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Exploring the active ingredients and important targets of Gleditsiae Spina for the treatment of pancreatic cancer through network pharmacology, molecular docking, and molecular dynamics simulations.

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km2) around the globe over a 37-year time span (1982–2018). Out of the 176032 lakes with area >1 km2 detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p

Background Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development. Methods This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models. Results Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy. Conclusions The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.

BACKGROUND: Senescence marker protein 30 (SMP30), which plays a pivotal role as a suppressor protein in cell proliferation, among other regulatory actions, is a marker of aging that shows decreased expression during senescence. Decreased SMP30 has been identified in several human cancers, but its expression and role in human non-small cell lung cancer (NSCLC) remain unclear. METHODS: Using tumor tissue and matched adjacent normal tissue from 341 patients with resected NSCLC, we assessed SMP30 expression using immunohistochemical methods. The relationship between SMP30 expression and clinicopathologic characteristics was investigated by Kaplan-Meier survival analysis and multivariate analysis. Cell viability assay, colony formation assay, EdU incorporation assay and in vivo tumor xenograft models were also performed to investigate NSCLC cell proliferation using A549 and H1299 cell lines. Recombinant lentivirus-meditated in vivo gene overexpression and Western blot were performed to clarify the underlying molecular mechanism of SMP30 inhibiting NSCLC proliferation. RESULTS: SMP30 expression was frequently downregulated in NSCLC tissue, as compared with adjacent non-tumor tissue. Kaplan-Meier survival analyses revealed NSCLC patients with low SMP30 expression had a significantly worse overall survival (OS), with median OS of 18 vs. 67 months in high SMP30 expression group. SMP30 overexpression significantly inhibited A549 and H1299 cell proliferation both in vitro and in tumor xenografts and downregulated the expression of c-Myc and CyclinD1 protein. Moreover, Western blot analyses confirmed that SMP30 overexpression significantly inhibited the histone deacetylase 4 (HDAC4) level in NSCLC cells, and HDAC4 overexpression reversed SMP30-mediated NSCLC repression both in vitro and in vivo. CONCLUSIONS: SMP30 inhibited NSCLC proliferation by reducing HDAC4 expression, and SMP30 and HDAC4 may serve as new prognostic biomarkers and future therapeutic targets for NSCLC.

Lakes are important natural sources of atmospheric methane (CH4), which should be considered in global CH4 budget estimations. However, the CH4 emissions from lakes can be highly variable, and the emissions from the river inlet region were less studied, which seriously hamper our understanding of CH4 budget estimates of lakes. Here, field measurements from over six years (2012 to 2017) at Lake Taihu, a large eutrophic shallow lake with a complicated river network, were conducted to address the issue. Results show that the river inlet region of the lake was a hot spot of CH4 emission. The CH4 emission at the river inlet region with an annual mean value of 0.183 mmol m−2 d−1 was seven times higher than that at pelagic region (0.028 mmol m−2 d−1). Peak CH4 emission occurred in warm seasons, and the lowest in cold seasons at the pelagic region, but peak emission occurred in cold seasons at the river inlet region. The seasonal CH4 emission at the pelagic region can be explained by water temperature; however, less temperature dependency of CH4 emission at the river inlet region was found. The variability of CH4 emission was linked to pollution loadings, and CH4-rich water in the inflowing river likely sustained the large CH4 emission of river inlet region.

Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of the heterogeneity of programmed cell death. The purpose of this work is to investigate and develop a cuproptosis-associated lncRNA-based LUAD prediction marker. We firstly performed bioinformatic analysis of the Cuprotosis database and The Cancer Genome Atlas (TCGA) database to obtain 19 cuprotosis-related gene datasets and transcriptional data for LUAD. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were utilized to construct cuproptosis-associated lncRNA modes. LUAD patients were thus classified into high-risk and low-risk categories based on prognostic risk values, with a median of It acted as a boundary. Risk models were evaluated and validated using Kaplan-Meier analysis, principal component analysis (PCA), gene set enrichment analysis (GSEA) and nomograms. Utilizing the TCGA-LUAD dataset, we identified seven predicted cuproptosis-associated lncRNAs in tumor microenvironment to create the risk model. 95.54% (214/224) of high-risk category tumor samples included cuproptosis-associated gene alterations, compared to 85.65% (203/237) of low-risk category tumor samples, with TP53 accounting for the bulk of occurrences. According to these findings, risk value was superior to other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival (OS). The predictive validity of the cuproptosis-associated lncRNA-based risk model for LUAD is high, and this may have implications for how lung cancer patients are treated individually.

Background Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and is characterized by high morbidity and mortality. However, the detailed molecular mechanisms underlying malignant progression of ESCC remain unclear. Methods 140 patients with esophageal squamous cell carcinoma who underwent surgery were enrolled in this study. Clinicopathologic characteristics and Kaplan–Meier survival analysis were performed to investigate DNMT3A expression and HDAC9 expression. Cell functional experiments were implemented both in vivo and in vitro to investigate the effects on ESCC cell proliferation and metastasis. Recombinant lentivirus–meditated gene overexpression or knockdown showed that HDAC9 participated in DNMT3A-mediated ESCC progression in vivo and in vitro, and this was further confirmed by HDAC inhibitor (SAHA) and immunohistochemical. Results We first demonstrated that DNMT3A expression was significantly higher in ESCC tissues than in corresponding adjacent non-cancerous tissues, and high DNMT3A expression was associated with poor prognosis. Then we confirmed that DNMT3A promoted proliferation and metastasis of ESCC cells in vivo and in vitro, suggesting that DNMT3A may be a promising therapeutic target for preventing esophageal cancer. Additionally, we found that DNMT3A up-regulated HDAC9, and HDAC9 overexpression significantly reversed the inhibitory effect caused by DNMT3A gene knockdown. In addition, we further confirmed the involvement of HDAC9 in DNMT3A-mediated carcinogenesis using HDAC inhibitor (SAHA). At last, we also validated HDAC9 expression was significantly higher in ESCC tissues than in corresponding adjacent non-tumour tissues, and high HDAC9 expression was positively associated with poor prognosis. Interestingly, Spearman correlation analysis confirmed a significant positive correlation between DNMT3A and HDAC9 expression. and ESCC patients with simultaneous DNMT3A and HDAC9 expression have the worst prognosis. Conclusions These results suggest that HDAC9 is involved in DNMT3A-mediated progression of ESCC, and targeting DNMT3A and HDAC9 may be a potential ESCC therapeutic strategy.

In order to compensate for high path loss, massive multiple-input multiple-output (MIMO) has been adopted in wireless communication systems operating at millimeter-wave (mmWave) bands. In particular, analog beamformer has been employed in hybrid massive MIMO systems to reduce the cost caused by radio frequency (RF) chains. However, due to hardware limitation, only conventional beamformer can be used at base station (BS) in existing hybrid massive MIMO systems, and as a consequence, inter-user-interference (IUI) cannot be removed completely in the presence of multiple users because of the sidelobe of conventional beamformer. In this letter, we will show that by utilizing the reconstructed spatial covariance matrix (SCM), adaptive beamformer can be also used at the BS in hybrid massive MIMO systems. Thanks to the reconstructed SCM, nulling directions of the adaptive beamformer can be adjusted adaptively to the directions of non-desired users, and therefore the residual IUI can be effectively removed. Simulation results show that the adaptive beamformer can outperform the conventional beamformer significantly in hybrid massive MIMO systems.

