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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 P

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.

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. ...

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.

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.

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.

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.

High spatial resolution satellite sensors provide opportunities to observe spatial variations of biogeochemical properties of small- and medium-sized inland water bodies. However, high spatial resolution sensors are usually equipped with wider spectral bandwidth (>50 nm) that diminishes the features of the spectrum. Therefore, the effects of the border bandwidth issue need to be evaluated prior to application in aquatic environments. Based on the in situ optical data [remote sensing reflectance (Rrs) and absorption coefficients] and the radiative simulations of hyperspectral remote sensing reflectance and using band specifics of common sensors (e.g., OLCI, VIIRS, MSI, OLI, ETM+ and WFV) as examples, the effects of bandwidth on optical properties of inland waters were analyzed. The results showed the followings. (1) The difference between values at center-wavelength and band-averaged values increased with increasing bandwidth for Rrs and the absorption coefficients. The difference was wavelength-dependent. The difference of Rrs at the visible band was within 0.25% but greater than 0.5% for the spectral bands near 710 nm and 665 nm. (2) The accuracy of the total absorption coefficient derived from QAA-750E, spectral match technique (SMT) and deep neural network (DNN) decreased with increasing bandwidth. The QAA-750E was more sensitive to bandwidth than SMT and DNN. Otherwise, the empirical algorithms for estimating chlorophyll-a (Chla) concentrations were significantly affected by bandwidth. The performance of algorithms for estimating cyanobacterial phycocyanin (PC) and suspended particulate matter (SPM) concentrations changed slightly with a wider bandwidth. Finally, the maximum bandwidth requirement for optical remote sensing in inland waters was proposed. For bandwidth options, it should be within 20 nm for 700–710 nm, ∼30 nm maximum for ∼560 nm and ∼665 nm, 60 nm for ∼620 nm, and ∼80 nm for ∼443 nm and ∼490 nm, respectively. The difference between the Rrs of narrow bands (10–20 nm) and the Rrs of the bands with the recommended bandwidth was within 0.25%. The corresponding bandwidth from MSI and OLI sensors meet this criterion for Chla and SPM. However, the lack of spectral coverage near 700–710 nm may present a challenge to retrieve Chla concentration from OLI images. This study provided helpful theoretical and practical references for the retrieval of inland water parameters by high spatial resolution satellite sensors and its prospective development.

Inherent optical properties (IOPs) play an important role in underwater light field, and are difficult to estimate accurately using satellite data in optically complex waters. To study water quality in appropriate temporal and spatial scales, it is necessary to develop methods to obtain IOPs form space-based observation with quantified uncertainties. Field-measured IOP data (N = 405) were collected from 17 surveys between 2011 and 2017 in the three major largest freshwater lakes of China (Lake Chaohu, Lake Taihu, and Lake Hongze) in the lower reaches of the Yangtze River and Huai River (LYHR). Here we provide a case-study on how to use in-situ observation of IOPs to devise an improved algorithm for retrieval of IOPs. We then apply this algorithm to observation with Sentinel-3A OLCI (Ocean and Land Colour Instrument, corrected with our improved AC scheme), and use in-situ data to show that the algorithm performs better than the standard OLCI IOP product. We use the satellite derived products to study the spatial and seasonal distributions of IOPs and concentrations of optically active constituents in these three lakes, including chlorophyll-a (Chla) and suspended particulate matter (SPM), using all cloud-free OLCI images (115 scenes) over the lakes in the LYHR basin in 2017. Our study provides a strategy for using local and remote observations to obtain important water quality parameters necessary to manage resources such as reservoirs, lakes and coastal waters.

