Association of inpatient use of angiotensin converting enzyme inhibitors and angiotensin II receptor blockers with mortality among patients with hypertension hospitalized with COVID-19

Rationale: Use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) is a major concern for clinicians treating coronavirus disease 2019 (COVID-19) in patients with hypertension. Objective: To determine the association between in-hospital use of ACEI/ARB and all-cause mortality in COVID-19 patients with hypertension. Methods and Results: This retrospective, multi-center study included 1128 adult patients with hypertension diagnosed with COVID-19, including 188 taking ACEI/ARB (ACEI/ARB group; median age 64 [IQR 55-68] years; 53.2% men) and 940 without using ACEI/ARB (non-ACEI/ARB group; median age 64 [IQR 57-69]; 53.5% men), who were admitted to nine hospitals in Hubei Province, China from December 31, 2019 to February 20, 2020. Unadjusted mortality rate was lower in the ACEI/ARB group versus the non-ACEI/ARB group (3.7% vs. 9.8%; P = 0.01). In mixed-effect Cox model treating site as a random effect, after adjusting for age, gender, comorbidities, and in-hospital medications, the detected risk for all-cause mortality was lower in the ACEI/ARB group versus the non-ACEI/ARB group (adjusted HR, 0.42; 95% CI, 0.19-0.92; P =0.03). In a propensity score-matched analysis followed by adjusting imbalanced variables in mixed-effect Cox model, the results consistently demonstrated lower risk of COVID-19 mortality in patients who received ACEI/ARB versus those who did not receive ACEI/ARB (adjusted HR, 0.37; 95% CI, 0.15-0.89; P = 0.03). Further subgroup propensity score-matched analysis indicated that, compared to use of other antihypertensive drugs, ACEI/ARB was also associated with decreased mortality (adjusted HR, 0.30; 95%CI, 0.12-0.70; P = 0.01) in COVID-19 patients with hypertension. Conclusions: Among hospitalized COVID-19 patients with hypertension, inpatient use of ACEI/ARB was associated with lower risk of all-cause mortality compared with ACEI/ARB non-users. While study interpretation needs to consider the potential for residual confounders, it is unlikely that in-hospital use of ACEI/ARB was associated with an increased mortality risk

Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.Comment: EMNLP 2020 (long

Sleep architecture, periodic breathing and mood disturbance of expeditioners at Kunlun Station (4087 m) in Antarctica

Several studies have reported the detrimental impacts of hypoxia exposure on sleep. Chinese Kunlun Station (altitude 4087 m) is located at Dome A, the highest point on the Antarctic ice sheet and one of the most extreme environments on Earth. This study investigated alteration of sleep, breathing and mood status in healthy expeditioners at Kunlun Station at Dome A. The study examined 10 male volunteers of the inland transverse party to Kunlun Station during the 31st Chinese National Antarctic Research Expedition, and valid data from eight volunteers were analyzed. Sleep structure, breathing pattern and mood were monitored using portable polysomnography (PSG) and profile of mood state (POMS) at two time points: (1) at Zhongshan Station (10 m) before departure to Kunlun Station; (2) on nights 12 –13 of residence at Kunlun Station. Slow-wave sleep (Stage 3 non-rapid eye movement) was markedly reduced at Kunlun Station (P < 0.01). Total sleep time, sleep efficiency and sleep latency showed no significant changes. Total respiratory events (P < 0.05), apnea/hypopnea index (AHI) (P < 0.05) and hypopnea index (P < 0.01) substantially increased at Kunlun Station. The most common respiratory disorder was periodic breathing, occurring almost exclusively during non-rapid eye movement sleep. The oxygen desaturation index increased markedly (P < 0.05), while nocturnal oxygen saturation dramatically fell at Kunlun Station (P < 0.05). Vigor scores decreased at Kunlun Station (P < 0.05). Expeditioners exhibited reduced slow wave sleep, induced periodic breathing, decreased oxygen saturation and decreased vigor at Kunlun Station

