The temperature–mortality relationship: an analysis from 31 Chinese provincial capital cities

<p>We aim to explore the Minimum Mortality Temperature (MMT) of different cities and regions, and that provides evidence for developing reasonable heat wave definition in China. The death data of 31 Chinese provincial capital cities from seven geographical regions during 2008–2013 was included in this study. In the first stage, a DLNM (Distributed Lag Non-linear Model) was used to estimate the association between mean temperature and mortality in a single city, then we pooled them with a multivariate meta-analysis to estimate the region-specific effects. The range of MMT was from 17.4 °C (Shijiazhuang) to 28.4 °C (Haikou), and the regional MMT increased as the original latitude decreased. Different cities and regions have their own specialized MMT due to geography and demographic characteristics. These findings indicate that the government deserves to adjust measures to local conditions to develop public health policies.</p

Isothermal Self-Assembly of Complex DNA Structures under Diverse and Biocompatible Conditions

Nucleic acid nanotechnology has enabled researchers to construct a wide range of multidimensional structures in vitro. Until recently, most DNA-based structures were assembled by thermal annealing using high magnesium concentrations and nonphysiological environments. Here, we describe a DNA self-assembly system that can be tuned to form a complex target structure isothermally at any prescribed temperature or homogeneous condition within a wide range. We were able to achieve isothermal assembly between 15 and 69 °C in a predictable fashion by altering the strength of strand–strand interactions in several different ways, for example, domain length, GC content, and linker regions between domains. We also observed the assembly of certain structures under biocompatible conditions, that is, at physiological pH, temperature, and salinity in the presence of the molecular crowding agent polyethylene glycol (PEG) mimicking the cellular environment. This represents an important step toward the self-assembly of geometrically precise DNA or RNA structures in vivo

Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC

Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC

Comparative analysis of expansin gene codon usage patterns among eight plant species

<p>Expansins are essential components of plant cell wall and play an important role in plant growth, development, and stress resistance via loosening function. To understand the codon usage pattern of expansin genes, we gained the sequence data of expansin genes from eight plant species. Statistics analysis showed obvious codon characteristics between monocot and dicot plants. Comparably, expansin genes in monocot plants had really higher GC content, more high-frequency codons, and more optimal codons than that in dicot plants. Several monocot plants performed somehow as dicot plants in a few characters. Codon information of expansin genes might contribute to the understanding of the relationship and evolution clues between monocot and dicot plants. It further gained insight into the improvement of the gene expression and roles.</p

Design Space for Complex DNA Structures

Nucleic acids have emerged as effective materials for assembling complex nanoscale structures. To tailor the structures to function optimally for particular applications, a broad structural design space is desired. Despite the many discrete and extended structures demonstrated in the past few decades, the design space remains to be fully explored. In particular, the complex finite-sized structures produced to date have been typically based on a small number of structural motifs. Here, we perform a comprehensive study of the design space for complex DNA structures, using more than 30 distinct motifs derived from single-stranded tiles. These motifs self-assemble to form structures with diverse strand weaving patterns and specific geometric properties, such as curvature and twist. We performed a systematic study to control and characterize the curvature of the structures, and constructed a flat structure with a corrugated strand pattern. The work here reveals the broadness of the design space for complex DNA nanostructures

Time dependence of RMSDs of ATP in five systems versus simulation time in the five systems.

<p>(A) The triphophate moiety of ATP. (B) The dihedral O_α3-P_β-O_β3-P_γ. (C) The P_α and P_γ atoms of triphosphate moiety of ATP.</p

Additional file 1 of Mortality and years of life lost due to pancreatic cancer in China, its provinces, urban and rural areas from 2005 to 2020: results from the national mortality surveillance system

Supplementary Material

Hydrogen bonds and coordination bonds at the ATP binding pocket of CDK9 in five systems.

<p>CDK9 is shown as a gray ribbon with a gray stick representing residues involved in hydrogen bond or coordination bond. ATP is depicted by a yellow stick. All oxygen atoms, nitrogen atoms, and phosphate atoms are depicted in red, blue, and orange, respectively. Mg₁²⁺ and Mg₂²⁺ ions are exhibited as green spheres and water molecules are shown as red spheres. Red dotted lines indicate hydrogen bonds and blue dotted lines represent coordination bonds.</p

The compositions of the five simulations.

<p>The compositions of the five simulations.</p