Background: Multimodal therapies, such as perioperative chemotherapy and neoadjuvant chemoradiotherapy together with surgical advances, have improved the outcomes of patients with resectableesophageal cancer (EC).However, the 5-year survival of these patients remains below 50%. The advent of immunotherapy has resulted in novel approaches to advanced or metastatic disease, but the role of immunotherapy in the neoadjuvant settings has not yet to be established. In this single-arm study, the efficacy and safetyofneoadjuvant pembrolizumab plus chemotherapy were evaluatedin patients with resectableesophageal squamous cell carcinoma (ESCC).Methods: This study was conducted at 2 hospitals(in Xi'an and Tianjin)and included patients with ESCC of clinical stage II–IVAwho underwent surgery within 4 to 6 weeks after completing treatment withpembrolizumab(200mg) combined with a conventional chemotherapy regimen (3 cycles). The efficacy and safety of this combination treatment were evaluated as primary endpointsof the study. This study was retrospectively registered (Registration number: ChiCTR2100048917).Results:A total of 22 patients (including 18 men) were enrolled, of whom 17 patients progressed to surgery,4 patients refused surgery due to tumor regression and symptomatic relief, and 1 patient had progressive disease. As determinedby the Response Evaluation Criteria in Solid Tumors(RECIST,version 1.1), 7 patients (31.8%) had a partial response and 4 (18.2%) patients hada complete response, which translated to an objective response rate of 50%. The symptom remission rate was 90.9% according to the Stooler classification. Five patients (22.7%) experienced serious treatment-related adverse events of grade 3–4 (including anorexia, vomiting, fatigue, alopecia and hypoadrenalism). Postoperative pathology revealed a major pathological response in 14 cases (82.3%) and a complete pathological response in 8 cases (47.1%). Programmed death-ligand 1expression in pretreatment specimens was not significantly associated with the percentage of residual viable tumor (RVT)(r=-0.55, p=0.08). Changes in counts of CD68+ macrophage between pre- and post-treatment specimens wereweakly correlated with RVT(r=0.71; p=0.07), while a positive correlation was observed between postoperative Foxp3+T cells/CD4+Tcellsratios and RVT(r=0.84, p=0.03).Conclusion: The combination of neoadjuvantimmunotherapy and chemotherapy for ESCC is associated with a high pathological response and immunologic effects in the tumor microenvironment. It has acceptable toxicity and great efficacy, suggesting a strong rationale for its further evaluation in randomized clinical trials.Trial registration: ChiCTR, ChiCTR2100048917. Registered 19 July 2021 - Retrospectively registered, https://www.chictr.org.cn/edit.aspx?pid=130073&htm=4

Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red/blue band ratio algorithm was applied to map SDD for lakes (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found that the lakes with SDDs  4 m were the largest, accounting for 48.28 % of the total lakes. During 1984–2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia-Xinjiang lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01–1 km2, whereas that in the TQR and MXR were primarily concentrated in large lakes (> 10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571.

The impacts of COVID-19 lockdowns on air quality around the world have received wide attention. In comparison, assessments of the implications for water quality are relatively rare. As the first country impacted by COVID-19, China implemented local and national lockdowns that shut down industries and businesses between January and May 2020. Based on monthly field measurements (N = 1693) and daily automonitoring (N = 65), this study analyzed the influence of the COVID-19 lockdown on river water quality in China. The results showed significant improvements in river water quality during the lockdown period but out-of-step improvements for different indicators. Reductions in ammonia nitrogen (NH4+-N) began relatively soon after the lockdown; chemical oxygen demand (COD) and dissolved oxygen (DO) showed improvements beginning in late January/early February and mid-March, respectively, while increases in pH were more temporally concentrated in the period from mid-March to early May. Compared to April 2019, the Water Quality Index increased at 67.4% of the stations in April 2020, with 75.9% of increases being significant. Changes in water quality parameters also varied spatially for different sites and were mainly determined by the locations and levels of economic development. After the lifting of the lockdown in June, all water quality parameters returned to pre-COVID-19 lockdown conditions. Our results clearly demonstrate the impacts of human activities on water quality and the potential for reversing ecosystem degradation by better management of wastewater discharges to replicate the beneficial impacts of the COVID-19 lockdown. Capsule summary River water quality improved during China's COVID-19 lockdown, but returned to normal conditions after the lockdown., Graphical abstract Unlabelled Image

