IceTFT v 1.0.0: Interpretable Long-Term Prediction of Arctic Sea Ice Extent with Deep Learning

Annual reductions in Arctic sea ice extent (SIE) due to global warming. According to International Panel on Climate Change (IPCC) climate model projections, the summer Arctic will be nearly sea ice free in the 50s of the 21st century, resulting in sea level rise and thus affecting human life. Therefore, it is important to predict SIE accurately. For the most current studies, the majority of deep learning-based SIE prediction models focus on single-step prediction, and they not only have short lead times but also have limited forecasting skills. In addition, these models often lack interpretability. In this study paper, we construct the Ice Temporal Fusion Transformer (IceTFT) model, which consists mainly of the variable selection network (VSN), the long short-term memory (LSTM) encoder, and multi-headed attention mechanism. Then we select 11 predictors for IceTFT model, including SIE, atmospheric, and ocean variables according to the physical mechanisms influencing sea ice development. And the VSN in IceTFT can automatically adjust the weights of predictors and filter spuriously correlated variables. We also evaluate the IceTFT model from the division of the training set, the slicing methods of input data, and the length of input. The IceTFT model directly generates 12-month SIE with average monthly prediction errors of less than 0.21 106 km2. And it predicts the September SIE nine months in advance with prediction error of less than 0.1 106 km2, which is superior to the models from Sea Ice Outlook (SIO). Furthermore, we analyze the sensitivity of the selected predictors to the SIE prediction. It verifies that the IceTFT model has some physical interpretability. And the variable sensitivities also provide some reference for understanding the mechanisms governing sea ice development and selecting the assimilation variables in dynamic models.</p

Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)

The Research & Development (R&D) phase of drug development is a lengthy and costly process. To revolutionize this process, we introduce our new concept QMLS to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein while the Quantum Simulation (QS) filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization, the resultant molecules generated and filtered from MLMG and QS are compared, and molecules that appear as a result of both processes will be made into dozens of molecular variations through Machine Learning Molecule Variation (MLMV), while others will only be made into a few variations. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations. In this paper we will go over the detailed design and framework of QMLS, including MLMG, MLMV, and QS.Comment: 13 pages, 6 figure

Effect of Langmuir monolayer of bovine serum albumin protein on the morphology of calcium carbonate

Bovine serum albumin (BSA) Langmuir monolayer, as a model of biomineralization-associated proteins, was used to study its effect on regulated biomineralization of calcium carbonate. The effects of the BSA Langmuir monolayer and the concentration of the subphase solution on the nucleation and growth processes and morphology of the calcium carbonate crystal were investigated. The morphology and polymorphic phase of the resulting calcium carbonate crystals were characterized by scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD). Moreover, the interaction mechanisms of the subphase solution with the BSA Langmuir monolayer were discussed. It was found that BSA Langmuir monolayer could be used as a template to successfully manipulate the polymorphic phase and crystal morphology of calcium carbonate and had obvious influence on the oriented crystallization and growth. The final morphology or aggregation mode of the calcite crystal was closely dependent on the concentration of calcium bicarbonate solution. It is expected that this research would help to better understand the mechanism of biomineralization by revealing the interactions between protein matrices and crystallization of calcium carbonate crystal.Comment: 4 pages, 4 figure

The Cortical and Striatal Gene Expression Profile of 100 Hz Electroacupuncture Treatment in 6-Hydroxydopamine-Induced Parkinson's Disease Model

Electroacupuncture (EA), especially high-frequency EA, has frequently been used as an alternative therapy for Parkinson disease (PD) and is reportedly effective for alleviating motor symptoms in patients and PD models. However, the molecular mechanism underlying its effectiveness is not completely understood. To implement a full-scale search for the targets of 100 Hz EA, we selected rat models treated with 6-hydroxydopamine into the unilateral MFB, which mimic end-stage PD. High-throughput microarray analysis was then used to uncover the regulated targets in the cortex and striatum after 4-week EA treatment. In the differentially regulated transcripts, the proportion of recovered expression profiles in the genes, the functional categories of targets in different profiles, and the affected pathways were analyzed. Our results suggested that the recovery of homeostasis in the transcript network and many regulated functional clusters in the cortex and striatum after EA treatment may contribute to the behavioral improvement of PD rats

Assessment of Evolving TRMM-Based Real-Time Precipitation Estimation Methods and Their Impacts on Hydrologic Prediction in a High-Latitude Basin

The real-time availability of satellite-derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time estimates (TMPA-RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA-RT estimates over its three major evolving periods were evaluated and inter-compared at daily, monthly and seasonal scales in the high-latitude Laohahe basin in China. Assessment results show that the performance of TMPA-RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA-RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest dataset of TMPA-RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA-RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events

