New Atomic Decompositions of Weighted Local Hardy Spaces

We introduce a new class of weighted local approximate atoms including classical weighted local atoms. Then we further obtain the weighted local approximate atomic decompositions of weighted local Hardy spaces $h_{\omega} ^p(R^n)$ with $0<p\leq 1$ and weight $\omega\in A_1(R^n)$. As an application, we prove the boundedness of inhomogeneous Calder\'on-Zygmund operators on $h_{\omega}^p(R^n)$ via weighted local approximate atoms and molecules

Theoretical Analysis of Random Scattering Induced by Microlensing

Theoretical investigations into the deflection angle caused by microlenses offer a direct path to uncovering principles of the cosmological microlensing effect. This work specifically concentrates on the the probability density function (PDF) of the light deflection angle induced by microlenses. We have made several significant improvements to the widely used formula from Katz et al. First, we update the coefficient from 3.05 to 1.454, resulting in a better fit between the theoretical PDF and our simulation results. Second, we developed an elegant fitting formula for the PDF that can replace its integral representation within a certain accuracy, which is numerically divergent unless arbitrary upper limits are chosen. Third, to facilitate further theoretical work in this area, we have identified a more suitable Gaussian approximation for the fitting formula.Comment: 15 pages, 6 figures, accepted for publication in Research in Astronomy and Astrophysic

TransportTP: A two-phase classification approach for membrane transporter prediction and characterization

<p>Abstract</p> <p>Background</p> <p>Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides.</p> <p>Results</p> <p>In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation.</p> <p>Conclusions</p> <p>TransportTP is the most effective tool for eukaryotic transporter characterization up to date.</p

Plasma lensing interpretation of FRB 20201124A bursts at the end of September 2021

When the radio photons propagate through a non-uniform electron density volume, the plasma lensing effect can induce an extreme magnification to the observed flux at certain frequencies. Because the plasma lens acts as a diverging lens, it can extremely suppress the observed flux when aligned with source. These two properties can theoretically cause a highly magnified Fast Radio Burst (FRB) to faint or even disappear for a period of time. In this paper, we interpret that the significant increase in burst counts followed by a sudden quenching in FRB 20201124A in September 2021 can be attributed to plasma lensing. Based on the one-dimensional Gaussian lens model, we search for double main-peak structures in spectra just before its extinction on September 29, 2021. After the de-dispersion and de-scintillation procedures, we find eight bursts with double main-peaks at stable positions. There are three parameters in our modelling, the height and width of the one-dimension Gaussian lens and its distance to the source. We reformulate them as a combined parameter $\mathrm{P}_0 \propto \left ( \frac{a}{\mathrm{AU}}\right )\sqrt{\frac{\mathrm{kpc}}{D_{\mathrm{LS}}} \frac{\mathrm{pc}\;\mathrm{cm}^{-3}}{N_0} }$. The frequency spectra can give an accurate estimation of $\mathrm{P}_0$ corresponding to $\left ( \frac{a}{\mathrm{AU}}\right )\sqrt{\frac{\mathrm{kpc}}{D_{\mathrm{LS}}} \frac{\mathrm{pc}\;\mathrm{cm}^{-3}}{N_0} } \approx 28.118$, while the time of arrival only give a relatively loose constraint on $a^2/D_{\mathrm{LS}}$. Comparing with the observation dynamic spectra, we suggest that for a plasma lens in host galaxy, e.g., $D_{\mathrm{LS}}\approx 1\mathrm{kpc}$, the width of lens can not be larger than $40\mathrm{AU}$. At last, we estimate the relative transverse motion velocity between the lens and source, $v\approx98\left(\frac{a}{\mathrm{AU}}\right)\mathrm{km/s}$.Comment: 9 pages, 12 figures. Comments are welcom

DeGNServer: Deciphering Genome-Scale Gene Networks through High Performance Reverse Engineering Analysis

Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online

Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery

Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate estimates are crucial for timely and effective post-disaster responses. Interferometric Synthetic aperture radar (InSAR) data is important in providing high-resolution onsite information for rapid hazard estimation. Most recent methods using InSAR imagery signals predict a single type of hazard and thus often suffer low accuracy due to noisy and complex signals induced by co-located hazards, impacts, and irrelevant environmental changes (e.g., vegetation changes, human activities). We introduce a novel stochastic variational inference with normalizing flows derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy InSAR imagery

Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models

When a damaging earthquake occurs, immediate information about casualties is critical for time-sensitive decision-making by emergency response and aid agencies in the first hours and days. Systems such as Prompt Assessment of Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS) were developed to provide a forecast within about 30 minutes of any significant earthquake globally. Traditional systems for estimating human loss in disasters often depend on manually collected early casualty reports from global media, a process that's labor-intensive and slow with notable time delays. Recently, some systems have employed keyword matching and topic modeling to extract relevant information from social media. However, these methods struggle with the complex semantics in multilingual texts and the challenge of interpreting ever-changing, often conflicting reports of death and injury numbers from various unverified sources on social media platforms. In this work, we introduce an end-to-end framework to significantly improve the timeliness and accuracy of global earthquake-induced human loss forecasting using multi-lingual, crowdsourced social media. Our framework integrates (1) a hierarchical casualty extraction model built upon large language models, prompt design, and few-shot learning to retrieve quantitative human loss claims from social media, (2) a physical constraint-aware, dynamic-truth discovery model that discovers the truthful human loss from massive noisy and potentially conflicting human loss claims, and (3) a Bayesian updating loss projection model that dynamically updates the final loss estimation using discovered truths. We test the framework in real-time on a series of global earthquake events in 2021 and 2022 and show that our framework streamlines casualty data retrieval, achieving speed and accuracy comparable to manual methods by USGS.Comment: 10 pages, 8 figure

Long-term comparative effectiveness of antihypertensive monotherapies in primary prevention of cardiovascular events:A population-based retrospective inception cohort study in the Netherlands

OBJECTIVE: To determine the long-term effectiveness of antihypertensive monotherapies in primary prevention of cardiovascular events.DESIGN: Retrospective inception cohort study covering a 25-year study period.SETTING: University Groningen IADB.nl pharmacy prescription database with data from 1996 to 2020.PARTICIPANTS: Patients aged 18 years or older, free of any cardiovascular disease (CVD) drug therapies prior to initiation of a preventive antihypertensive monotherapy (ACE inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), beta-blockers (BBs), calcium channel blockers (CCBs) and thiazides).OUTCOME MEASURES: Primary outcome was the time to first prescription of acute cardiac drug therapy (CDT) measured by valid drug proxies to identify a first major CVD event in patients without a history of CVD.RESULTS: Among 33 427 initiators, 5205 (15.6%) patients experienced an acute CDT. The average follow-up time was 7.9±5.5 years. The 25-year incidence rate per 1000 person-years were 25.3, 22.4, 18.2, 24.4 and 22.0 for ACEI, ARB, BB, CCB and thiazide starters, respectively. Inverse probability of treatment-weighted Cox regression showed that thiazide starters had lower hazards than the reference BB starters (HR: 0.88, 95% CI: 0.81 to 0.95). Among patients on diabetes drugs, risks were lower (HR: 0.49, 95% CI: 0.28 to 0.85). CCB starters had higher hazards than reference BB (HR: 1.21, 95% CI: 1.07 to 1.36). The overall estimated number needed to treat for thiazides compared with BBs to prevent one acute CDT in 25 years was 26, and four among patients on diabetes drugs.CONCLUSIONS: After adjustments for confounders, patients starting on monotherapy with thiazides had a lower incidence of CDT compared with those starting on BBs, notably among patients on diabetes drugs. Conversely, patients who began CCB monotherapy had a higher incidence of CDT compared with those starting on BBs. Other monotherapies had comparable incidence of cardiovascular disease compared with BBs.</p

Understanding the Role of Coordinatively Unsaturated Al3+ Sites on Nanoshaped Al2O3 for Creating Uniform Ni–Cu Alloys for Selective Hydrogenation of Acetylene

Acknowledgments This work was financially supported by the National Key R&D Program of China (2021YFB3801600), the National Natural Science Foundation of China (22218017), and the Fundamental Research Funds for the Central Universities (buctrc201921, JD2223). We acknowledge the Beijing Synchrotron Radiation Facility (BSRF) for providing the experimental resources for XAS experiments.Peer reviewedPostprin