Antibiotics receive many concerns since their negative environmental impacts are being revealed, especially in aqua-agricultural areas. Rainfall events are responsible for transferring excess contaminants to receiving waters. However, the understanding of antibiotics transport and fate responding to rainfall events was constrained by limited event-based data and lacking integrated consideration of dissolved and particulate forms. We developed an intensive monitoring strategy to capture responses of fourteen antibiotics to different types of rainfall events and inter-event low flow periods. Pollutant-rich suspended particles, as high as 1471 ng/g, were found in low flow periods while the very heavy rainfall events and consecutive rainfall events stimulated the release of antibiotics from eroded soil particles to river water. Therefore, these rainfall events drove radical increase of dissolved antibiotic concentration up to 592 ng/L and total flux up to 25.0 g/d. Sulfonamides were particularly sensitive to rainfall events because of their residues in manure-applied agricultural lands. Transport dynamics of most antibiotics were accretion whereas only clarithromycin exhibited a dilution pattern by concentration-discharge relationships. Aquaculture ponds were inferred to significantly contribute tetracycline, oxytetracycline, and clarithromycin. Conventional contaminants were compared to discriminate potential sources of antibiotics and imply effective catchment management. The results provided novel insights into event-based drivers and dynamics of antibiotics and could lead to appropriate management strategy.

Neoadjuvant immunotherapy for patients with locally advanced esophageal cancer (EC) has developed rapidly in recent years. The efficacy and safety outcomes may change the recommended neoadjuvant therapeutic regimens for patients with EC.Systematic screening for articles focusing on the efficacy and safety of neoadjuvant immunotherapy in locally advanced and surgically resectable EC was performed using PubMed, Embase, Web of Science and international tumor congresses. Meta-analysis of non-comparative binary outcomes was carried out to combine the main results. The pooled results were compared with the traditional neoadjuvant chemotherapy (nCT) and chemoradiotherapy (nCRT) using direct comparative analysis. The results were expressed as the risk ratio (RR).A total of 20 articles with 621 patients were included in the present study. The pooled pathological complete response and major pathological response rates were 33.8% (95% CI: 29.6%-37.9%) and 53.5% (95% CI: 47.9%-59%), respectively, in the neoadjuvant immunotherapy combined with chemotherapy (nICT) group and 39.8% (95% CI: 27%-53.9%) and 88.8% (95% CI: 64.8%-97.2%) in the neoadjuvant immunotherapy combined with chemoradiotherapy (nICRT) group, respectively. In addition, the pooled grade 3-4 treatment-related adverse events (TRAEs) rate was 19.4% (95% CI: 11.5%-31.5%) in the nICT group. The results of direct comparison showed that compared with nCRT and nICRT, nICT could improve safety while achieving comparable efficacy. The results of subgroup analysis, sensitivity analysis and publication bias evaluation indicated that the above findings were stable and reliable.The current meta-analysis revealed that neoadjuvant immunotherapy in patients with locally advanced EC was safe and effective and nICT could be used as the recommended neoadjuvant therapeutic option for patients with EC. However, additional studies are urgently needed to reveal the long-term outcomes of neoadjuvant immunotherapy.

The generation of organic matter (OM) occurs synchronously with phytoplankton growth. Characterization of the generated particulate and dissolved OM during algal blooms in eutrophic lakes is crucial for better understanding the carbon cycle but remains limited. We speculate that sediments play a critical role in the biogeochemical transformation of OM derived from algal blooms in shallow lakes. In this study, changes in OM quantity and quality and the concentrations of biogenic elements (nutrients and metals) during algal blooms, were studied in situ in a shallow eutrophic lake (Lake Chaohu, China). Two enclosure treatments in the presence and absence of sediments were compared, and the cause-effect relationships among sediment, nutrients, metals, phytoplankton, particulate OM (POM), and dissolved OM (DOM) were revealed by a partial least square-path model (PLS-PM). The results showed that the changes in nutrients and metals concentrations over time were consistent with that of chlorophyll a (Chl a), and at the end of the treatment, the concentrations of Chl a, nutrients, and metals in Treatment S (with sediments) were approximately 3-5 times of those in Treatment N (without sediments). The high concentration of Chl a in Treatment S resulted in a high quantity of POM, which showed low molecular weight, low humification, and was enriched in protein-like components (∼ 70%). For DOM, the quantity increased after the decrease in POM, and DOM quality showed a significantly higher abundance of humic-like components and a higher molecular weight than POM did. The PLS-PM results showed that the significant positive effects of sediment on nutrients, metals, phytoplankton, POM, and DOM were 0.28, 0.37, 0.28, 0.25, and 0.25, respectively, suggesting that sediment had an important role in the biogeochemical cycles of these substances. The significant negative relationship between POM and DOM (-0.62) and the distinct difference in POM and DOM quality implied the efficient transformation of the freshly generated OM to those with a higher molecular weight, higher humification, and potentially refractory. Our results depicted the quick biogeochemical transformation of nutrients, metals, and the potential formation of refractory organic carbon in water column, as driven by the couple of the algae pump with the microbial carbon pump.