Constraining GRB Initial Lorentz Factor with the Afterglow Onset Feature and Discovery of a Tight Gamma_0-E_iso Correlation

The onset of GRB afterglow is characterized by a smooth bump in the early afterglow lightcurve. We make an extensive search for such a feature. Twenty optically selected GRBs and 12 X-ray selected GRBs are found, among which 17 optically selected GRBs and 2 X-ray-selected GRBs have redshift measurements. We fit the lightcurves with a smooth broken power-law and measure the temporal characteristic timescales of the bumps at FWHM. Strong mutual correlations among these timescales are found, and a dimmer and broader bump tends to peak at a later peak time. The ratio of rising to decaying timescales is almost universal among bursts, but the ratio of the rising time to the peak time varies from 0.3~1. The E_iso is tightly correlated with the peak luminosity and the peak time of the bump in the burst frame. Assuming that the bumps signal the deceleration of the GRB fireballs in a constant density medium, we calculate the initial Lorentz factor (Gamma_0) and the deceleration radius (R_dec) of the GRBs in the optical-selected sample. It is found that Gamma_0 are typically a few hundreds, and the typical deceleration radius is R_dec~10^{17} cm. More intriguingly, a tight correlation between the Gamma_0 and E_iso is found, namely Gamma_0 ~ 195 E_iso, 52}^{0.27} (satisfied for both the optical and X-ray z-known samples). It is helpful to understand GRB physics, and may serve as an indicator of Gamma_0. We find that the early bright X-rays are usually dominated by a different component from the external shock emission, but occasionally (for one case) an achromatic deceleration feature is observed. Components in X-rays would contribute to the diversity of the observed X-ray lightcurves (abridge).Comment: 15 pages, including 4 tables and 7 figures, Submitted to Ap

A Comprehensive Analysis of Fermi Gamma-ray Burst Data: III. Energy-Dependent T90 Distributions of GBM GRBs and Instrumental Selection Effect on Duration Classification

The durations (T90) of 315 GRBs detected with Fermi/GBM (8-1000 keV) by 2011 September are calculated using the Bayesian Block method. We compare the T90 distributions between this sample and those derived from previous/current GRB missions. We show that the T90 distribution of this GRB sample is bimodal, with a statistical significance level being comparable to those derived from the BeppoSAX/GRBM sample and the Swift/BAT sample, but lower than that derived from the CGRO/BATSE sample. The short-to-long GRB number ratio is also much lower than that derived from the BATSE sample, i.e., 1:6.5 vs 1:3. We measure T90 in several bands, i.e., 8-15, 15-25, 25-50, 50-100, 100-350, and 350-1000 keV, to investigate the energy-dependence effect of the bimodal T90 distribution. It is found that the bimodal feature is well observed in the 50-100 and 100-350 keV bands, but is only marginally acceptable in the 25-50 keV and 350-1000 keV bands. The hypothesis of the bimodality is confidently rejected in the 8-15 and 15-25 keV bands. The T90 distributions in these bands are roughly consistent with those observed by missions with similar energy bands. The parameter T90 as a function of energy follows \bar T90 \propto E^{-0.20\pm 0.02} for long GRBs. Considering the erratic X-ray and optical flares, the duration of a burst would be even much longer for most GRBs. Our results, together with the observed extended emission of some short GRBs, indicate that the central engine activity time scale would be much longer than T90} for both long and short GRBs and the observed bimodal T90 distribution may be due to an instrumental selection effect.Comment: 29 pages, 2 tables, 9 figures, accepted for publication in Ap

SARS-CoV-2 Causes a Significant Stress Response Mediated by Small RNAs in the Blood of COVID-19 Patients

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a serious impact on the world. In this study, small RNAs from the blood of COVID-19 patients with moderate or severe symptoms were extracted for high-throughput sequencing and analysis. Interestingly, the levels of a special group of tRNA-derived small RNAs (tsRNAs) were found to be dramatically upregulated after SARS-CoV-2 infection, particularly in coronavirus disease 2019 (COVID-19) patients with severe symptoms. In particular, the 3′CCA tsRNAs from tRNA-Gly were highly consistent with the inflammation indicator C-reactive protein (CRP). In addition, we found that the majority of significantly changed microRNAs (miRNAs) were associated with endoplasmic reticulum (ER)/unfolded protein response (UPR) sensors, which may lead to the induction of proinflammatory cytokine and immune responses. This study found that SARS-CoV-2 infection caused significant changes in the levels of stress-associated small RNAs in patient blood and their potential functions. Our research revealed that the cells of COVID-19 patients undergo tremendous stress and respond, which can be reflected or regulated by small non-coding RNA (sncRNAs), thus providing potential thought for therapeutic intervention in COVID-19 by modulating small RNA levels or activities