As core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human ...organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing.
We perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH
fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors, and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues.
The adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.
We examine relationship between common institutional ownership and corporate social responsibility (CSR). We find that common institutional ownership is negatively associated with the level of CSR, ...which supports an anti-competitive view. We conduct a propensity score matching (PSM) analysis and a difference-in-differences (DiD) analysis based on a quasi-natural experiment of financial institution mergers. The results alleviate concerns about endogeneity. Using the DiD setting, we find further support for the anti-competitive view, and can rule out alternative explanations. Additional analyses on investor characteristics show that our results come mainly from common owners with long-term investment horizons or lower social inclination. Moreover, we find that the anti-competitive effect is more pronounced for mature firms, and for firms in industries with lower labor intensity and lower customer sensitivity.
Persistent luminescence nanoparticles (PLNPs) are unique optical materials emitting long-lasting luminescence after ceasing excitation. Such a unique optical feature allows luminescence detection ...without constant external illumination to avoid the interferences of autofluorescence and scattering light from biological fluids and tissues. Besides, near-infrared (NIR) PLNPs have advantages of deep penetration and the reactivation of the persistent luminescence (PL) by red or NIR light. These features make the application of NIR-emitting PLNPs in long-term bioimaging no longer limited by the lifetime of PL. To take full advantage of PLNPs for biological applications, the versatile strategies for bridging PLNPs and biological system become increasingly significant for the design of PLNPs-based nanoprobes. In this Account, we summarize our systematic achievements in the biological applications of PLNPs from biosensing/bioimaging to theranostics with emphasizing the engineering strategies for fabricating specific PLNPs-based nanoprobes. We take surface engineering and manipulating energy transfer as the major principles to design various PLNPs-based nanoprobes based on the nature of interactions between nanoprobes and targets. We have developed target-induced formation or interruption of fluorescence resonance energy transfer systems for autofluorescence-free biosensing and imaging of cancer biomarkers. We have decorated single or dual targeting ligands on PLNPs for tumor-targeted imaging, and integrated other modal imaging agents into PLNPs for multimodal imaging. We have also employed specific functionalization for various biomedical applications including chemotherapy, photodynamic therapy, photothermal therapy, stem cells tracking and PL imaging-guided gene therapy. Besides, we have modified PLNPs with multiple functional units to achieve challenging metastatic tumor theranostics. The proposed design principle and comprehensive strategies show great potential in guiding the design of PLNPs nanoprobes and promoting further development of PLNPs in the fields of biological science and medicine. We conclude this Account by outlining the future directions to further promote the practical application of PLNPs. The novel protocols for the synthesis of small-size, monodisperse, and water-soluble PLNPs with high NIR PL intensity and superlong afterglow are the vibrant directions for the biomedical applications of PLNPs. In-depth theories and evidence on luminescence mechanism of PLNPs are highly desired for further improvement of their luminescence performance. Furthermore, other irradiations without tissue penetrating depth limit, such as X-ray, are encouraged for use in energy storage and re-excitation of PLNPs, enabling imaging in deep tissue in vivo and integrating other X-ray sensitized theranostic techniques such as computed tomography imaging and radiotherapy. Last but not least, PLNPs-based nanoprobes and the brand new hybrids of PLNPs with other nanomaterials show a bright prospect for accurate diagnosis and efficient treatment of diseases besides tumors.
Abstract
It is easy to form hydrates in the development of natural gas. It is of great significance to study the formation of prediction models to guide the safe production of oil and gas fields. ...Based on computer intelligence algorithms, a prediction model of natural gas hydrate formation based on Extremely randomized trees was established and compared with the BP Neral Network model. To objectively evaluate the predictive power of the model, an extensive database of more than 1000 hydrate formation conditions was established. The results show that the number of optimal decision trees for the Extremely randomized trees model is 6, and the decision tree depth is 32. The BP Neral Network model has a flat error distribution with a maximum error of 6.37%. The error distribution of the Extremely randomized trees model is abrupt, with a maximum error of 3.39%, with higher stability and accuracy. In terms of pure water, the BP Neral Network model performs well only in a small number of conditions due to over-fitting, but the Extremely randomized trees model can avoid over-fitting by using the large number theorem, showing a stronger advantage.