Lake Dianchi is one of the most eutrophic lakes in China. The decline in water quality and the occurrence of massive algal blooms pose a significant threat to the health and environmental safety of the water ecosystem, making Lake Dianchi a key concern for algal bloom management in China. Obtaining the spatiotemporal dynamics of algal blooms for the longest time possible is crucial to algal bloom management and future prediction. However, it is difficult to acquire a long-term record of algal blooms from a single sensor in order to cover a more extended period of eutrophication in the lake due to the limitation of the spatial and temporal resolution of the sensors. In this study, Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) images were combined with the Floating Algae Index (FAI) to reconstruct a unified time series of bloom areas to analyze the algal bloom dynamics in Lake Dianchi in recent decades. Regarding the interannual variation, the bloom area showed an increasing trend from 1987 to 2021, with larger bloom areas in 1991–1992, 2000–2003, 2012–2013, and 2020–2021. In terms of seasonal characteristics, the bloom area was significantly more prominent in the rainy season compared with the dry season during the year. The spatial distribution of the bloom frequency showed a pattern of higher frequencies in the north and lower frequencies in the south. From 2000 to 2021, the initial bloom time and bloom duration showed a trend of delaying and then advancing and decreasing and then increasing, respectively. We analyzed the importance of long-term records of algal blooms and found that the percentage of rainy season images is an essential factor in reconstructing time series based on different sensors. In addition, the relationship between environmental factors and algal blooms was analyzed. The results show that wind speed and air temperature were the main meteorological factors controlling the interannual variation in algal blooms in Lake Dianchi. Water quality factors such as nutrients have less of an influence on the variation in algal blooms because the algal growth demand has been met. Environmental management measures taken by local governments have led to improvements in the lake’s trophic state, and continued strengthening of environmental pollution control is expected to curb the algal blooms in Lake Dianchi. This study provides a long-term record of algal blooms in Lake Dianchi, which provides essential reference information for a comprehensive understanding of the development process of algal blooms in Lake Dianchi and its sustainable development.

Climate change and human activities have been heavily affecting oceanic and inland waters, and it is critical to have a comprehensive understanding of the aquatic optical properties of lakes. Since many key watercolor parameters of Qinghai Lake are not yet available, this paper aims to study the spatial and temporal variations of the water clarity (i.e., Secchi-disk depth, ZSD) and suspended particulate matter concentration (CSPM) in Qinghai Lake from 2001 to 2020 using MODIS images. First, the four atmospheric correction models, including the NIR–SWIR, MUMM, POLYMER, and C2RCC were tested. The NIR–SWIR with decent accuracy in all bands was chosen for the experiment. Then, four existing models for ZSD and six models for CSPM were evaluated. Two semi-analytical models proposed by Lee (2015) and Jiang (2021) were selected for ZSD (R2 = 0.74) and CSPM (R2 = 0.73), respectively. Finally, the distribution and variation of the ZSD and CSPM were derived over the past 20 years. Overall, the water of Qinghai Lake is quite clear: the monthly mean ZSD is 5.34 ± 1.33 m, and CSPM is 2.05 ± 1.22 mg/L. Further analytical results reveal that the ZSD and CSPM are highly correlated, and the relationship can be formulated with ZSD=8.072e−0.212CSPM (R2 = 0.65). Moreover, turbid water mainly exists along the edge of Qinghai Lake, especially on the northwestern and northeastern shores. The variation in the lakeshore exhibits some irregularity, while the main area of the lake experiences mild water quality deterioration. Statistically, 81.67% of the total area is dominated by constantly increased CSPM, and the area with decreased CSPM occupies 4.56%. There has been distinct seasonal water quality deterioration in the non-frozen period (from May to October). The water quality broadly deteriorated from 2001 to 2008. The year 2008 witnessed a sudden distinct improvement, and after that, the water quality experienced an extremely inconspicuous degradation. This study can fill the gap regarding the long-time monitoring of water clarity and total suspended matter in Qinghai Lake and is expected to provide a scientific reference for the protection and management of the lake.

