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Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model’s reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems. Artificial neural networks have been successfully used for language recognition. Tsai et al. use the same techniques to link between language processing and prediction of molecular trajectories and show capability to predict complex thermodynamics and kinetics arising in chemical or biological physics.

Background: The Guangzhou Nutrition and Health Study (GNHS) aims to assess the determinants of metabolic disease in nutritional aspects, as well as other environmental and genetic factors, and explore possible biomarkers and mechanisms with multi-omics integration.Methods: The population-based sample of adults in Guangzhou, China (baseline: 40–83 years old; n = 5,118) was followed up about every 3 years. All are tracked via on-site follow-up and health information systems. We assessed detailed information on lifestyle factors, physical activities, dietary assessments, psychological health, cognitive function, body measurements, and muscle function. Instrument tests included dual-energy X-ray absorptiometry scanning, carotid artery and liver ultrasonography evaluations, vascular endothelial function evaluation, upper-abdomen and brain magnetic resonance imaging, and 14-day real-time continuous glucose monitoring tests. We also measured multi-omics, including host genome-wide genotyping, serum metabolome and proteome, gut microbiome (16S rRNA sequencing, metagenome, and internal transcribed spacer 2 sequencing), and fecal metabolome and proteome.Results: The baseline surveys were conducted from 2008 to 2015. Now, we have completed 3 waves. The 3rd and 4th follow-ups have started but have yet to end. A total of 5,118 participants aged 40–83 took part in the study. The median age at baseline was approximately 59.0 years and the proportion of female participants was about 69.4%. Among all the participants, 3,628 (71%) completed at least one on-site follow-up, with a median duration of 9.48 years.Conclusion: The cohort will provide data that will be influential in establishing the role of nutrition in metabolic diseases with multi-omics.

The microRNA319 (miR319) family is conserved among diverse plant species. In rice (Oryza sativa L.), the miR319 gene family is comprised of two members, Osa-miR319a and Osa-miR319b. We found that overexpressing Osa-miR319b in rice resulted in wider leaf blades and delayed development. Here, we focused on the biological function and potential molecular mechanism of the Osa-miR319b gene in response to cold stress in rice. The expression of Osa-miR319b was down-regulated by cold stress, and the overexpression of Osa-miR319b led to an enhanced tolerance to cold stress, as evidenced by higher survival rates and proline content. Also, the expression of a handful of cold stress responsive genes, such as DREB1A/B/C, DREB2A, TPP1/2, was increased in Osa-miR319b transgenic lines. Furthermore, we demonstrated the nuclear localization of the transcription factors, OsPCF6 and OsTCP21, which may be Osa-miR319b-targeted genes. We also showed that OsPCF6 and OsTCP21 expression was largely induced by cold stress, and the degree of induction was obviously repressed in plants overexpressing Osa-miR319b. As expected, the down-regulation of OsPCF6 and OsTCP21 resulted in enhanced tolerance to cold stress, partially by modifying active oxygen scavenging. Taken together, our findings suggest that Osa-miR319b plays an important role in plant response to cold stress, maybe by targeting OsPCF6 and OsTCP21.

As the climate warms, increasing heat-related health risks are expected, and can be exacerbated by the urban heat island (UHI) effect. UHIs can also offer protection against cold weather, but a clear quantification of their impacts on human health across diverse cities and seasons is still being explored. Here we provide a 500 m resolution assessment of mortality risks associated with UHIs for 85 European cities in 2015-2017. Acute impacts are found during heat extremes, with a 45% median increase in mortality risk associated with UHI, compared to a 7% decrease during cold extremes. However, protracted cold seasons result in greater integrated protective effects. On average, UHI-induced heat-/cold-related mortality is associated with economic impacts of €192/€ − 314 per adult urban inhabitant per year in Europe, comparable to air pollution and transit costs. These findings urge strategies aimed at designing healthier cities to consider the seasonality of UHI impacts, and to account for social costs, their controlling factors, and intra-urban variability. Urban heat islands have the greatest acute impacts on human mortality risk during extreme heat. However, protracted cold seasons result in greater annually integrated protective effects in most European cities under the current climate.

We investigate the dynamics of drop impacts on dry solid surfaces. By synchronising high-speed photography with fast force sensing, we simultaneously measure the temporal evolution of the shape and impact force of impacting drops over a wide range of Reynolds numbers ( $\\mathit{Re}$ ). At high $\\mathit{Re}$ , when inertia dominates the impact processes, we show that the early time evolution of impact force follows a square-root scaling, quantitatively agreeing with a recent self-similar theory. This observation provides direct experimental evidence on the existence of upward propagating self-similar pressure fields during the initial impact of liquid drops at high $\\mathit{Re}$ . When viscous forces gradually set in with decreasing $\\mathit{Re}$ , we analyse the early time scaling of the impact force of viscous drops using a perturbation method. The analysis quantitatively matches our experiments and successfully predicts the trends of the maximum impact force and the associated peak time with decreasing $\\mathit{Re}$ . Furthermore, we discuss the influence of viscoelasticity on the temporal signature of impact forces. Last but not least, we also investigate the spreading of liquid drops at high $\\mathit{Re}$ following the initial impact. Particularly, we find an exact parameter-free self-similar solution for the inertia-driven drop spreading, which quantitatively predicts the height of spreading drops at high $\\mathit{Re}$ . The limit of the self-similar approach for drop spreading is also discussed. As such, our study provides a quantitative understanding of the temporal evolution of impact forces across the inertial, viscous and viscoelastic regimes and sheds new light on the self-similar dynamics of drop-impact processes.