Tumor-associated macrophages (TAMs) are the most abundant inflammatory infiltrates in the tumor microenvironment and contribute to lymph node (LN) metastasis. However, the precise mechanisms of ...TAMs-induced LN metastasis remain largely unknown. Herein, we identify a long noncoding RNA, termed Lymph Node Metastasis Associated Transcript 1 (LNMAT1), which is upregulated in LN-positive bladder cancer and associated with LN metastasis and prognosis. Through gain and loss of function approaches, we find that LNMAT1 promotes bladder cancer-associated lymphangiogenesis and lymphatic metastasis. Mechanistically, LNMAT1 epigenetically activates CCL2 expression by recruiting hnRNPL to CCL2 promoter, which leads to increased H3K4 tri-methylation that ensures hnRNPL binding and enhances transcription. Furthermore, LNMAT1-induced upregulation of CCL2 recruits macrophages into the tumor, which promotes lymphatic metastasis via VEGF-C excretion. These findings provide a plausible mechanism for LNMAT1-modulated tumor microenvironment in lymphatic metastasis and suggest that LNMAT1 may represent a potential therapeutic target for clinical intervention in LN-metastatic bladder cancer.
The toxicity and bioaccumulation potential of phthalate acid esters (PAEs) make their impact on the environment a matter of considerable concern. Due to China's recent economic development and ...population growth, it has become one of the largest manufacturers and consumers of PAEs, with an associated contamination threat to several environmental compartments. The aim of this overview is to present a systematic account of PAE occurrence in various environmental media in China in the last decade, including the air, surface water, sediments, soil, sewage, and sludge; human exposure to PAEs is also evaluated. This reveals a location-dependence that can be attributed to regional differences in economic and industrial development as well as specific geographic location. A need for further study into the transportation and transformation behavior of PAEs in different environmental media and into PAE control technologies is identified, as a means of effectively assessing potential ecological and health risks.
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•PAEs were detected ubiquitously and abundantly in China.•DEHP was the most abundant phthalates in various environmental media.•PAEs concentrations exhibit a location dependent.•Limited treatment might cause the widespread contaminations of PAEs.
Although Panax ginseng is a famous traditional Chinese medicine and has been widely used to treat a variety of metabolic diseases including hyperglycemia, hyperlipidemia, and hepatosteatosis, the ...effective mediators and molecular mechanisms remain largely unknown. In this study we found that ginsenoside Rb2, one of the major ginsenosides in Panax ginseng, was able to prevent hepatic lipid accumulation through autophagy induction both in vivo and in vitro. Treatment of male db/db mice with Rb2 significantly improved glucose tolerance, decreased hepatic lipid accumulation, and restored hepatic autophagy. In vitro, Rb2 (50 µmol/L) obviously increased autophagic flux in HepG2 cells and primary mouse hepatocytes, and consequently reduced the lipid accumulation induced by oleic acid in combination with high glucose. Western blotting analysis showed that Rb2 partly reversed the high fatty acid in combination with high glucose (OA)-induced repression of autophagic pathways including AMP-activated protein kinase (AMPK) and silent information regulator 1 (sirt1). Furthermore, pharmacological inhibition of the sirt1 or AMPK pathways attenuated these beneficial effects of Rb2 on hepatic autophagy and lipid accumulation. Taken together, these results suggested that Rb2 alleviated hepatic lipid accumulation by restoring autophagy via the induction of sirt1 and activation of AMPK, and resulted in improved nonalcoholic fatty liver disease (NAFLD) and glucose tolerance.
Thompson sampling is a randomized Bayesian machine learning method, whose original motivation was to sequentially evaluate treatments in clinical trials. In recent years, this method has drawn wide ...attention, as Internet companies have successfully implemented it for online ad display. In “Online network revenue management using Thompson sampling,” K. Ferreira, D. Simchi-Levi, and H. Wang propose using Thompson sampling for a revenue management problem where the demand function is unknown. A main challenge to adopt Thompson sampling for revenue management is that the original method does not incorporate inventory constraints. However, the authors show that Thompson sampling can be naturally combined with a linear program formulation to include inventory constraints. The result is a dynamic pricing algorithm that incorporates domain knowledge and has strong theoretical performance guarantees as well as promising numerical performance results. Interestingly, the authors demonstrate that Thompson sampling achieves poor performance when it does not take into account domain knowledge. Finally, the proposed dynamic pricing algorithm is highly flexible and is applicable in a range of industries, from airlines and internet advertising all the way to online retailing.
We consider a price-based network revenue management problem in which a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As is common in practice, we assume the demand function contains unknown parameters that must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a trade-off commonly referred to as the “exploration-exploitation trade-off.” Toward the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price (“exploration” objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). We propose a class of dynamic pricing algorithms that builds on the simple, yet powerful, machine learning technique known as “Thompson sampling” to address the challenge of balancing the exploration-exploitation trade-off under the presence of inventory constraints. Our algorithms have both strong theoretical performance guarantees and promising numerical performance results when compared with other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multiarmed bandit problems with resource constraints as well as in applications in other revenue management settings and beyond.
The online appendix is available at
https://doi.org/10.1287/opre.2018.1755
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