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Surface soil moisture (SM) plays an important role in water and energy cycles. Passive microwave remote sensing observation has become the main means of obtaining large-scale surface SM. Due to its low spatial resolution, the spatial downscaling is required. With the development of artificial intelligence, data-driven SM downscaling models have emerged in recent years and have shown better accuracy than traditional physical models. However, data-driven SM downscaling models still have problems such as poor interpretability and easy overfitting. Therefore, this paper proposes a new SM downscaling model based on physical rule-constrained deep learning, named Physics-informed Soil Moisture Downscaling Deep Neural Network (PhySoilNet). This model adds the physical relationship between SM and the downscaling factor Land surface Evaporative Efficiency, as well as the saturated and residual boundary of SM into the Loss function of deep learning, thereby constraining the neural network. Results showed that PhySoilNet successfully downscaled the 9 km Soil Moisture Active Passive (SMAP) SM to 500 m, and performed well in the evaluations with in-situ, aerial, and SMAP SM. Compared to the downscaling model of only data-driven, the PhySoilNet had better performance in all evaluations, and the metrics in the in-situ SM network evaluation were improved by 20 % for R, 9.9 % for ubRMSE, 7.2 % for MAE, and 7.2 % for RMSE. At the same time, the number of SM predicted by PhySoilNet that outside the reasonable SM boundary range was significantly reduced. This fully demonstrates that data-driven based on physical rule constraints can achieve SM downscaling more effectively. Coupling physical rules and deep learning can fully utilize the powerful fitting ability of data-driven methods while improving the generalization ability and interpretability of downscaling models through prior physical knowledge.

The forest is an important part of carbon resources. Forest growing stock volume (GSV) is an important parameter of forest. The Water Cloud Model (WCM) is a simple equation that describes the interaction between ground objects and electromagnetic waves. It has also been applied in the estimation of forest GSV. When estimating GSV, the WCM equation parameters are usually calculated using least squares, but the least squares method relies on field reference data. The subsequent WCM development algorithm BIOMASAR uses a sliding window method that does not rely on measured data. However, the sliding window method is inefficient and can easily lead to missing pixels. We designed the backscatter/fractional vegetation coverage (FVC) feature space based on WCM and BIOMASAR to estimate forest GSV. Comparing with the national forest inventory (NFI) reference dataset and the BIOMASAR algorithm results in the study area, the method is evaluated from three aspects: accuracy, efficiency, and texture. The results show that this method does not rely on actual reference data, and the efficiency is increased from 1661s in the sliding window to 663s. The correlation with the NFI reference data is 0.45, the RMSE is 116.2327 m3/ha, and the RRMSE is 64.86%. The accuracy is better than the BIOMASAR sliding window GSV results in this study area, and compared with Google Earth images of the same period, it is also more consistent with the field texture. In short, the backscatter/FVC feature space can efficiently obtain forest GSV estimates more consistent with field conditions without relying on measured data.

Abstract Background Although obstructive sleep apnea (OSA) and periodontitis are associated, whether this association is causative is uncertain. Methods We conducted a bidirectional Mendelian randomization (MR) analysis using data from publically accessible genome-wide association studies. The single-nucleotide polymorphisms (SNPs) for OSA were derived from 16,761 cases and 201,194 controls. The pooled data of periodontitis association involved up to 17,353 individuals. Disease-associated single-nucleotide polymorphisms were selected as an instrumental variable at the genome-wide significance level (p

Abstract Background Common chronic infections induced low-grade inflammation has been correlated with atherosclerosis as supported by strong evidence. The balance between pro-and anti-inflammatory factors was exploited to elucidate the effects of chronic periodontitis on diabetes-associated atherosclerosis. Methods Study subjects encompassed 30 SPF male rats randomly divided into four groups: A group (NC), B group (T2DM), C group (CP), D group (DM + CP). After developing the model, blood samples were collected from the angular vein analyze serum APN, hs-CRP, and blood lipid. the carotid artery was isolated for HE staining. Result Compared with group A, the serum APN in group B, C and D decreased gradually with the progression of the disease. Serum hs-CRP in group B, C and D was significantly increased. At T3, T4 and T5 in group B, C and D, APN/hs-CRP significantly decreased. TC, LDL and TG significantly increased in group B, D; HDL significantly decreased in group C. Carotid artery HE staining showed: compared with group A, different degrees of endothelial defect, destruction of elastic fibers in the middle membrane, disorder of smooth muscle arrangement, and partial dissolution 、 fragmentation and Calcium salt deposition necrosis occurred in group B, C and D. Conclusion Enhanced systemic inflammation, decreased adiponectin level, and disorganized lipid metabolism with or without type 2 diabetes attributed to local inflammation of periodontitis can result in an imbalance of pro-inflammatory and anti-inflammatory effects. Therefore, it’s more meaningful to predict the progression of DAA with anti-inflammatory/pro-inflammatory variation.

