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We study the production of$$D^0$$$D^0$meson inp+pandp-Pb collisions using the improved AMPT model considering both coalescence and independent fragmentation of charm quarks after the Cronin broadening is included. After a detailed discussion of the improvements implemented in the AMPT model for heavy quark production, we show that the modified AMPT model can provide a good description of$$D^0$$$D^0$meson spectra inp-Pb collisions, the$$Q_{\textrm{pPb}}$$$Q_{\text{pPb}}$data at different centralities and$$R_{\textrm{pPb}}$$$R_{\text{pPb}}$data in both mid- and forward (backward) rapidities. We also studied the effects of nuclear shadowing and parton cascade on the rapidity dependence of$$D^{0}$$$D^0$meson production and$$R_{\textrm{pPb}}$$$R_{\text{pPb}}$. Our results indicate that using the same strength of the Cronin effect (i.e$$\delta $$$\delta$value) as that obtained from the mid-rapidity data leads to a considerable overestimation of the$$D^0$$$D^0$meson spectra and$$R_{\textrm{pPb}}$$$R_{\text{pPb}}$data at high$$p_{\textrm{T}}$$$p_{\text{T}}$in the backward rapidity. As a result, the$$\delta $$$\delta$is determined via a$$\chi ^2$$${\chi }^2$fitting of the$$R_{\textrm{pPb}}$$$R_{\text{pPb}}$data across various rapidities. This work lays the foundation for a better understanding of cold-nuclear-matter (CNM) effects in relativistic heavy-ion collisions.

 

Characterizing the relative onset time, strength, and duration of molecular signals is critical for understanding the operation of signal transduction and genetic regulatory networks. However, detecting multiple such molecules as they are produced and then quickly consumed is challenging. A MER can encode information about transient molecular events as stable DNA sequences and are amenable to downstream sequencing or other analysis. Here, we report the development of a de novo molecular event recorder that processes information using a strand displacement reaction network and encodes the information using the primer exchange reaction, which can be decoded and quantified by DNA sequencing. The event recorder was able to classify the order at which different molecular signals appeared in time with 88% accuracy, the concentrations with 100% accuracy, and the duration with 75% accuracy. This simultaneous and highly programmable multiparameter recording could enable the large-scale deciphering of molecular events such as within dynamic reaction environments, living cells, or tissues.

 

By using specialized extraction chromatography columns, we have developed an innovative approach that effectively separates Lu and Hf from apatite with high yields and minimal interference, addressing the challenges associated with dating apatite using the Lu–Hf isochron technique.

 

Deep neural networks, including the Transformer architecture, have achieved remarkable performance in various time series tasks. However, their effectiveness in handling clinical time series data is hindered by specific challenges: 1) Sparse event sequences collected asynchronously with multivariate time series, and 2) Limited availability of labeled data. To address these challenges, we propose Our code is available at https://github.com/SigmaTsing/TransEHR.git . , a self-supervised Transformer model designed to encode multi-sourced asynchronous sequential data, such as structured Electronic Health Records (EHRs), efficiently. We introduce three pretext tasks for pre-training the Transformer model, utilizing large amounts of unlabeled structured EHR data, followed by fine-tuning on downstream prediction tasks using the limited labeled data. Through extensive experiments on three real-world health datasets, we demonstrate that our model achieves state-of-the-art performance on benchmark clinical tasks, including in-hospital mortality classification, phenotyping, and length-of-stay prediction. Our findings highlight the efficacy of in effectively addressing the challenges associated with clinical time series data, thus contributing to advancements in healthcare analytics.