Dendrobium Sw. has been used for thousands of years in China as a precious traditional Chinese medicine. It is derived from stems of various Dendrobium plants and has the functions of nourishing Yin and clearing heat, activating water and nourishing the stomach, moistening the lung and relieving cough. Modern phytochemical studies show that the main components of Dendrobium include alkaloids, polysaccharides, terpenoids, diphenylbenzene, and phenanthrene. Alkaloids are natural products with obvious biological activity and are important effective components of the medicinal activity or toxicity of plants. At present, dozens of alkaloids with various structures have been isolated from Dendrobium plants, and the alkaloid contents in Dendrobium plants of different species are quite different. From the perspective of food safety, the type, molecular structure, content and potential physiological activity or toxicity of alkaloids are important bases for evaluating the safety of edible plants. Studies have shown that the alkaloids isolated from Dendrobium have neuroprotective, anti-inflammatory and antitumor activities, showing that these alkaloids with potential medicinal activity are important sources of lead compounds in innovative drug development.To summarize the research progress on alkaloids in Dendrobium and provide a reference for research on the food safety and medicinal development of Dendrobium.Information about alkaloids from Dendrobium was collected from the scientific databases Web of Science, PubChem and PubMed. We discuss the biosynthetic pathway, biological activities and total synthesis of alkaloids from Dendrobium from 1964 to 2020 and summarize the knowledge of alkaloids from Dendrobium, the biosynthetic pathway, biological activities and total synthesis. We chose publications on their chemistry, drug effects, pharmacology, metabolism and biosynthesis, physiology and toxicity. Alkaloids, Dendrobium, biosynthetic pathway and biological activities were used as keywords to extract the relevant literature.In this paper, the structural classification, biological activity, target and toxicology and synthesis of the alkaloids in Dendrobium were systematically reviewed, which will provide a reference for the safety, development and application of Dendrobium.

e20615 Background: Combine chemotherapy and immunotherapy have become the first-line treatment program for extensive small cell lung cancer. The aim of this study is to explore the safety and efficacy of neoadjuvant chemotherapy and immunotherapy in limited-stage small cell lung cancer. Methods: Treatment-naïve patients were enrolled and the patients received the 2-3 cycles of neoadjuvent chemotherapy and atezolizumab (1200mg, D1). surgery was performed within 4-6 weeks after the last treatment. The primary endpoint was surgery patients pathological complete response (PCR). Safety was assessed by adverse events (AEs) and postoperative complications. This trial was registered at the Chinese Clinical Trial Registry (Registration Number: (ChiCTR2100042367). Results: Recruitment started in May 2020. We reported 9 patients with neoadjuvent immunochemotherapy who have completed surgical treatment. Baseline characteristics: 7 males, with an average age of 52.1 years old, all ECOG PS 0-1; 6 current smokers; clinical stages IIB / IIIA / IIIB n = 6 / 1 / 2. There were 1 atezolizumab treatment-related Gr1 AEs (rush) and 4 treatment-related Gr 3-4 AEs. According to Recist 1.1, all patients achieved partial response. 9 patients completed surgery, 3 patients received bilobectomy, and 3 patients underwent pneumonectomy. Surgical methods included robot-assisted thoracoscopic surgery (RATS) /video-assisted thoracoscopic surgery (VATS) (3, 33.3%) and thoracotomy (6, 66.7%). Complete resection (R0) was achieved in 100% of patients (9/9). The mean operation time was 136 min; the mean bleeding volume was 194.4ml, and the mean hospital stay was 8.9 days. The postoperative pathology results showed a PCR (66.7%) in 6 patients and a MPR (88.9%) in 8 patients. Conclusions: Neoadjuvant chemotherapy and atezolizumab therapy significantly improves PCR in small cell lung cancer without unknown AE events and no surgical delay, so this protocol is safe and feasible. Whether the survival benefit is still unknown. Keywords: neoadjuvant therapy, Small cell lung cancer, immunotherapy, surgery, PCR. Clinical trial information: ChiCTR2100042367. [Table: see text]

Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains operating at millimeter-wave bands, hybrid analog-digital structure has been adopted in massive MIMO transceivers. In this situation, the received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. In addition, based on insightful analysis on that linear equations, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used for DOA estimation in hybrid massive MIMO systems with multiple RF chains.

