Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference

While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs. For example, a state-of-the-art Macaw question-answering (QA) model answers 'Yes' to 'Is a sparrow a bird?' and 'Does a bird have feet?' but answers 'No' to 'Does a sparrow have feet?'. To address this failure mode, we propose a framework, Consistency Correction through Relation Detection, or ConCoRD, for boosting the consistency and accuracy of pre-trained NLP models using pre-trained natural language inference (NLI) models without fine-tuning or re-training. Given a batch of test inputs, ConCoRD samples several candidate outputs for each input and instantiates a factor graph that accounts for both the model's belief about the likelihood of each answer choice in isolation and the NLI model's beliefs about pair-wise answer choice compatibility. We show that a weighted MaxSAT solver can efficiently compute high-quality answer choices under this factor graph, improving over the raw model's predictions. Our experiments demonstrate that ConCoRD consistently boosts accuracy and consistency of off-the-shelf closed-book QA and VQA models using off-the-shelf NLI models, notably increasing accuracy of LXMERT on ConVQA by 5% absolute. See https://ericmitchell.ai/emnlp-2022-concord/ for code and data.Comment: 16 pages. EMNLP 2022 Camera Ready. See https://ericmitchell.ai/emnlp-2022-concord/ for code and dat

Neighborhood regulation by lncRNA promoters, transcription, and splicing

Mammalian genomes are pervasively transcribed to produce thousands of spliced long noncoding RNAs (lncRNAs), whose functions remain poorly understood. Because recent evidence has implicated several specific lncRNA loci in the local regulation of gene expression, we sought to determine whether such local regulation is a property of many lncRNA loci. We used genetic manipulations to dissect 12 genomic loci that produce lncRNAs and found that 5 of these loci influence the expression of a neighboring gene in cis. Surprisingly, however, none of these effects required the specific lncRNA transcripts themselves and instead involved general processes associated with their production, including enhancer-like activity of gene promoters, the process of transcription, and the splicing of the transcript. Interestingly, such effects are not limited to lncRNA loci: we found similar effects on local gene expression at 4 of 6 protein-coding loci. These results demonstrate that 'crosstalk' among neighboring genes is a prevalent phenomenon that can involve multiple mechanisms and cis regulatory signals, including a novel role for RNA splicing. These mechanisms may explain the function and evolution of some genomic loci that produce lncRNAs

Improving the Representativeness of Behavioral and Clinical Surveillance for Persons with HIV in the United States: The Rationale for Developing a Population-Based Approach

The need for a new surveillance approach to understand the clinical outcomes and behaviors of people in care for HIV evolved from the new challenges for monitoring clinical outcomes in the HAART era, the impact of the epidemic on an increasing number of areas in the US, and the need for representative data to describe the epidemic and related resource utilization and needs. The Institute of Medicine recommended that the Centers for Disease Control and Prevention and the Heath Resources and Services Administration coordinate efforts to survey a random sample of HIV-infected persons in care, in order to more accurately measure the need for prevention and care services. The Medical Monitoring Project (MMP) was created to meet these needs. This manuscript describes the evolution and design of MMP, a new nationally representative clinical outcomes and behavioral surveillance system, and describes how MMP data will be used locally and nationally to identify care and treatment utilization needs, and to plan for prevention interventions and services

Standard Definitions and Common Data Elements for Clinical Trials in Patients With Alcoholic Hepatitis: Recommendation From the NIAAA Alcoholic Hepatitis Consortia

Heavy drinkers are at risk for a spectrum of histologic alcohol-related liver injury: steatosis, alcoholic steatohepatitis (ASH), alcohol-related fibrosis, and cirrhosis. Alcoholic hepatitis (AH), the clinical entity associated with severe ASH, has high short-term mortality. The standard-of-care therapy, prednisolone, has limited efficacy and many side effects; no other treatment has consistently shown survival benefit. The National Institute on Alcohol Abuse and Alcoholism (NIAAA)-funded Alcoholic Hepatitis Consortia carry out translational research on pathophysiologic mechanisms, genetic and environmental risk factors, phase II clinical trials, and development of biomarkers. The consortia members were convened by the National Institutes of Health to address diagnostic criteria and practical issues related to clinical AH research, and to develop a set of common data elements to harmonize ongoing and future trials. This was accomplished through 3 face-to-face meetings of the investigators and representatives of the National Institutes of Health, and subsequent electronic communications over the course of 6 months. Evidence for the recommendations was based on published trials and observational data from several of the consortia members. A draft manuscript was iteratively reviewed by members of the consortia. The goal was to reach agreements on recommendations and definitions that could facilitate trial design, and simultaneously be tested by research groups pooling their data. The recommendations made here are specifically directed to achieve better uniformity in clinical trials, rather than serving as clinical practice guidelines

