Chain-of-Thought Embeddings for Stance Detection on Social Media

Stance detection on social media is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit stance labels. Chain-of-Thought (COT) prompting has recently been shown to improve performance on stance detection tasks -- alleviating some of these issues. However, COT prompting still struggles with implicit stance identification. This challenge arises because many samples are initially challenging to comprehend before a model becomes familiar with the slang and evolving knowledge related to different topics, all of which need to be acquired through the training data. In this study, we address this problem by introducing COT Embeddings which improve COT performance on stance detection tasks by embedding COT reasonings and integrating them into a traditional RoBERTa-based stance detection pipeline. Our analysis demonstrates that 1) text encoders can leverage COT reasonings with minor errors or hallucinations that would otherwise distort the COT output label. 2) Text encoders can overlook misleading COT reasoning when a sample's prediction heavily depends on domain-specific patterns. Our model achieves SOTA performance on multiple stance detection datasets collected from social media.Comment: Accepted at EMNLP-2023, 8 page

Theme-driven Keyphrase Extraction to Analyze Social Media Discourse

Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyphrase extraction is used to identify salient concepts in social media discourse without being constrained by predefined entity classes. This paper introduces a theme-driven keyphrase extraction framework tailored for social media, a pioneering approach designed to capture clinically relevant keyphrases from user-generated health texts. Themes are defined as broad categories determined by the objectives of the extraction task. We formulate this novel task of theme-driven keyphrase extraction and demonstrate its potential for efficiently mining social media text for the use case of treatment for opioid use disorder. This paper leverages qualitative and quantitative analysis to demonstrate the feasibility of extracting actionable insights from social media data and efficiently extracting keyphrases using minimally supervised NLP models. Our contributions include the development of a novel data collection and curation framework for theme-driven keyphrase extraction and the creation of MOUD-Keyphrase, the first dataset of its kind comprising human-annotated keyphrases from a Reddit community. We also identify the scope of minimally supervised NLP models to extract keyphrases from social media data efficiently. Lastly, we found that a large language model (ChatGPT) outperforms unsupervised keyphrase extraction models, and we evaluate its efficacy in this task.Comment: 11 pages, 2 figures, submitted to ICWSM. This version represents a substantial expansion and refocus of the previous manuscript, including new experiments, expanded data analysis, and comprehensive discussion

Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity

Amidst the sharp rise in the evaluation of large language models (LLMs) on various tasks, we find that semantic textual similarity (STS) has been under-explored. In this study, we show that STS can be cast as a text generation problem while maintaining strong performance on multiple STS benchmarks. Additionally, we show generative LLMs significantly outperform existing encoder-based STS models when characterizing the semantic similarity between two texts with complex semantic relationships dependent on world knowledge. We validate this claim by evaluating both generative LLMs and existing encoder-based STS models on three newly collected STS challenge sets which require world knowledge in the domains of Health, Politics, and Sports. All newly collected data is sourced from social media content posted after May 2023 to ensure the performance of closed-source models like ChatGPT cannot be credited to memorization. Our results show that, on average, generative LLMs outperform the best encoder-only baselines by an average of 22.3% on STS tasks requiring world knowledge. Our results suggest generative language models with STS-specific prompting strategies achieve state-of-the-art performance in complex, domain-specific STS tasks.Comment: Under review GEM@EMNLP-2023, 12 page

Differentiating SIADH from Cerebral/Renal Salt Wasting: Failure of the Volume Approach and Need for a New Approach to Hyponatremia

Hyponatremia is the most common electrolyte abnormality. Its diagnostic and therapeutic approaches are in a state of flux. It is evident that hyponatremic patients are symptomatic with a potential for serious consequences at sodium levels that were once considered trivial. The recommendation to treat virtually all hyponatremics exposes the need to resolve the diagnostic and therapeutic dilemma of deciding whether to water restrict a patient with the syndrome of inappropriate antidiuretic hormone secretion (SIADH) or administer salt and water to a renal salt waster. In this review, we briefly discuss the pathophysiology of SIADH and renal salt wasting (RSW), and the difficulty in differentiating SIADH from RSW, and review the origin of the perceived rarity of RSW, as well as the value of determining fractional excretion of urate (FEurate) in differentiating both syndromes, the high prevalence of RSW which highlights the inadequacy of the volume approach to hyponatremia, the importance of changing cerebral salt wasting to RSW, and the proposal to eliminate reset osmostat as a subtype of SIADH, and finally propose a new algorithm to replace the outmoded volume approach by highlighting FEurate. This algorithm eliminates the need to assess the volume status with less reliance on determining urine sodium concentration, plasma renin, aldosterone and atrial/brain natriuretic peptide or the BUN to creatinine ratio