BACKGROUND: Preoperative chemotherapy or chemoradiotherapy is the standard treatment for resectable esophageal cancer (EC); however, it is associated with increased postoperative complications and mortality. Recently, Immune Checkpoint inhibitors have been incorporated in the treatment of advanced EC. Its role in the preoperative setting has not been established yet. In this multicenter, single-arm study, we evaluated the efficacy and safety of neoadjuvant therapy with sintilimab in combination with chemotherapy in treating EC. METHODS: Patients received neoadjuvant therapy with 3 cycles of sintilimab 200 mg Q3W in combination with platinum-based chemotherapy. Surgery was performed within 4–6 weeks after neoadjuvant therapy. The primary endpoints of the trial were pathological complete response (pCR) and safety. RESULTS: A total of 23 patients (21 men and 2 women) were enrolled. Surgery was completed in 17 participants, with 16 achieving R0 resection and 1 had R1 resection, 5 participants refused surgery. One patient progressed prior to surgery. Twenty one patients (91%) had significant improvement in their dysphagia following treatment as assessed by Stooler’s criteria. The majority of patients who underwent resection have a good pathological response and downstaging rate was 76.5% (13/17). A pCR was achieved in 6 cases (6/17, 35.3%) and major pathological response (MPR) in 9 cases (9/17, 52.9%). The main preoperative adverse events (AEs) were vomiting (13/23, 56.5%), leukopenia (12/23, 52.2%), neutropenia (9/23, 39.1%), and malaise (8/23, 34.8%). Immune-related AEs were mild and included hypothyroidism (2/23, 8.7%) and rash (4/23, 17.4%). The incidence of ≥ grade 3 treatment related AEs was 30.4% (7/23). There were no ≥ grade 4 AEs. CONCLUSIONS: Sintilimab in combination with chemotherapy in the neoadjuvant treatment of EC is safe and lead to a high pCR. Therefore, further testing is warranted.

Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R2 of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier.

River water quality (RWQ) data has obvious characteristics of spatial and temporal distribution, and tables are conventionally exploited for storage of multi-period monitoring data of RWQ; however, neither effective visualization nor accurate analysis of the obtained data can be realized due to its dispersion character. In this paper, a novel spatiotemporal data model is proposed for RWQ data to realize conveniently data representation and spatiotemporal analysis. In this model, a spatial point, containing both location and dynamic water quality information, is considered as the basic element of river spaces, and methods of expanding a point to a line segment, a flat surface and a cube are designed respectively so as to make this model be applicable to different generalizations of river spaces. Moreover, a temporal data storage structure is designed so that efficient inquiry and advanced analysis of RWQ data can be guaranteed and the occupied memory space can be reduced. Finally, case studies are conducted by performing 3D visualization, trend analysis and anomaly identification on RWQ data, the result of which showing that tridimensional representation of RWQ data can be realized efficiently, the computational complexity is reduced significantly and the occupied memory space of monitoring data is effectively economized. Accordingly, the proposed spatiotemporal data model can contribute to the efficient visualization and advanced analysis of RWQ data.

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash–Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.

Artificial water diversion project has been increasingly implemented to control eutrophication and alleviate water shortage of lakes. Water diversion can affect the biogeochemical cycles, however, carbon dioxide (CO2) sensitivity in lakes to water diversion was poorly understood. This study addressed the issue based on the famous Water Diversion project from the Yangtze River to eutrophic Lake Taihu, which has been carried out regularly since 2002 in China. The partial pressure of CO2 (pCO2) at two regions of the eutrophic lake, water-receiving Gonghu Bay and non-water-receiving Central Zone, was investigated over 16 years (2002–2017) field measurement with high sampling frequency. Results showed the river-to-lake water diversion decreased phytoplankton biomass, increased nutrient loadings, and significantly elevated the pCO2 and associated CO2 emission. Meanwhile, the water diversion project accounted for the notable CO2 spatial heterogeneity between regions and within region. However, weak CO2 seasonal variability was observed at Gonghu Bay during water diversion period. The estimated CO2 emission flux increased averagely by 54% due to water diversion, primarily resulting from phytoplankton dilution, nutrient enrichment, and external CO2 input. Water diversion and ammonia nitrogen together explained 69% of interannual variability pCO2 at Gonghu Bay, implying water diversion should be considered to better understanding the contribution of lake CO2 emissions to C budget under a changing environment. These findings were expected to serve as a reference to evaluate the water diversion effects, further study should focus on the sources of the excess CO2 to make the project a CO2-neutral option.

