Cancer associated fibroblasts-derived SULF1 promotes gastric cancer metastasis and CDDP resistance through the TGFBR3-mediated TGF-ß signaling pathway.

SULF1 has been implicated in a number of malignancies. The function of SULF1 in gastric cancer is disputed. The objective of this study was to examine the role and underlying molecular mechanisms of SULF1 in the context of gastric cancer. We found that the expression of SULF1 was increased in gastric cancer, especially in cancer-associated fibroblasts. The overexpression of SULF1 was found to be significantly correlated with unfavorable prognosis among individuals diagnosed with gastric cancer. Functionally, cancer-associated fibroblasts-derived SULF1 served as a oncogenic molecule which facilitated gastric cancer cells metastasis and CDDP resistance. Mechanistically, SULF1 regulated the communication between gastric cancer cells and cancer-associated fibroblasts in tumor microenvironment as a signaling molecule. Cancer-associated fibroblasts-secreted SULF1 interfered with the interaction between TGF-ß1 and TGFBR3 by combining with TGFBR3 on gastric cancer cell membrane, subsequently activated TGF-ß signaling pathway. In conclusion, our findings have presented novel approaches for potential treatment and prognosis prediction in individuals diagnosed with gastric cancer through the targeting of the CAFs-SULF1-TGFBR3-TGF-ß1 signaling axis.

Low-Toxicity and High-Efficiency Streptomyces Genome Editing Tool Based on the Miniature Type V-F CRISPR/Cas Nuclease AsCas12f1.

Genome editing tools based on SpCas9 and FnCpf1 have facilitated strain improvements for natural product production and novel drug discovery in Streptomyces. However, due to high toxicity, their editing requires high DNA transformation efficiency, which is unavailable in most streptomycetes. The transformation efficiency of an all-in-one editing tool based on miniature Cas nuclease AsCas12f1 was significantly higher than those of SpCas9 and FnCpf1 in tested streptomycetes, which is due to its small size and weak DNA cleavage activity. Using this tool, in Streptomyces coelicolor, we achieved 100% efficiency for single gene or gene cluster deletion and 46.7 and 40% efficiency for simultaneous deletion of two genes and two gene clusters, respectively. AsCas12f1 was successfully extended to Streptomyces hygroscopicus SIPI-054 for efficient genome editing, in which SpCas9/FnCpf1 does not work well. Collectively, this work offers a low-toxicity, high-efficiency genome editing tool for streptomycetes, particularly those with low DNA transformation efficiency.

Unveiling the oncogenic role of CLDN11-secreting fibroblasts in gastric cancer peritoneal metastasis through single-cell sequencing and experimental approaches.

BACKGROUND: Fibroblasts are necessary to the progression of cancer. However, the role of fibroblasts in peritoneal metastasis (PM) of gastric cancer (GC) remains elusive. In this study, we would explore the role of fibroblasts mediated cell interaction in PM of GC. METHODS: Single-cell sequencing data from public database GSE183904 was used to explore the specific fibroblast cluster. Fibroblasts were extracted from PM and GC tissues. The expression level of CXCR7 was verified by western blot, immunohistochemistry. The role of CLDN11 was investigate through in vitro and in vivo study. Multiple immunohistochemistry was used to characterize the tumor microenvironment. RESULTS: CXCR7-positive fibroblasts were significantly enriched in PM of GC. CXCR7 could promote the expression of CLDN11 through activation of the AKT pathway in fibroblasts. Fibroblasts promote the GC proliferation and peritoneal metastasis by secreting CLDN11 in vitro and in vivo. Furthermore, it was revealed that CXCR7-positive fibroblasts were significantly associated with M2-type macrophages infiltration in tissues. CONCLUSION: CXCR7-positive fibroblasts play an essential role in PM of GC via CLDN11. Therapy targeting CXCR7-positive fibroblasts or CLDN11 may be helpful in the treatment of GC with PM.