The ProtekDuo® Cannula for Acute Mechanical Circulatory Support

The ProtekDuo® is a dual lumen cannula that can be used in numerous configurations to treat cardiogenic shock and hypotension. Its default function is as a temporary percutaneous right ventricular assist device (RVAD) system, however, other configurations both alone and with other mechanical circulatory support (MCS) devices have evolved. In addition to its use as a component of a ventricular assist device (VAD), it can be used as a cannula for extracorporeal membrane oxygenation (ECMO) and may serve as double lumen drainage cannula on cardiopulmonary bypass (CPB). The role of the cannula in ECMO has been described in multiple configurations including traditional veno-pulmonary (V-P) or “oxygenated RVAD” (oxyRVAD), veno-venopulmonary (V-VP), or venopulmonary-arterial (VP-A). This book chapter summarizes various configurations and technical aspects of the ProtekDuo(R) cannula in the management of hypotension and cardiogenic shock

Whole-Genome Sequencing and Concordance Between Antimicrobial Susceptibility Genotypes and Phenotypes of Bacterial Isolates Associated with Bovine Respiratory Disease.

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease-associated bacterial isolates (Histophilus somni, Mycoplasma bovis, Mannheimia haemolytica, and Pasteurella multocida) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni, M. haemolytica, and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% (P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes

Prevalence and Level of Enterohemorrhagic \u3ci\u3eEscherichia coli\u3c/i\u3e in Culled Dairy Cows at Harvest

The primary objective of this study was to determine the prevalence and level of enterohemorrhagic Escherichia coli (EHEC) O26, O45, O103, O111, O121, and O145 (collectively EHEC-6) plus EHEC O157 in fecal, hide, and preintervention carcass surface samples from culled dairy cows. Matched samples (n=300) were collected from 100 cows at harvest and tested by a culture-based method and two molecular methods: NeoSEEK STEC (NS) and Atlas STEC EG2 Combo. Both the culture and NS methods can be used to discriminate among the seven EHEC types (EHEC-7), from which the cumulative prevalence was inferred, whereas the Atlas method can discriminate only between EHEC O157 and non-O157 EHEC, without discrimination of the serogroup. The EHEC-7 prevalence in feces, hides, and carcass surfaces was 6.5, 15.6, and 1.0%, respectively, with the culture method and 25.9, 64.9, and 7.0%, respectively, with the NS method. With the Atlas method, the prevalence of non-O157 EHEC was 29.1, 38.3, and 28.0% and that of EHEC O157 was 29.1, 57.0, and 3.0% for feces, hides, and carcasses, respectively. Only two samples (a hide sample and a fecal sample) originating from different cows contained quantifiable EHEC. In both samples, the isolates were identified as EHEC O157, with 4.7 CFU/1,000 cm2 in the hide sample and 3.9 log CFU/g in the fecal sample. Moderate agreement was found between culture and NS results for detection of EHEC O26 (k=0.58,

Genomic Characterization of Novel Human Parechovirus Type

Using a simple metagenomic approach, we identified a divergent human parechovirus (HPeV) in the stool of a child in Pakistan. Genomic characterization showed this virus was distinct enough from reported HPeV types to qualify as candidate prototype for the seventh HPeV type

Serial prophylactic exchange blood transfusion in pregnant women with sickle cell disease (TAPS-2): study protocol for a randomised controlled feasibility trial.