Exosomes are a subset of extracellular vesicles that carry specific combinations of proteins, nucleic acids, metabolites, and lipids. Mounting evidence suggests that exosomes participate in intercellular communication and act as important molecular vehicles in the regulation of numerous physiological and pathological processes, including cancer development. Exosomes are released by various cell types under both normal and pathological conditions, and they can be found in multiple bodily fluids. Moreover, exosomes carrying a wide variety of important macromolecules provide a window into altered cellular or tissue states. Their presence in biological fluids renders them an attractive, minimally invasive approach for liquid biopsies with potential biomarkers for cancer diagnosis, prediction, and surveillance. Due to their biocompatibility and low immunogenicity and cytotoxicity, exosomes have potential clinical applications in the development of innovative therapeutic approaches. Here, we summarize recent advances in various technologies for exosome isolation for cancer research. We outline the functions of exosomes in regulating tumor metastasis, drug resistance, and immune modulation in the context of cancer development. Finally, we discuss prospects and challenges for the clinical development of exosome-based liquid biopsies and therapeutics.

Drop impact causes severe surface erosion, dictating many important natural, environmental and engineering processes and calling for substantial prevention and preservation efforts. Nevertheless, despite extensive studies on the kinematic features of impacting drops over the last two decades, the dynamic process that leads to the drop-impact erosion is still far from clear. Here, we develop a method of high-speed stress microscopy, which measures the key dynamic properties of drop impact responsible for erosion, i.e., the shear stress and pressure distributions of impacting drops, with unprecedented spatiotemporal resolutions. Our experiments reveal the fast propagation of self-similar noncentral stress maxima underneath impacting drops and quantify the shear force on impacted substrates. Moreover, we examine the deformation of elastic substrates under impact and uncover impact-induced surface shock waves. Our study opens the door for quantitative measurements of the impact stress of liquid drops and sheds light on the origin of low-speed drop-impact erosion. The dynamic process behind the low-speed drop-impact erosion remains challenging to understand. Cheng et al. develop a method of high-speed microscopy, revealing the fast propagation of self-similar stress maxima underneath impacting drops and the formation of surface waves on impacted substrates.

Using the panel data of 938 listed manufacturing companies in China from 2011 to 2020, this paper scientifically examines the impact of artificial intelligence (AI) on total factor productivity (TFP) of China’s manufacturing enterprises by using the fixed effect model, mediating effect model and difference-in-differences model. The results show that AI can significantly improve the TFP of China’s manufacturing enterprises, as confirmed by a series of robustness tests. Technological innovation, human capital optimization and market matching improvement have proved to be three important channels for AI to affect the TFP of China’s manufacturing enterprises. The impact of AI on TFP varies greatly among China’s manufacturing enterprises in different geographical locations, industry characteristics, ownership and life cycle stages. The findings of this paper can provide theoretical insights and empirical evidence at the micro enterprise level for policymakers to give full play to the role of AI in promoting the high-quality development of China's manufacturing industry.

Background The propensity of the activated neutrophils to form extracellular traps (NETs) is demonstrated in multiple inflammatory conditions. In this study, we investigated the roles of NETs in metastasis of hepatocellular carcinoma (HCC) and further explored the underlying mechanism of how NETs affect metastasis as well as the therapeutic value. Methods The neutrophils were isolated from the blood of human HCC patients and used to evaluate the formation of NETs. The expression of NET markers was detected in tumor specimens. A LPS-induced NET model was used to investigate the role of NETs on HCC metastasis. RNA-seq was performed to identify the key molecular event triggered by NETs, and their underlying mechanism and therapeutic significance were explored using both in vitro and in vivo assays. Results NET formation was enhanced in neutrophils derived from HCC patients, especially those with metastatic HCCs. NETs trapped HCC cells and subsequently induced cell-death resistance and enhanced invasiveness to trigger their metastatic potential, which was mediated by internalization of NETs into trapped HCC cells and activation of Toll-like receptors TLR4/9-COX2 signaling. Inhibition of TLR4/9-COX2 signaling abrogated the NET-aroused metastatic potential. A combination of DNase 1 directly wrecking NETs with anti-inflammation drugs aspirin/hydroxychloroquine effectively reduced HCC metastasis in mice model. Conclusions NETs trigger tumorous inflammatory response and fuel HCC metastasis. Targeting NETs rather than neutrophils themselves can be a practice strategy against HCC metastasis.

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework “reweighted autoencoded variational Bayes for enhanced sampling (RAVE).” We study two molecular systems—urea and glycine—in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.