Unsupervised person re-identification (re-ID) is becoming increasingly popular due to its power in real-world systems such as public security and intelligent transportation systems. However, the person re-ID task is challenged by the problems of data distribution discrepancy across cameras and lack of label information. In this paper, we propose a coarse-to-fine heterogeneous graph alignment (HGA) method to find cross-camera person matches by characterizing the unlabeled data as a heterogeneous graph for each camera. In the coarse-alignment stage, we assign a projection for each camera and utilize an adversarial learning based method to align coarse-grained node groups from different cameras into a shared space, which consequently alleviates the distribution discrepancy between cameras. In the fine-alignment stage, we exploit potential fine-grained node groups in the shared space and introduce conservative alignment loss functions to constrain the graph aligning process, resulting in reliable pseudo labels as learning guidance. The proposed domain adaptation framework not only improves model generalization on target domain, but also facilitates mining and integrating the potential discriminative information across different cameras. Extensive experiments on benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-arts.

Spatial covariance matrix (SCM) plays an important role in super-resolution direction-of-arrival (DOA) estimation, which can be derived using traditional sample average approach. Hybrid analog-digital structure has been applied in massive multiple-input multiple-output (MIMO) systems for reducing the cost in millimeter-wave communications. However, the SCM cannot be obtained because the received signals at the antennas are unavailable to the digital receiver in hybrid massive MIMO systems. Based on our previous work, we will present an adaptive reconstruction approach in this paper where the SCM can be derived iteratively using the stochastic gradient descent algorithm. In this way, direct matrix inverse can be avoided so that the computational complexity for SCM reconstruction can be reduced significantly. Converge analyzes and numerical simulations are carried out to demonstrate the advantageous performance of the adaptive reconstruction approach.

Water color is an important parameter in water quality assessment. However, the existing water color investigations have mostly focused on the lakes with areas greater 1 km2. In order to improve the understanding of the color of water bodies in China, a cloud-free composite image of China for the summer of 2015 was generated using time-series of Landsat-8 imagery and the best-available-pixel (BAP) compositing algorithm. Then, the first Forel–Ule index (FUI) water color product with a resolution of 30 m was produced for China using the generated BAP composite and the Google Earth Engine computing platform. Finally, the first national-scale assessment of the FUI of natural lakes with an area >0.01 km2 (N = 60026) was conducted based on the generated FUI product. The generated FUI product was shown to have a high degree of consistency with in situ water surface reflectance-derived FUI (R2 = 0.90, P in situ Secchi depth (R2 = 0.90, P 2 = 0.62, P 2) found to be yellow was much larger than for large lakes (area ≥ 1 km2) (50% against 28%). Our results will provide important information that can be used for preserving and restoring inland water resources.

Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the near-infrared band and combined it with four “common bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the near-infrared band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with Rrs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from Rrs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer Rrs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.

Water color is an important parameter in water quality assessment. However, the existing water color investigations have mostly focused on the lakes with areas greater 1 km2. In order to improve the understanding of the color of water bodies in China, a cloud-free composite image of China for the summer of 2015 was generated using time-series of Landsat-8 imagery and the best-available-pixel (BAP) compositing algorithm. Then, the first Forel–Ule index (FUI) water color product with a resolution of 30 m was produced for China using the generated BAP composite and the Google Earth Engine computing platform. Finally, the first national-scale assessment of the FUI of natural lakes with an area >0.01 km2 ( N = 60026) was conducted based on the generated FUI product. The generated FUI product was shown to have a high degree of consistency with in situ water surface reflectance-derived FUI (R2 = 0.90, P in situ Secchi depth (R2 = 0.90, P P