The estimation of chlorophyll-a (chl-a) concentration remains a great challenge in turbid waters due to their complex optical conditions. To improve chl-a estimation, this study aims to determine whether combined use of polarimetric synthetic-aperture radar (SAR) data has potential for improving the chl-a estimation from hyperspectral sensing reflectance for turbid waters such as those found in Lake Taihu, China. In situ measurements of hyperspectral reflectance data and water samples were collected over the lake corresponding to ENVISAT ASAR data. Semiempirical (two-band and three-band models) and empirical [multiple linear regression (MLR) and multilayer perceptron network (MLP)] models are compared to estimate the chl-a concentration from in situ hyperspectral reflectance and SAR data. The results show that there is a general underestimation of chl-a for concentrations higher than 26 ug/L, which is probably caused by the large spatial variation of chl-a in the study area. The results also demonstrate that the MLR model performs in a more stable manner than the MLP network does, while MLP underestimates low and high areas of chl-a concentrations in the lake. On the other hand, due to the availability of one scenic SAR data on the same day, our results show that the additional use of SAR data improved chl-a estimation very slightly in this case study, although the performance of vertical/vertical polarization SAR data was better than that of horizontal/horizontal polarization data in chl-a estimation. Potential future work in this subject could explore other measures of mutual information between SAR and hyperspectral optical data beyond the correlation and regression techniques described. Therefore, it is still necessary to apply more SAR data in varied turbid waters in the near future to determine how SAR data can be useful in the improvement of chl-a estimation.

Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complexity of inland water. Therefore, this study applied a deep neural network model to monitor the vertical distribution of Chlorophyll-a (Chl-a), phycocyanin (PC), and turbidity (Turb) using drone-borne hyperspectral imagery, in-situ measurement, and meteoroidal data. The pigment concentrations were measured between depths of 0 m and 5.0 m with 0.05 m intervals. Here, four state-of-the-art data-driven model structures (ResNet-18, ResNet-101, GoogLeNet, and Inception v3) were adopted for estimating the vertical distributions of the harmful algal pigments. Among the four models, the ResNet-18 model showed the best performance, with an R2 value of 0.70. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) substantially provided informative reflectance band ranges near 490 nm and 620 nm in the hyperspectral image for the vertical estimation of pigments. Therefore, this study demonstrated that the explainable deep learning model with drone-borne hyperspectral images has the potential to estimate Chl-a, PC, and Turb vertical distributions and to show influential features that contribute to describing the vertical profile phenomena.

Dissolved organic carbon (DOC) in lakes serves as a substrate for heterotrophic bacterial growth, a regulator of the global carbon cycle, and a light absorption agent. DOC in eutrophic lakes is greatly influenced by phytoplankton phenology and terrigenous input by rivers. Therefore, it is necessary and significant to dynamically monitor the concentration, storage, and riverine exchange flux of DOC. By using in-situ DOC measurements from 2004 until 2018 (N = 2019), a machine learning algorithm, namely, a multilayer back-propagation neural network (MBPNN) model, was developed in this work to improve the remote sensing estimation of DOC concentrations in eutrophic Lake Taihu. The model yielded a mean estimation error of 15.14% for the testing dataset. The monthly mean DOC concentration significantly increased from 2003 to 2018 (N = 192, p

