Indian Summer Monsoon variations and competing influences between hemispheres since ~35 ka recorded in Tengchongqinghai Lake, southwest China

The southwestern Yunnan Province of China, which is located at the southeastern margin of the Tibetan Plateau and close to Bay of Bengal, is significantly influenced by the Indian Summer Monsoon (ISM). In this study, we reconstruct proxies for the ISM from 35 to 1 ka through detailed analysis of grain-size distribution, geochemical composition and environmental magnetism from a 7.96 m sediment core from Tengchongqinghai Lake, Yunnan Province, China. Globally recognized, abrupt climatic events, including Heinrich Events 0–3 (H0−H3) and the Bølling-Allerød (B/A) warm period are identified in most of our proxies, and the long-term trend is consistent with other published records such as stalagmite oxygen isotopes (δ18O) from Sangxing Cave. Northern Hemisphere (NH) temperature, which is influenced by NH solar insolation, is commonly suggested to play a dominant role in controlling the ISM. A comparison of our record with the δ18O variations of ice cores from Greenland and Antarctica, a sea surface temperature (SST) record from the Bay of Bengal, and summer solar insolation at 25°N latitude demonstrates that the general pattern of ISM change does follow variations in summer insolation; however, the ISM lags summer insolation by thousands of years. While the ISM fluctuations are highly correlated with NH temperature on shorter timescales (centennial-millennial), the gradually weakened ISM from 22.5 ka until the Last Glacial Maximum (LGM) indicates a close relationship with the rise of Southern Hemisphere (SH) temperature and the relatively cold background of the SH. Our record expands on the findings of ISM records from Heqing paleolake basin in southwestern China and the Arabian Sea sediments, suggesting that the NH and SH have a competitive influence on ISM by controlling the cross-equatorial pressure gradient. This relationship means that when NH temperatures are relatively high, it has a stronger influence on the ISM than SH influences. In contrast, when the SH temperature is relatively low, it has a dominant influence on ISM. In addition, we speculate that the change of SH temperature not only influences the cross-equatorial pressure gradient directly, but also likely modulates the circulation system of ocean energy by influencing the Atlantic Meridional Overturning Circulation (AMOC)

Change Profiles and Functional Targets of MicroRNAs in Type 2 Diabetes Mellitus Patients with Obesity

Background MicroRNAs (miRNAs) exert an essential contribution to obesity and type 2 diabetes mellitus (T2DM). This study aimed to investigate the differences of miRNAs in the presence and absence of T2DM in patients with obesity, as well as before and after bariatric surgery in T2DM patients with obesity. Characterization of the common changes in both was further analyzed. Methods We enrolled 15 patients with obesity but without T2DM and 15 patients with both obesity and T2DM. Their preoperative clinical data and serum samples were collected, as well as 1 month after bariatric surgery. The serum samples were analyzed by miRNA sequencing, and the miRNAs profiles and target genes characteristics were compared. Results Patients with T2DM had 16 up-regulated and 32 down-regulated miRNAs compared to patients without T2DM. Improvement in metabolic metrics after bariatric surgery of T2DM patients with obesity was correlated with changes in miRNAs, as evidenced by the upregulation of 20 miRNAs and the downregulation of 30 miRNAs. Analysis of the two miRNAs profiles identified seven intersecting miRNAs that showed opposite changes. The target genes of these seven miRNAs were substantially enriched in terms or pathways associated with T2DM. Conclusion We determined the expression profiles of miRNAs in the obese population, with and without diabetes, before and after bariatric surgery. The miRNAs that intersected in the two comparisons were discovered. Both the miRNAs discovered and their target genes were closely associated with T2DM, demonstrating that they might be potential targets for the regulation of T2DM

Flexible but Refractory Single-Crystalline Hyperbolic Metamaterials

The fabrication of flexible single-crystalline plasmonic or photonic components in a scalable way is fundamentally important to flexible electronic and photonic devices with high speed, high energy efficiency, and high reliability. However, it remains to be a big challenge so far. Here, we have successfully synthesized flexible single-crystalline optical hyperbolic metamaterials by directly depositing refractory nitride superlattices on flexible fluoro phlogopite-mica substrates with magnetron sputtering. Interestingly, these flexible hyperbolic metamaterials show dual-band hyperbolic dispersion of dielectric constants with low dielectric losses and high figure-of-merit in the visible to near-infrared ranges. More importantly, the optical properties of these nitride-based flexible hyperbolic metamaterials show remarkable stability under either heating or bending. Therefore, the strategy developed in this work offers an easy and scalable route to fabricate flexible, high-performance, and refractory plasmonic or photonic components, which can significantly expand the applications of current electronic and photonic devices.Comment: 15 page

Possible Luttinger liquid behavior of edge transport in monolayer transition metal dichalcogenide crystals.