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide, with high incidence and mortality rates and low survival rates. However, the detailed molecular mechanism of ESCC progression remains unclear. Here, we first showed significantly higher WNT5A and SNAIL expression in ESCC samples than in corresponding paracancerous samples. High WNT5A and SNAIL expression levels correlated positively with lymphatic metastasis and poor prognosis for patients with ESCC based on immunohistochemical (IHC) staining of 145 paired ESCC samples. Spearman’s correlation analyses confirmed the strong positive correlation between WNT5A and SNAIL expression, and patients with ESCC presenting coexpression of WNT5A and SNAIL had the worst prognosis. Then, we verified that the upregulation of WNT5A promoted ESCC cell metastasis in vivo and in vitro, suggesting that WNT5A might be a promising therapeutic target for the prevention of ESCC. Furthermore, WNT5A overexpression induced the epithelial-mesenchymal transition via histone deacetylase 7 (HDAC7) upregulation, and HDAC7 silencing significantly reversed WNT5A-induced SNAIL upregulation and ESCC cell metastasis. In addition, we used HDAC7 inhibitors (SAHA and TMP269) to further confirm that HDAC7 participates in WNT5A-mediated carcinogenesis. Based on these results, HDAC7 is involved in WNT5A-mediated ESCC progression, and approaches targeting WNT5A and HDAC7 might be potential therapeutic strategies for ESCC.

Melatonin is a hormone synthesized in the pineal gland and has widespread physiological and pharmacological functions. Moreover, it can activate protective receptor-dependent processes. These processes can prevent tissue carcinogenesis and inhibit malignant tumor progression and metastasis. Therefore, we investigated the regulatory effects of melatonin on dysregulated circular RNAs in human lung adenocarcinoma (LUAD) cells. In this study, we treated LUAD cells with melatonin and measured the expression of hsa_circ_0017109, miR-135b-3p, and TOX3 by quantitative reverse transcription polymerase chain reaction. Colony formation and cell counting kit-8 assays were used to determine cell proliferation. The wound-healing assay and Transwell experiment were carried out to evaluate the migration potential and invasive capacity of LUAD cells. Also, cell apoptosis was detected using a cell apoptosis kit, and protein production was identified by Western blot. It was suggested that melatonin could inhibit LUAD progression in vivo and in vitro, and the role of TOX3 in this process was explored. Additionally, hsa_circ_0017109 was found to sponge miR-135b-3p, a downstream factor of circ_0017109, which was demonstrated to target TOX3 in LUAD cells and could promote the Hippo pathway and epithelial-mesenchymal transition pathway. To summarize, we demonstrated that melatonin decreases the expression of circ_0017109 and suppresses the non-small-cell lung cancer cell migration, invasion, and proliferation through decreasing TOX3 expression via direct activation of miR-135b-3p.

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

Lakes play an important role in the global carbon cycle; however, there are still large uncertainties in the estimation of global lake carbon emission due to the limitations in conducting field surveys at large geographic scales. Using long-term Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery and field observation data in eutrophic Lake Taihu, we developed a novel approach to estimate the concentration of dissolved carbon dioxide (cCO2) in lakes. Based on the MODIS-derived chlorophyll-a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation, a spatially explicit cCO2 model was developed using multivariate quadratic polynomial regression (coefficient of determination (R2) = 0.84, root-mean-square error (RMSE) = 11.81 μmol L-1, unbiased percent difference (UPD) = 22.46%). Monte Carlo simulations indicated that the model is stable with relatively small deviations in cCO2 estimates caused by input variables (UPD = 26.14%). MODIS data from 2003 to 2018 showed a significant declining trend (0.42 μmol L-1 yr-1, p < 0.05) in the annual mean cCO2. This was associated with a complex balance between the increasing algae biomass and decreasing external inputs of inorganic carbon, nutrients, and organic matter. The high spatiotemporal variabilities in cCO2 were attributed to river inputs and seasonal changes in temperature and algae biomass. The study shows that satellite remote sensing can play an important role in the field of inland water carbon cycling, providing timely much-needed insights into the drivers of the spatial and temporal changes in dissolved CO2 concentrations in inland waters.