RNA-RNA Interactions Enable Specific Targeting of Noncoding RNAs to Nascent Pre-mRNAs and Chromatin Sites

Intermolecular RNA-RNA interactions are used by many noncoding RNAs (ncRNAs) to achieve their diverse functions. To identify these contacts, we developed a method based on RNA antisense purification to systematically map RNA-RNA interactions (RAP-RNA) and applied it to investigate two ncRNAs implicated in RNA processing: U1 small nuclear RNA, a component of the spliceosome, and Malat1, a large ncRNA that localizes to nuclear speckles. U1 and Malat1 interact with nascent transcripts through distinct targeting mechanisms. Using differential crosslinking, we confirmed that U1 directly hybridizes to 5′ splice sites and 5′ splice site motifs throughout introns and found that Malat1 interacts with pre-mRNAs indirectly through protein intermediates. Interactions with nascent pre-mRNAs cause U1 and Malat1 to localize proximally to chromatin at active genes, demonstrating that ncRNAs can use RNA-RNA interactions to target specific pre-mRNAs and genomic sites. RAP-RNA is sensitive to lower abundance RNAs as well, making it generally applicable for investigating ncRNAs

Verification of high-level transformations with inductive refinement types

International audienceHigh-level transformation languages like Rascal include expressive features for manipulating large abstract syntax trees: first-class traversals, expressive pattern matching, backtrack-ing and generalized iterators. We present the design and implementation of an abstract interpretation tool, Rabit, for verifying inductive type and shape properties for transformations written in such languages. We describe how to perform abstract interpretation based on operational semantics, specifically focusing on the challenges arising when analyzing the expressive traversals and pattern matching. Finally, we evaluate Rabit on a series of transformations (normaliza-tion, desugaring, refactoring, code generators, type inference, etc.) showing that we can effectively verify stated properties. CCS Concepts • Software and its engineering → General programming languages; • Social and professional topics → History of programming languages

The incidence of liver injury in Uyghur patients treated for TB in Xinjiang Uyghur autonomous region, China, and its association with hepatic enzyme polymorphisms nat2, cyp2e1, gstm1 and gstt1.

BACKGROUND AND OBJECTIVE: Of three first-line anti-tuberculosis (anti-TB) drugs, isoniazid is most commonly associated with hepatotoxicity. Differences in INH-induced toxicity have been attributed to genetic variability at several loci, NAT2, CYP2E1, GSTM1and GSTT1, that code for drug-metabolizing enzymes. This study evaluated whether the polymorphisms in these enzymes were associated with an increased risk of anti-TB drug-induced hepatitis in patients and could potentially be used to identify patients at risk of liver injury. METHODS AND DESIGN: In a cross-sectional study, 2244 tuberculosis patients were assessed two months after the start of treatment. Anti-TB drug-induced liver injury (ATLI) was defined as an ALT, AST or bilirubin value more than twice the upper limit of normal. NAT2, CYP2E1, GSTM1 and GSTT1 genotypes were determined using the PCR/ligase detection reaction assays. RESULTS: 2244 patients were evaluated, there were 89 cases of ATLI, a prevalence of 4% 9 patients (0.4%) had ALT levels more than 5 times the upper limit of normal. The prevalence of ATLI was greater among men than women, and there was a weak association with NAT2*5 genotypes, with ATLI more common among patients with the NAT2*5*CT genotype. The sensitivity of the CT genotype for identifying patients with ATLI was 42% and the positive predictive value 5.9%. CT ATLI was more common among slow acetylators (prevalence ratio 2.0 (95% CI 0.95,4.20) )compared to rapid acetylators. There was no evidence that ATLI was associated with CYP2E1 RsaIc1/c1genotype, CYP2E1 RsaIc1/c2 or c2/c2 genotypes, or GSTM1/GSTT1 null genotypes. CONCLUSIONS: In Xinjiang Uyghur TB patients, liver injury was associated with the genetic variant NAT2*5, however the genetic markers studied are unlikely to be useful for screening patients due to the low sensitivity and low positive predictive values for identifying persons at risk of liver injury