patients. Conclusion Palpable soft tissue swelling in IP joints in patients with erosive HOA is a strong predictor for erosive progression. In these joints adalimumab significantly halted the progression of joint damage compared to placebo

DataVinci: Learning Syntactic and Semantic String Repairs

String data is common in real-world datasets: 67.6% of values in a sample of 1.8 million real Excel spreadsheets from the web were represented as text. Systems that successfully clean such string data can have a significant impact on real users. While prior work has explored errors in string data, proposed approaches have often been limited to error detection or require that the user provide annotations, examples, or constraints to fix the errors. Furthermore, these systems have focused independently on syntactic errors or semantic errors in strings, but ignore that strings often contain both syntactic and semantic substrings. We introduce DataVinci, a fully unsupervised string data error detection and repair system. DataVinci learns regular-expression-based patterns that cover a majority of values in a column and reports values that do not satisfy such patterns as data errors. DataVinci can automatically derive edits to the data error based on the majority patterns and constraints learned over other columns without the need for further user interaction. To handle strings with both syntactic and semantic substrings, DataVinci uses an LLM to abstract (and re-concretize) portions of strings that are semantic prior to learning majority patterns and deriving edits. Because not all data can result in majority patterns, DataVinci leverages execution information from an existing program (which reads the target data) to identify and correct data repairs that would not otherwise be identified. DataVinci outperforms 7 baselines on both error detection and repair when evaluated on 4 existing and new benchmarks.Comment: 13 page

Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example

Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actions such as "color red all entries in a column that are negative" or "bold all rows not containing error or failure." Unfortunately, users who want to exercise this functionality need to manually write these conditional formatting (CF) rules. We introduce CORNET, a system that automatically learns such conditional formatting rules from user examples. CORNET takes inspiration from inductive program synthesis and combines symbolic rule enumeration, based on semi-supervised clustering and iterative decision tree learning, with a neural ranker to produce accurate conditional formatting rules. In this demonstration, we show CORNET in action as a simple add-in to Microsoft Excel. After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.Comment: 4 Pages, VLDB 2023 Demonstration Trac

TSTR^\mathrm{R}: Target Similarity Tuning Meets the Real World

Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs) to improve performance. Its goal is to adapt a sentence embedding model to have the similarity between two NL inputs match the similarity between their associated code outputs. In this paper, we propose different methods to apply and improve TST in the real world. First, we replace the sentence transformer with embeddings from a larger model, which reduces sensitivity to the language distribution and thus provides more flexibility in synthetic generation of examples, and we train a tiny model that transforms these embeddings to a space where embedding similarity matches code similarity, which allows the model to remain a black box and only requires a few matrix multiplications at inference time. Second, we show how to efficiently select a smaller number of training examples to train the TST model. Third, we introduce a ranking-based evaluation for TST that does not require end-to-end code generation experiments, which can be expensive to perform.Comment: Accepted for EMNLP-Findings, 202

Differential mucosal expression of Th17-related genes between the inflamed colon and ileum of patients with inflammatory bowel disease

<p>Abstract</p> <p>Background</p> <p>Immunological and genetic findings implicate Th17 effector cytokines in the pathogenesis of inflammatory bowel disease (IBD). Expression of Th17 pathway-associated genes is mainly studied in colonic disease. The present study assessed the mRNA expression levels of Th17 effector cytokines (<it>IL17A</it>, <it>IL17F</it>, <it>IL21</it>, <it>IL22 </it>and <it>IL26</it>) and genes involved in differentiation (<it>IL6</it>, <it>IL1B</it>, <it>TGFB1</it>, <it>IL23A </it>and <it>STAT3</it>) and recruitment of Th17 cells (<it>CCR6 </it>and <it>CCL20</it>) by quantitative real-time PCR analysis of colonic and ileal biopsies from 22 healthy control subjects, 26 patients with Crohn's disease (CD) and 12 patients with ulcerative colitis (UC). Inflammation was quantified by measuring expression of the inflammatory mediators <it>IL8 </it>and <it>TNF</it>.</p> <p>Results</p> <p>Evaluation of mRNA expression levels in colonic and ileal control samples revealed that <it>TNF</it>, <it>TGFB1</it>, <it>STAT3 </it>and <it>CCR6 </it>were expressed at higher levels in the ileum than in the colon. Expression of all the Th17 pathway-associated genes was increased in inflamed colonic samples. The increased expression of these genes was predominantly observed in samples from UC patients and was associated with more intense inflammation. Although increased expression of <it>IL17A</it>, <it>IL17F</it>, <it>IL21 </it>and <it>IL26 </it>was detected in inflamed ileal samples, expression of the indispensable Th17 cell differentiation factors <it>TGFB1 </it>and <it>IL23A</it>, the signaling molecule <it>STAT3 </it>and the Th17 recruitment factors <it>CCR6 </it>and <it>CCL20 </it>were unchanged.</p> <p>Conclusions</p> <p>Our findings suggest that immune regulation is different in colonic and ileal disease, which might have important consequences for therapeutic intervention.</p

A multiparameter approach to monitor disease activity in collagen-induced arthritis

Introduction: Disease severity in collagen-induced arthritis (CIA) is commonly assessed by clinical scoring of paw swelling and histological examination of joints. Although this is an accurate approach, it is also labour-intensive and the application of less invasive and less time-consuming methods is of great interest. However, it is still unclear which of these methods represents the most discriminating measure of disease activity. Methods: We undertook a comparative analysis in which different measurements of inflammation and tissue damage in CIA were studied on an individual mouse level. We compared the current gold standard methods - clinical scoring and histological examination - with alternative methods based on scoring of X-ray or micro-computed tomography (CT) images and investigated the significance of systemically expressed proteins, involved in CIA pathogenesis, that have potential as biomarkers. Results: Linear regression analysis revealed a marked association of serum matrix metalloproteinase (MMP)-3 levels with all features of CIA including inflammation, cartilage destruction and bone erosions. This association was improved by combined detection of MMP-3 and anti-collagen IgG2a antibody concentrations. In addition, combined analysis of both X-ray and micro-CT images was found to be predictive for cartilage and bone damage. Most remarkably, validation analysis using an independent data set proved that variations in disease severity, induced by different therapies, could be accurately represented by predicted values based on the proposed parameters. Conclusions: Our analyses revealed that clinical scoring, combined with serum MMP-3, anti-collagen IgG2a measurement and scoring of X-ray and micro-CT images, yields a comprehensive insight into the different aspects of disease activity in CIA

Towards automated relational data wrangling

It is well-known in data science that 80% of the work is devoted to preprocessing and only 20% to the actual machine learning or data mining step. This motivates us to explore different ways to (help) automate that preprocessing step. This note focusses on the question whether it is possible to (help) automate the data wrangling process for tabular data in data science.status: publishe