Vector maps are distributed frequently among various departments and subscribers, and traditional single-stage watermarking technology cannot provide powerful multi-copyright protection of them. In this paper, a novel multi-stage watermarking algorithm is proposed for vector maps, aiming at robustness enhancement and maximum-supported watermark capacity improvement. A watermark generation method is designed based on QR code to shorten watermark length, and watermarks are embedded into polar coordinates of map vertices so as to improve the robustness of the proposed scheme. Another method of copyright information combination is raised to keep watermark length invariant along with the increasing of the total stages of watermark data, and the maximum-supported stages of watermarks can be thus improved without affecting the robustness of watermarking algorithm. Moreover, each stage of copyright information occupies a specified space within the final watermark data, and non-interference among various watermarks can be guaranteed and different copyright data can be efficiently distinguished after watermark extraction. Compared with the existing researches, the proposed scheme can increase effectively the size of maximum-supported watermark capacity, and it has strong robustness under common geometric and non-geometric attacks, which have been validated by both theoretical analysis and comprehensive experiments.

Multiple signal classification (MUSIC) has been widely applied in wireless communications for direction-of-arrival (DOA) estimation. For massive multiple-input multiple-output (MIMO) systems operating at millimeter-wave bands, hybrid analog-digital structure has been adopted in transceiver design to reduce the cost of radio frequency chains. In hybrid massive MIMO systems, the received signals at the antennas are not sent to the receiver directly, and spatial covariance matrix, which is essential in MUSIC algorithm, is thus unavailable. As a consequence, MUSIC algorithm cannot be directly used in hybrid massive MIMO systems. In this letter, we propose a beam sweeping approach for spatial covariance matrix reconstruction in hybrid massive MIMO systems. In particular, analog beamformer switches the beam direction to a group of predetermined DOA angles in turn, and then the spatial covariance matrix can be reconstructed by solving a set of linear equations. Insightful analysis on the reconstruction accuracy is also presented in this letter. Simulation results show that the proposed approach can reconstruct the spatial covariance matrix accurately so that MUSIC algorithm can be well used for DOA estimation in hybrid massive MIMO systems.

Diabetic retinopathy (DR) is considered a main cause for vision loss in diabetes. To our knowledge, there were no studies on the association of genetic variants with DR in Chinese Hui nationality. ...

Grassland ecosystems account for more than 10% of the global CH4 sink in soils. A 4-year field experiment found that addition of P alone did not affect CH4 uptake and experimental addition of N alone significantly suppressed CH4 uptake, whereas concurrent N and P additions suppressed CH4 uptake to a lesser degree. A meta-analysis including 382 data points in global grasslands corroborated these findings. Global extrapolation with an empirical modelling approach estimated that contemporary N addition suppresses CH4 sink in global grassland by 11.4% and concurrent N and P deposition alleviates this suppression to 5.8%. The P alleviation of N-suppressed CH4 sink is primarily attributed to substrate competition, defined as the competition between ammonium and CH4 for the methane mono-oxygenase enzyme. The N and P impacts on CH4 uptake indicate that projected increases in N and P depositions might substantially affect CH4 uptake and alter the global CH4 cycle.

Clinical benefit of neoadjuvant immunotherapy in resectable esophageal squamous cell carcinoma (ESCC). remains unclear. This study evaluated the efficacy and safety of the programmed death 1 (PD-1) inhibitor tislelizumab combined with chemotherapy as neoadjuvant therapy in patients with resectable ESCC.Treatment-naïve patients were enrolled and eligible patients received 3 cycles of neoadjuvant therapy with tislelizumab, carboplatin, and nab-paclitaxel. The primary endpoint was surgery patients major pathological response (MPR). Subgroup analysis was stratified by tumor downstaging, circumferential resection margin (CRM), PD-ligand 1 (PD-L1) expression, and tumor mutation burden (TMB). Safety was assessed by adverse events (AEs) and postoperative complications.Between September 2020 and March 2021, 45 patients were enrolled. Thirty-six (80.0%) of 45 patients underwent surgery, and 29 (80.5%) underwent successful R0 resection. MPR and pathological complete response (pCR) for surgery patients were 72.0% and 50.0%, respectively. Intention to treatment (ITT) patients MPR and PCR were 57.5% and 40%. Downgrading occurred in 75% of 36 patients. MPR and pCR were identified to be associated with tumor downstaging and CRM but not PD-L1 expression or TMB. TPS levels in MPR and pCR group were significantly higher than that in Non-MPR and Non-pCR group, respectively. Treatment-related AEs of grade 3-4 and immune-related AEs occurred in 42.2% and 22.2% of 45 patients, respectively, and postoperative complications occurred in 77.8% of 36 patients. No treatment-related surgical delay or death occurred. No associations between gene mutation and pathological efficacy were observed.Tislelizumab plus chemotherapy as neoadjuvant therapy demonstrates promising antitumor activity for resectable ESCC with high rates of MPR, pCR, and R0 resection, as well as acceptable tolerability.