Triglyceride-glucose index is associated with a higher risk of stroke in a hypertensive population.

BACKGROUND: This study aimed to evaluate the association of triglyceride-glucose (TyG) index, an insulin resistance surrogate biomarker, with first stroke in a hypertensive population and to explore potential influencing factors. METHODS: This study, a cohort study among a rural Chinese hypertensive population, utilized data from the China Stroke Primary Prevention Trial (CSPPT). The TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Multivariate analysis using Cox proportional hazards models was conducted. RESULTS: A total of 7569 hypertensive patients were included in this study. When TyG index was assessed as quartiles, compared with the reference group (Quartile 1), the hazard ratio of stroke was 1.04 in Quartile 2, 1.43 in Quartile 3, and 1.45 in Quartile 4, with a significant trend test (P = 0.013). When Quartiles 3 and 4 were combined (≥ 8.8), the hazard ratio was 1.41 compared with combined Quartiles 1 and 2 (< 8.8). Similar findings were observed for the association of TyG index with ischemic stroke. Further, a joint effect of baseline TyG index and age on first stroke was found. Using the group with TyG < 8.8 and age < 60 years as a reference, the highest hazard ratio of stroke was found in the group with a higher TyG index and aged 60 or greater (HR: 2.15, 95% CI 1.50, 3.07, P < 0.001). CONCLUSIONS: In a hypertensive population, baseline TyG index was associated with a significantly higher risk of first stroke. In addition, age was a significant effect modifier for this association.

Poor sleep quality association with higher lung cancer risk: a nested case-control study.

Background: Little is known about the relationship between sleep quality and lung cancer incidence. Thus, this study was conducted to investigate the potential connection between sleep quality and lung cancer incidence. Methods: We performed and selected a nested case-control study that included 150 lung cancer cases and 150 matched controls based on the Lianyungang cohort. Univariate and multivariate logistic regression was utilized to investigate the connection between potential risk factors and lung cancer incidence risk. Results: In this study, the average age of participants was 66.5 ± 9.1 years, with 58.7% being male, and 52.7% reportedly experiencing sleep quality problems. The results of multivariate logistic regression showed that poor sleep quality was connected to an increased lung cancer incidence risk (P = 0.033, odds ratio = 1.83, 95% confidence interval = [1.05-3.19]) compared with those with good sleep quality. The stratified analyses showed a significantly positive connection between poor sleep quality (vs. good sleep quality) and cancer risk in smokers (vs. non-smoker, P for interaction = 0.085). The combined effect analysis indicated that smokers with poor sleep quality suffered from a 2.79-fold increase in cancer incidence rates when compared with non-smokers with good sleep quality. Conclusions: Poor sleep quality was positively connected to an increased lung cancer incidence risk. In addition, among those individuals with poor sleep quality, smoking increased the lung cancer incidence risk.

Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma.

Background and purpose: The presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN. Materials and methods: In this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation. Results: Using the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods. Conclusion: Our modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction.

A classification-based approach to mapping particulate organic matter (POM) in inland water using OLCI images.

Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were used for water type 1; [Formula: see text], [Formula: see text], and [Formula: see text] were adopted for water type 2; and [Formula: see text], [Formula: see text], and [Formula: see text] were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56% and 52.21% to 32.61%, and the root mean square errors (RMSE) decreased from 3.05 mg/L and 2.24 mg/L to 1.75 mg/L. A random error analysis of the atmospheric correction demonstrated that this algorithm is robust and can still perform well within a random error of 30%. Finally, this method was successfully applied to map the POM concentrations in the YRD using OLCI images acquired on November 12, 2020.

MUC1 promotes lung metastases of liver cancer by impairing anti-tumor immunity.