Abstract Aim The study aimed to identify the underlying differentially expressed genes (DEGs) and mechanism of unstable atherosclerotic plaque using bioinformatics methods. Methods GSE120521, which includes four unstable samples and four stable atherosclerotic samples, was downloaded from the GEO database. DEGs were identified using LIMMA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using the Database for metascape Visualization online tool. Based on the STRING database, protein–protein interactions (PPIs) network among DEGs were constructed. Regulatory networks were visualized using Cytoscape. We use the xCell to analyze the different immune cell subtypes. Results A total of 1626 DEGs (1034 up-regulated and 592 down-regulated DEGs) were identified between unstable and stable samples. I pulled 62 transcription factors (34 up-regulated TFs and 28 down-regulated TFs) from the Trust database. The up-regulated TFs were mainly enrichment in positive regulation of myeloid leukocyte differentiation, and the down-regulated TFs were mainly enrichment in connective tissue development. In the PPI network, RB1, CEBPA, PPARG, BATF was the most significantly up-regulated gene in ruptured atherosclerotic samples. The immune cell composition enriched in CD cells and macrophages in the unstable carotid plaque. Conclusions Upregulated RB1, CEBPA, PPARG, BATF and down-regulated SRF, MYOCD, HEY2, GATA6 might perform critical promotional roles in atherosclerotic plaque rupture, furthermore, number and polarization of macrophages may play an important role in vulnerable plaques.

Agricultural drought seriously threatens the food and ecological security of most of the world’s developing countries. Data-driven integrated agricultural drought index with remote sensing provides an effective tool to monitor, evaluate, and predict the agricultural drought. However, there is still a lack of comprehensive analytical work on taking the most effective machine learning (ML) and deep learning (DL) methods to construct such integrated drought index. In other words, it is still unclear whether the recent DL methods can improve integrated drought monitoring as compared with the currently widely used ML methods. Therefore, we critically evaluated the performances of four representative DL methods (represents the four currently popular DL network types) i.e., Entity Embedding Deep Neural Network (EEDNN), One-dimensional Convolutional Neural Network (1D-CNN), Gated Recurrent Unit (GRU), and Self-Attention Mechanism (SAM) and three widely used tree-based ML methods i.e., Cubist, Random Forest (RF), and Light Gradient Boosting Machine (LGBM), through constructing a QuickDRI like integrated drought index (abbreviated as QuickDRI-China). About 30 years of meteorological data, 14 years of remote sensing data, and various biophysical variables in China such as land use/land cover, available water capacity, irrigated agriculture, elevation, and ecoregion were employed in this study. Results showed that the EEDNN performed best, followed by the RF and LGBM, and then the other methods including the currently wide used Cubist, according to the station accuracy evaluations, spatial description evaluations, and responses to specific drought event. The tree-based ML methods such as RF and LGBM are still competitive in constructing the integrated agricultural drought index at the current stage. However, the higher accuracy, the smoother spatial description, and the more responsive ability of the EEDNN demonstrate great potential of DL methods. The future integrated agricultural drought monitoring with remote sensing should develop a specialized DL network for heterogeneous agricultural drought features.

Abstract The large energy loss (Eloss) is one of the main obstacles to further improve the photovoltaic performance of organic solar cells (OSCs), which is closely related to the charge transfer (CT) state. Herein, ternary donor alloy strategy is used to precisely tune the energy of CT state (ECT) and thus the Eloss for boosting the efficiency of OSCs. The elevated ECT in the ternary OSCs reduce the energy loss for charge generation (ΔECT), and promote the hybridization between localized excitation state and CT state to reduce the nonradiative energy loss (ΔEnonrad). Together with the optimal morphology, the ternary OSCs afford an impressive power conversion efficiency of 19.22% with a significantly improved open‐circuit voltage (Voc) of 0.910 V without sacrificing short‐cicuit density (Jsc) and fill factor (FF) in comparison to the binary ones. This contribution reveals that precisely tuning the ECT via donor alloy strategy is an efficient way to minimize Eloss and improve the photovoltaic performance of OSCs.