BACKGROUND: Pregnancies in women with sickle cell disease (SCD) are associated with a higher risk of sickle and pregnancy complications. Limited options exist for treating SCD during pregnancy. Serial prophylactic exchange blood transfusion (SPEBT) has been shown to be effective in treating SCD outside pregnancy, but evidence is lacking regarding its use during pregnancy. The aim of this study is to assess the feasibility and acceptability of conducting a future phase 3 randomised controlled trial (RCT) to establish the clinical and cost effectiveness of SPEBT in pregnant women with SCD. METHODS: The study is an individually randomised, two-arm, feasibility trial with embedded qualitative and health economic studies. Fifty women, 18 years of age and older, with SCD and a singleton pregnancy at ≤ 18 weeks' gestation will be recruited from six hospitals in England. Randomisation will be conducted using a secure online database and minimised by centre, SCD genotype and maternal age. Women allocated to the intervention arm will receive SPEBT commencing at ≤ 18 weeks' gestation, performed using automated erythrocytapheresis every 6-10 weeks until the end of pregnancy, aiming to maintain HbS% or combined HbS/HbC% below 30%. Women in the standard care arm will only receive transfusion when clinically indicated. The primary outcome will be the recruitment rate. Additional endpoints include reasons for refusal to participate, attrition rate, protocol adherence, and maternal and neonatal outcomes. Women will be monitored throughout pregnancy to assess maternal, sickle, and foetal complications. Detailed information about adverse events (including hospital admission) and birth outcomes will be extracted from medical records and via interview at 6 weeks postpartum. An embedded qualitative study will consist of interviews with (a) 15-25 trial participants to assess experiences and acceptability, (b) 5-15 women who decline to participate to identify barriers to recruitment and (c) 15-20 clinical staff to explore fidelity and acceptability. A health economic study will inform a future cost effectiveness and cost-utility analysis. DISCUSSION: This feasibility study aims to rigorously evaluate SPEBT as a treatment for SCD in pregnancy and its impact on maternal and infant outcomes. TRIAL REGISTRATION: NIH registry (www.clinicaltrials.gov), registration number NCT03975894 (registered 05/06/19); ISRCTN (www.isrctn.com), registration number ISRCTN52684446 (retrospectively registered 02/08/19)

Making 'chemical cocktails' - evolution of urban geochemical processes across the periodic table of elements.

Urbanization contributes to the formation of novel elemental combinations and signatures in terrestrial and aquatic watersheds, also known as 'chemical cocktails.' The composition of chemical cocktails evolves across space and time due to: (1) elevated concentrations from anthropogenic sources, (2) accelerated weathering and corrosion of the built environment, (3) increased drainage density and intensification of urban water conveyance systems, and (4) enhanced rates of geochemical transformations due to changes in temperature, ionic strength, pH, and redox potentials. Characterizing chemical cocktails and underlying geochemical processes is necessary for: (1) tracking pollution sources using complex chemical mixtures instead of individual elements or compounds; (2) developing new strategies for co-managing groups of contaminants; (3) identifying proxies for predicting transport of chemical mixtures using continuous sensor data; and (4) determining whether interactive effects of chemical cocktails produce ecosystem-scale impacts greater than the sum of individual chemical stressors. First, we discuss some unique urban geochemical processes which form chemical cocktails, such as urban soil formation, human-accelerated weathering, urban acidification-alkalinization, and freshwater salinization syndrome. Second, we review and synthesize global patterns in concentrations of major ions, carbon and nutrients, and trace elements in urban streams across different world regions and make comparisons with reference conditions. In addition to our global analysis, we highlight examples from some watersheds in the Baltimore-Washington DC region, which show increased transport of major ions, trace metals, and nutrients across streams draining a well-defined land-use gradient. Urbanization increased the concentrations of multiple major and trace elements in streams draining human-dominated watersheds compared to reference conditions. Chemical cocktails of major and trace elements were formed over diurnal cycles coinciding with changes in streamflow, dissolved oxygen, pH, and other variables measured by high-frequency sensors. Some chemical cocktails of major and trace elements were also significantly related to specific conductance (p<0.05), which can be measured by sensors. Concentrations of major and trace elements increased, peaked, or decreased longitudinally along streams as watershed urbanization increased, which is consistent with distinct shifts in chemical mixtures upstream and downstream of other major cities in the world. Our global analysis of urban streams shows that concentrations of multiple elements along the Periodic Table significantly increase when compared with reference conditions. Furthermore, similar biogeochemical patterns and processes can be grouped among distinct mixtures of elements of major ions, dissolved organic matter, nutrients, and trace elements as chemical cocktails. Chemical cocktails form in urban waters over diurnal cycles, decades, and throughout drainage basins. We conclude our global review and synthesis by proposing strategies for monitoring and managing chemical cocktails using source control, ecosystem restoration, and green infrastructure. We discuss future research directions applying the watershed chemical cocktail approach to diagnose and manage environmental problems. Ultimately, a chemical cocktail approach targeting sources, transport, and transformations of different and distinct elemental combinations is necessary to more holistically monitor and manage the emerging impacts of chemical mixtures in the world's fresh waters