Co-Fe layered double hydroxide (Co-Fe LDH) prepared by the co-precipitation method was proposed as the Fenton-like catalyst for nitrobenzene removal. The properties of prepared catalysts were characterized by BET, SEM and XRD analyses. The initial pH, hydrogen peroxide concentration, catalyst dosage and the nitrobenzene concentration on the removal of nitrobenzene have been studied. The best removal efficiency (100%) was obtained at pH = 2.7, [H 2 O 2 ] 0 = 500 mg/L, catalyst dosage = 1 g/L for the initial concentration of 50 mg/L nitrobenzene. Co-Fe LDH retained the catalytic activity after five cycles. The mechanism of nitrobenzene oxidation was radical mediated process, confirmed by the addition of T-butanol (·OH scavenger). The iron leaching was only 12.47 mg/L even at pH 2.7. Moreover, the intermediates were detected by LC/MS and a possible pathway for nitrobenzene removal in the Co-Fe LDH and H 2 O 2 system was proposed.

Changes in global climate drivers have multiple impacts on lake ecosystems, as rain and wind conditions control catchment surface runoff and lake mixing regimes. However, human activities in lakes and their watersheds may have direct and indirect impacts on aquatic optical properties. Therefore, identifying key drivers that can be controlled (human) from those that cannot (climate) represents an important objective. In the present study, we develop an algorithm to estimate the concentrations of suspended particulate matter (SPM) in Lake Hongze (the fourth largest freshwater lake in China) using MODIS/Aqua images with concurrent data collected from six cruise surveys. The algorithm resulted in root mean square errors (RMSEs) of 7.64–7.86 mg/L for SPM ranging from 10 to 80 mg/L. The algorithm was applied to 1602 cloud-free MODIS/Aqua images from 2002 to 2015. Our results show: (1) inter-annual and seasonal variations of SPM concentrations in Lake Hongze are divided into two distinct periods between 2002 and 2011 and 2012–2015, with the transition associated to intensive dredging activities that were initiated in 2012. (2) SPM concentrations exhibit four typical patterns of spatial distribution which depend on local meteorological (wind speed and wind direction) and hydrological conditions (catchment rainfall and Huai River flowrate). Based on these results, a new spatial zoning of the lake is derived to support government and agency monitoring. The study shows additive and synergistic effects of climate change and human activities on SPM concentrations over short and long timescales and the possibility to monitor these changes by remote sensing in shallow optically complex lakes.

The Middle and Lower Yangtze River (MLY) basin holds the most freshwater in East Asia; however, the conditions of basin-scale water turbidity remain unknown. In this work, a remote sensing algorithm was developed to estimate the concentrations of the total suspended sediments (TSS) in large lakes and reservoirs over the MLY basin and was based on a band ratio between 555 nm and 645 nm of the atmospherically corrected surface reflectance of the Moderate Resolution Imaging Spectroradiometer (MODIS). In situ samples used to calibrate the algorithm were collected from 58 lakes and reservoirs with a TSS range of 1 to 300 mg L − 1 , and the uncertainty of this algorithm was 30–40%. The algorithm was subsequently applied to a total of 102 lakes and reservoirs located in the MLY basin to derive TSS maps from 2000 to 2014 at a 250 m spatial resolution, and the first comprehensive document of the TSS distributions and dynamics of large inland waters of the MLY basin was created. The seasonal patterns among the selected water bodies were similar, with the largest TSS concentrations occurring in the first and fourth quarters in a year and the lowest values occurring in the third quarter. In contrast, spatial heterogeneities were revealed by the 15-year long-term mean TSS climatology information. Although most lakes downstream of Poyang Lake were turbid with 15-year TSS climatology values of 45–100 mg L − 1 , waters between Poyang and Doting Lake were relatively clearer with TSS climatology values of 15–45 mg L − 1 , and the clearest waters ( − 1 ) were found in reservoirs. The turbidity of 64.5% (e.g., 49/76) for lakes in Class II exhibited a decreasing trend over the 15-year period, and the Three Gorges Reservoir (TGR) and Dongting Lake in Class I also showed significant TSS declines. Analysis with meteorological data shows that the intra-annual variations appear to be significantly correlated with local precipitation, with a time lag of two months for TSS. The prominent TSS decreasing trend of the lakes in Class II was probably linked to the significant NDVI increase in the MLY basin, whereas the TSS decrease in the TGR and Dongting Lake is likely to be attributed to the impoundment of the Three Gorges Dam. The TSS environmental data record (EDR) of large inland waters presented in this study serves as an important reference for future water quality monitoring and evaluation in the MLY and in China.