Platinum (Pt) drugs have developed rapidly in clinical applications because of their broad and highly effective antitumor effects. In recent years, with the rapid development of immunotherapy, Pt-based antitumor agents have gained new challenges and opportunities. Since the discovery of their pharmacological effects in immunotherapy and tumor microenvironment regulation, research into Pt drugs has progressed to multi-ligand and multi-functional Pt precursors and their own shortcomings have been further highlighted. With the development of antitumor immunotherapy and the rise of combination therapy, the development of Pt-based drugs has started to move in the direction of multi-targeting, nanocarrier modification, immunotherapy and photodynamic therapy. In this paper, we first overview the recent applications of Pt-based drugs in antitumor inorganic chemistry, with a focus on summarizing the application of Pt-based drugs and their precursors in the anticancer immune response. The paper also provides a reasonable outlook on the future development of Pt-based drugs from the chemical and immunological perspectives, relying on the existing content and problems of Pt-based drug development. On the basis of the gathered information, joint multidisciplinary programs on implementing comprehensive immune analyses for the future development of novel anticancer metal compounds should be initiated.

Risk prediction is one of the important issues that draws much attention from academia and industry. And the fluctuation—absolute value of the change of price, is one of the indexes of risk. In this paper, we focus on the relationship between fluctuation and order volume. Based on the observation that the price would move when the volume of order changes, the prediction of price fluctuation can be converted into the prediction of order volume. Modelling the trader’s behaviours—order placement and order cancellation, we propose an order-based fluctuation prediction model. And our model outperforms better than baseline in OKCoin and BTC-e datasets.

Ternary polymer solar cells (PSCs) have been widely demonstrated as an effective method to improve device performance. Meanwhile, the simple fabrication technology can be well kept with a single bulk heterojunction active layer, which is beneficial to the potential industrialization of PSCs. The photovoltaic parameters, such as short-circuit current density, open-circuit voltage, and fill factor, of ternary PSCs can be simultaneously improved by appropriately incorporating the third component, resulting in the increased power conversion efficiency (PCE) compared with the corresponding binary PSCs. To date, the PCE of ternary PSCs has exceeded 17% with the development of effective donors and non-fullerene acceptors. The fundamental designing rules of ternary PSCs are summarized to give some available guidelines for material selection. Next, the recent progress of efficient ternary PSCs based on various types is investigated. Then, some methods are introduced to investigate the working mechanisms of ternary PSCs. At length, the perspective toward the future development of ternary PSCs is discussed.

Abstract Background Ferulic acid (4-hydroxy-3-methoxycinnamic acid, FA) is a hydroxycinnamic acid derived from a rich polyphenolic compound. This study aimed to investigate the effect of ferulic acid (4-hydroxy-3-methoxycinnamic acid; FA) on cell proliferation, invasion, apoptosis, and autophagy in Hela and Caski cervical carcinoma cell lines. Methods The cell proliferation of FA in Hela and Caski cells were detected by MTT assay. The cell invasion of FA in Hela and Caski cells were detected by Transwell assay. Subsequently, MMP-9 mRNA expression for cell invasion was detected by RT-PCR. Additionally, cell cycle and apoptosis were assayed using flow cytometry. Expression levels of 7 proteins for both cell cycle and autophagy were measured by Western blot analysis. Results After treated with FA (2.0 mM) for 48 h, the inhibition rates of FA in Hela and Caski cells were 88.3 and 85.4%, respectively. In addition, FA inhibited cell invasion through reducing MMP-9 mRNA expression. FA induced arrest in G0/G1 phase of the cell cycle in Hela and Caski cells with dose dependent (P

Nowadays, a large amount of information is stored as text, and numerous text mining techniques have been developed for various applications, such as event detection, news topic classification, public opinion detection, and sentiment analysis. Although significant progress has been achieved for short text classification, document-level text classification requires further exploration. Long documents always contain irrelevant noisy information that shelters the prominence of indicative features, limiting the interpretability of classification results. To alleviate this problem, a model called MIPELD (mining the frequent pattern of a named entity for long document classification) for long document classification is demonstrated, which mines the frequent patterns of named entities as features. Discovered patterns allow semantic generalization among documents and provide clues for verifying the results. Experiments on several datasets resulted in good accuracy and marco-F1 values, meeting the requirements for practical application. Further analysis validated the effectiveness of MIPELD in mining interpretable information in text classification.

The goal of this study was to improve the solubility and oral bioavailability of tamibarotene by complexing it with hydroxypropyl-β-cyclodextrin (HP-β-CD). The inclusion complex of tamibarotene with hydroxypropyl-β-cyclodextrin (Am80-HP-β-CD) was prepared through a freeze-drying method at the mole ratio of 1:1 (Am80: HP-β-CD). Fourier transform infrared spectroscopy (FT-IR) and differential scanning calorimetry (DSC) indicated the formation of Am80-HP-β-CD. In vitro dissolution studies showed that the solubility and dissolution percentage of Am80-HP-β-CD was improved substantially compared to Am80. An improved dissolution with approximately 97% drug release in 3 min was observed, in comparison with Am80 with approximately 60% release in 45 min. In vivo studies indicated that the AUC0-∞ has increased 2.79 times and the Cmax 4.37 times after the formation of inclusion complex. The decrease of tmax indicated the Am80-HP-β-CD inclusion complex can be absorbed into blood faster. In short, the solubility and bio-availability of Am80 has notably increased with the complexation of HP-β-CD. Therefore, using the inclusion technique is a promising method to improve the solubility of insoluble drugs.