PURPOSE: MUC1 is a membrane bound protein that can regulate tumor progression but its role in tumor metastasis and the metastatic microenvironment remains unclear. METHODS: We performed differential gene analysis for primary liver cancer (n = 31) and lung metastases (n = 31) using the Gene Expression Omnibus (GEO) dataset (GSE141016) and obtained RNA sequencing data from 374 liver cancer and 50 normal tissues from The Cancer Genome Atlas (TCGA). We analyzed the prognostic value of MUC1 and the relationship between MUC1 and the TME using online databases and a clinical cohort. Immunohistochemistry detected MUC1 in normal liver, liver cancer, and lung metastases. Multiplex immunohistochemistry staining detected immune cells in the metastatic microenvironment. RESULTS: High MUC1 expression levels in hepatocellular carcinoma are associated with worse clinical prognosis and higher rates of lung metastasis. In addition, we observed a correlation between MUC1 and multiple immune cells in the metastatic microenvironment. In paired primary liver cancer and lung metastatic tumor tissues from the same patient, we observed higher MUC1 protein levels in lung metastases than in primary liver cancer. Furthermore, MUC1 was negatively correlated with CD8+T and Treg cells in the metastatic tumor microenvironment and positively correlated with DC. In addition, we found that MUC1 was associated with CD8+T cell activation and function using flow cytometry in another cohort of patients with liver cancer. CONCLUSION: These data confirm the potential of MUC1 as a prognostic marker and therapeutic target.

Long-term monitoring particulate composition change in the Great Lakes using MODIS data.

Particulate composition provides important information for understanding the changes in underwater light fields and primary productivity. In this study, a semianalytical algorithm, based on Rayleigh-corrected reflectance at 678 nm and 748 nm on Moderate Resolution Imaging Spectroradiometer (MODIS) images was used to estimate the ratio of chlorophyll a to total suspended solids (Chla/TSS), which characterizes the particulate composition of the Great Lakes. The long-term spatial and temporal characteristics of Chla/TSS in the Great Lakes from 2000 to 2020 were obtained. The results demonstrated that Lake Superior had the highest average Chla/TSS values (5.79±0.76 µg/mg), while Lake Erie had the lowest average Chla/TSS values (2.93±0.76 µg/mg). The Mann-Kendall test showed that the Chla/TSS of the Great Lakes all showed an increasing trend, notably in Lake Michigan, with 88.23% pixels showing significant increasing trend. Climatic and hydrological factors dominated the intra-annual variation of Chla/TSS, with contribution rates ranging from 71.47% to 92.54%. Through the annual Chla/TSS change pattern analysis, it was found that the contribution of wind speed to the annual variation in Chla/TSS was slight. Changes in temperature played a major role in the interannual variability of Chla/TSS in Lake Superior and Ontario; runoff and settlement were the major contributors in Lake Huron and Michigan, while cropland dominated the Chla/TSS interannual variability in Lake Erie. Furthermore, the significantly low values of Chla/TSS in spring had the potential to predict the occurrence of blooms in western Lake Erie, and the spatial distribution of Chla/TSS could help predict the location of blooms in the next few days.

Postoperative cytokine levels and their predictive value in critical patients after major abdominal surgery: a retrospective cohort study.

BACKGROUND: It remains uncertain as to what impact cytokine expression level has on patient outcomes. The association of serum levels of interleukin-1ß (IL-1ß), IL-2, IL-6, IL-8, IL-10, tumor necrosis factor-α (TNF-α), and procalcitonin with critically ill patient outcomes after major abdominal surgery still need to be explored. METHODS: From January 1, 2018 to June 30, 2019, a retrospective cohort study was conducted on patients admitted to the surgical intensive care unit (SICU). Levels of IL-1ß, IL-2, IL-6, IL-8, IL-10, TNF-α, and procalcitonin were assessed in 1,228 patients undergoing major abdominal surgery with blood samples drawn within 24 h after surgery. RESULTS: Of the 1,228 patients admitted to the SICU for the first time, 1,152 survived and 76 patients died, with a mortality rate of 6.2% (76/1,228). The results of univariate and multivariate analyses revealed that non-survivors had higher levels of IL-1ß (OR =2.438, P<0.001) and IL-2 (OR =1.561, P=0.006). Of 62 (5.0%) readmitted to the SICU, the data of 59 were collected, and showed 46 patients survived and 13 died, giving a mortality rate of 22.0% (13/59), which was 3.5 times higher than the mortality rate during the first SICU admission. Serum IL-6 level associated with SICU readmission (OR =1.37, P=0.029). Furthermore, non-survivors had a longer SICU stay and higher rates of mechanical ventilation and continuous renal replacement therapy (CRRT). CONCLUSIONS: High levels of IL-1ß and IL-2 were associated with mortality, and a high level of IL-6 was a risk factor for SICU readmission in critically ill patients who underwent major abdominal surgery. The mortality rate was higher during the second SICU stay.