In atomically-thin two-dimensional (2D) semiconductors, the nonuniformity in current flow due to its edge states may alter and even dictate the charge transport properties of the entire device. However, the influence of the edge states on electrical transport in 2D materials has not been sufficiently explored to date. Here, we systematically quantify the edge state contribution to electrical transport in monolayer MoS2/WSe2 field-effect transistors, revealing that the charge transport at low temperature is dominated by the edge conduction with the nonlinear behavior. The metallic edge states are revealed by scanning probe microscopy, scanning Kelvin probe force microscopy and first-principle calculations. Further analyses demonstrate that the edge-state dominated nonlinear transport shows a universal power-law scaling relationship with both temperature and bias voltage, which can be well explained by the 1D Luttinger liquid theory. These findings demonstrate the Luttinger liquid behavior in 2D materials and offer important insights into designing 2D electronics

Evaluation of the efficacy and safety of intravenous remdesivir in adult patients with severe COVID-19: study protocol for a phase 3 randomized, double-blind, placebo-controlled, multicentre trial.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by a novel corinavirus (later named SARS-CoV-2 virus), was fistly reported in Wuhan, Hubei Province, China towards the end of 2019. Large-scale spread within China and internationally led the World Health Organization to declare a Public Health Emergency of International Concern on 30th January 2020. The clinical manifestations of COVID-19 virus infection include asymptomatic infection, mild upper respiratory symptoms, severe viral pneumonia with respiratory failure, and even death. There are no antivirals of proven clinical efficacy in coronavirus infections. Remdesivir (GS-5734), a nucleoside analogue, has inhibitory effects on animal and human highly pathogenic coronaviruses, including MERS-CoV and SARS-CoV, in in vitro and in vivo experiments. It is also inhibitory against the COVID-19 virus in vitro. The aim of this study is to assess the efficacy and safety of remdesivir in adult patients with severe COVID-19. METHODS: The protocol is prepared in accordance with the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidelines. This is a phase 3, randomized, double-blind, placebo-controlled, multicentre trial. Adults (≥ 18 years) with laboratory-confirmed COVID-19 virus infection, severe pneumonia signs or symptoms, and radiologically confirmed severe pneumonia are randomly assigned in a 2:1 ratio to intravenously administered remdesivir or placebo for 10 days. The primary endpoint is time to clinical improvement (censored at day 28), defined as the time (in days) from randomization of study treatment (remdesivir or placebo) until a decline of two categories on a six-category ordinal scale of clinical status (1 = discharged; 6 = death) or live discharge from hospital. One interim analysis for efficacy and futility will be conducted once half of the total number of events required has been observed. DISCUSSION: This is the first randomized, placebo-controlled trial in COVID-19. Enrolment began in sites in Wuhan, Hubei Province, China on 6th February 2020. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04257656. Registered on 6 February 2020

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; \u3c37 \u3eweeks) or (2) early preterm birth (ePTB; \u3c32 \u3eweeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth

Probing NaCl hydrate formation from aqueous solutions by Terahertz Time-Domain Spectroscopy

The cooling-induced formation of hydrate in aqueous NaCl solutions was probed using terahertz time-domain spectroscopy (THz-TDS). It was found that the NaCl hydrate formation is accompanied with emergence of four new absorption peaks at 1.60, 2.43, 3.34 and 3.78 THz. Combining the X-ray diffraction measurement with the solid-state based density functional theory (DFT) calculations, we assign the observed terahertz absorption peaks to the vibrational modes of the formed NaCl⋅2H2O hydrate during cooling. This work dedicates THz-TDS based analysis great potential in studying ionic hydrate and the newly revealed collective vibrational modes could be the sensitive indicators to achieve quantitative analysis in phase transitions and lattice dynamics

A continuously benchmarked and crowdsourced challenge for rapid development and evaluation of models to predict COVID-19 diagnosis and hospitalization

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models