Oblique photography technology based on UAV (unmanned aerial vehicle) provides an effective means for the rapid, real-scene 3D reconstruction of geographical objects on a watershed scale. However, existing research cannot achieve the automatic and high-precision reconstruction of water regions due to the sensitivity of water surface patterns to wind and waves, reflections of objects on the shore, etc. To solve this problem, a novel rapid reconstruction scheme for water regions in 3D models of oblique photography is proposed in this paper. It extracts the boundaries of water regions firstly using a designed eight-neighborhood traversal algorithm, and then reconstructs the triangulated irregular network (TIN) of water regions. Afterwards, the corresponding texture images of water regions are intelligently selected and processed using a designed method based on coordinate matching, image stitching and clipping. Finally, the processed texture images are mapped to the obtained TIN, and the real information about water regions can be reconstructed, visualized and integrated into the original real-scene 3D environment. Experimental results have shown that the proposed scheme can rapidly and accurately reconstruct water regions in 3D models of oblique photography. The outcome of this work can refine the current technical system of 3D modeling by UAV oblique photography and expand its application in the construction of twin watershed, twin city, etc.

BACKGROUND: Centromere protein U (CENP-U) is a component of the kinetochore and can regulate the cell cycle as a receptor of polo-like kinase 1 (PLK1). Recent studies have partially identified the role of CENP-U in tumor progression, but the underlying mechanisms of CENP-U in tumor immunity remain obscure. METHODS: We performed pan-cancer analysis to evaluate the role of CENP-U in immunity and proliferation with data from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE) datasets, and Genotype-Tissue Expression (GTEx) project. Results of CENP-U expression and related clinicopathological data were obtained to show the expression levels, prognosis, tumor progression, immune neoantigens, and immune checkpoints of CENP-U in 33 tumors. The Tumor Immune Estimation Resource (TIMER) dataset was used to analyze immune infiltration scores. RESULTS: Results of the pan-cancer analysis demonstrated that CENP-U is differentially expressed in normal tissues and common tumor tissues. Moreover, differentially expressed CENP-U was also identified between matched normal and tumor tissues, and the high expression level of CENP-U was associated with poor prognosis for 33 kinds of tumor except for that of thymoma (THYM) and lymphoid neoplasm diffuse large B-cell lymphoma (DLBC). Furthermore, the correlation between CENP-U expression and immune checkpoints and immune neoantigens was determined. In addition, CENP-U expression was correlated with tumor-infiltrating immune cells especially in THYM but not in lung squamous cell carcinoma (LUSC), esophageal carcinoma (ESCA), or lung adenocarcinoma (LUAD). Finally, gene set enrichment analysis (GSEA) indicated that CENP-U is critically involved in tumor proliferation, immunity, and metabolism. CONCLUSIONS: CENP-U, a mitosis-related kinase, was found to be differentially expressed across different cancer types and to play an important role in tumor progression and immunity. CENP-U holds the potential to be a prognostic marker, whose targeting may provide therapeutic benefit.