The power conversion efficiency (PCE) of organic photovoltaics (OPVs) has exceeded 18% with narrow bandgap, non-fullerene materials Y6 or its derivatives when used as an electron acceptor. The PCE improvement of OPVs is due to strong photon harvesting in near-infrared light range and low energy loss. Meanwhile, ternary strategy is commonly recognized as a convenient and efficient means to improve the PCE of OPVs. In this review article, typical donor and acceptor materials in prepared efficient OPVs are summarized. From the device engineering perspective, the typical research work on ternary strategy and tandem structure is introduced for understanding the device design and materials selection for preparing efficient OPVs.

The aim of the current work was to develop generic sustained-release tablets containing 35 mg trimetazidine dihydrochloride and to establish an in vitro–in vivo correlation that could predict the bioavailability. The marketed sustained release tablet (Vastarel MR) used as reference, a sustained-release matrix tablet was prepared using hydroxypropyl methylcellulose (HPMC) as matrix by wet granulation and the in vitro dissolution profiles of the self-made tablets were determined in four different dissolution media (0.1 M HCl, pH 4.5 PBS, pH 6.8 PBS and water). A higher similarity between prepared tablets and Vastarel MR was established, with similarity factor (f2) ranging from 60 to 75 in the four media. The in vivo pharmacokinetics was studied in six healthy beagles. Compared with Vastarel MR, the Cmax of self-made tablets was slightly decreased, while the Tmax and MRT0–t were slightly prolonged, but with no significant difference (P > 0.05). The average of relative bioavailability (F) was 102.52% based on AUC0–t. For log-transformed AUC0–t and Cmax, the upper confidence limit on the appropriate criterion is

Vancomycin hydrochloride （VANH）, the first glycopeptide antibiotic, is a water-soluble drug for the treatment of acute osteomyelitis. Liposomal formulations of VANH have already been manipulated and characterized, which was a mean of increasing their therapeutic index, reducing their toxicity and altering drug biodistribution. One of the challenges for preparing VANH-Lips is their low encapsulation efficiency (EE). In the present study, we aim to improve the liposomal formulation of VANH for higher EE, longer systemic circulation, reduced nephrotoxicity and enhanced antimicrobial activities. Vancomycin hydrochloride-loaded liposomes (VANH-Lips) were formulated by the method of modified reverse phase evaporation. Based on the optimization of formulation with orthogonal experimental design, the average drug encapsulation efficiency and the mean particle size of VANH-Lips were found to be 40.78 ± 2.56% and 188.4 ± 2.77 nm. In vitro drug release of VANH-Lips possessed a sustained release characteristic and their release behavior was in accordance with the Weibull equation. After intravenous injection to mice, the mean residence time (MRT) of VANH-Lips group was significantly prolonged in vivo and the AUC value was improved as well compared with the vancomycin hydrochloride solution (VANH-Sol) group. Furthermore, the biodistribution results in mice showed that VANH-Lips decreased the accumulation of VANH in kidney after intravenous injection. In conclusion, VANH-Lips may be a potential delivery system for VANH to decrease nephrotoxicity in the treatment of osteomyelitis.

We study the electronic structures and topological properties of (M+N)-layer twisted graphene systems. We consider the generic situation that N-layer graphene is placed on top of the other M-layer graphene and is twisted with respect to each other by an angle θ. In such twisted multilayer graphene systems, we find that there exist two low-energy flat bands for each valley emerging from the interface between the M layers and the N layers. These two low-energy bands in the twisted multilayer graphene system possess valley Chern numbers that are dependent on both the number of layers and the stacking chiralities. In particular, when the stacking chiralities of the M layers and N layers are opposite, the total Chern number of the two low-energy bands for each valley equals ±(M+N-2) (per spin). If the stacking chiralities of the M layers and the N layers are the same, then the total Chern number of the two low-energy bands for each valley is ±(M-N) (per spin). The valley Chern numbers of the low-energy bands are associated with large, valley-contrasting orbital magnetizations, suggesting the possible existence of orbital ferromagnetism and anomalous Hall effect once the valley degeneracy is lifted either externally by a weak magnetic field or internally by Coulomb interaction through spontaneous symmetry breaking. Such an orbital ferromagnetic state is characterized by chiral current loops circulating around the AA region of the moiré pattern, which can be experimentally detected.