No evidence of brown adipose tissue activation after 24 weeks of supervised exercise training in young sedentary adults in the ACTIBATE randomized controlled trial

Exercise modulates both brown adipose tissue (BAT)metabolismand white adipose tissue (WAT) browning in murine models. Whether this is true in humans, however, has remained unknown. An unblinded randomized controlled trial (ClinicalTrials.gov ID: NCT02365129) was therefore conducted to study the effects of a 24-week supervised exercise intervention, combining endurance and resistance training, on BAT volume and activity (primary outcome). The study was carried out in the Sport and Health University Research Institute and the Virgen de las Nieves University Hospital of the University of Granada (Spain). One hundred and forty-five young sedentary adults were assigned to either (i) a control group (no exercise, n = 54), (ii) a moderate intensity exercise group (MOD-EX, n = 48), or (iii) a vigorous intensity exercise group (VIG-EX n = 43) by unrestricted randomization. No relevant adverse events were recorded. 97 participants (34 men, 63 women) were included in the final analysis (Control; n = 35, MOD-EX; n=31, and VIG-EX; n=31).We observed no changes in BAT volume (Δ Control: −22.2 ± 52.6ml; Δ MOD-EX: −15.5 ± 62.1ml, Δ VIG-EX: −6.8 ± 66.4 ml; P = 0.771) or 18F-fluorodeoxyglucose uptake (SUVpeak Δ Control: −2.6 ± 3.1ml; Δ MOD-EX: −1.2 ± 4.8, Δ VIG-EX: −2.2 ± 5.1; p = 0.476) in either the control or the exercise groups. Thus, we did not find any evidence of an exercise-induced change on BAT volume or activity in young sedentary adults.Spanish Government PI13/01393Retos de la Sociedad DEP2016-79512-R PTA-12264IEuropean CommissionSpanish Government FPU13/04365 FPU14/04172 FPU15/04059 FPU16/03653 FPU19/01609Consejo Nacional de Ciencia y Tecnologia (CONACyT) 440575Fundacion Iberoamericana de Nutricion (FINUT)Redes Tematicas de Investigacion Cooperativa RETIC Red SAMID RD16/0022AstraZenecaUniversity of Granada Plan Propio de Investigacion 2016 -Excellence actions: Unit of Excellence on Exercise and Health (UCEES)Plan Propio de Investigacion 2018 -Programa Contratos-PuentePrograma Perfecionamiento de DoctoresJunta de Andalucia Consejeria de Conocimiento, Investigacion y Universidades (ERDF) SOMM17/6107/UGRJunta de Andalucia P18-RT-4455Fundacion Alfonso Martin EscuderoMaria Zambrano fellowship by the Ministerio de Universidades y la Union Europea-NextGenerationEU RR_C_2021_04Novo Nordisk FoundationNovocure Limited NNF18OC003239

Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms

A Unified Generative Approach to Product Attribute-Value Identification

Product attribute-value identification (PAVI) has been studied to link products on e-commerce sites with their attribute values (e.g., <Material, Cotton>) using product text as clues. Technical demands from real-world e-commerce platforms require PAVI methods to handle unseen values, multi-attribute values, and canonicalized values, which are only partly addressed in existing extraction- and classification-based approaches. Motivated by this, we explore a generative approach to the PAVI task. We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text. Since the attribute value pairs are unordered set elements, how to linearize them will matter; we, thus, explore methods of composing an attribute-value pair and ordering the pairs for the task. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods on large-scale real-world datasets meant for those methods.Comment: Accepted to the Findings of ACL 202

Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly-available parallel corpora, and additionally mine 37.4 million sentence pairs from the web, resulting in a 4x increase. We mine the parallel sentences from the web by combining many corpora, tools, and methods: (a) web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar, which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at https://indicnlp.ai4bharat.org/samanantar/ and we hope they will help advance research in NMT and multilingual NLP for Indic languages.Comment: Accepted to the Transactions of the Association for Computational Linguistics (TACL