Is there any difference on cyanobacterial blooms patterns between Lake Chaohu and Lake Taihu over the last 20 years?

Serious cyanobacterial blooms (CBs) caused by lake eutrophication have become a global ecological and environmental problem and have adversely affected the production, life, and health of human beings. Lake Chaohu and Lake Taihu are two large closed shallow eutrophication lakes in the Yangtze River Delta in China with frequent CBs. In this study, the floating algae index (FAI) algorithm was applied to detect a long-time CBs sequence using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2019. The common characteristics and differences of the CBs patterns were further explored in both lakes over the last 20 years. The results showed that the severity of CBs in Lakes Chaohu and Taihu presented a similar trend of decreasing and then increasing during the period of 2000-2004 and 2005-2007, respectively. Although the severity of CBs in the two lakes was alleviated after 2008, CBs in Lake Taihu has gradually increased since 2011 and severe CBs broke out again in 2017 and 2019. Meanwhile, the CBs in Lake Chaohu have varied significantly in different years, and severe CBs were observed in 2012, 2014-2015, and 2018-2019, while in other years, CBs remained relatively low level. The high-frequency regions of CBs were mainly concentrated in the western part in Lake Chaohu and in Zhushan Bay and Meilian Bay in Lake Taihu in the initial years of 2000. However, since 2005, the CBs in Lake Chaohu gradually expanded to the central and eastern parts, and to the northwestern and western shore in Lake Taihu. Furthermore, the relationship between the monthly mean area of CBs (CBsmean) and environmental factors based on principal component analysis (PCA) indicated that temperature was the most important driving factor affecting CBs patterns. Compared to the period from 2001 to 2007, TP played a more important role in both lakes from 2008 to 2019. Various management measures have been adopted to reduce CBs in both lakes and these methods can effectively remove cyanobacteria in a short time, but they do not change CBs patterns in the long period.

Dynamic Interference Behaviors of Arbitrary Multilayer Commingling Production in Heavy Oil Reservoirs with Water Flooding.

To obtain sustainable economical oil production and recovery of investment, some oil fields adopted the strategy of multilayer commingling production at an early stage. This leads to interlayer interference and losing part of the recoverable reserves. In this paper, dynamic interference behaviors of arbitrary multilayer commingling production in heavy oil reservoirs are analyzed. Based on the non-Darcy flow equation, the Buckly-Leverett equation, and the material balance equation, a mathematical model of arbitrary multilayer commingling production is obtained. Oil and water relative permeability, saturation, and bottom hole flow pressure microelement and the iteration method are employed to solve the mathematical model in the time domain. The new model is verified by comparing the results from the typical black oil model using the Darcy law. The sensitivity analysis of critical parameters on interference behaviors, such as permeability, oil viscosity, effective drainage boundary, and voidage replacement ratio, is carried out. The model obtained in this paper can be used for oil and liquid productivity analysis during the overall process of commingling production and extended to be applied in numerical experiments with different combinations of typical parameters as well.

Simultaneous inversion of concentrations of POC and its endmembers in lakes: A novel remote sensing strategy.

Data on the concentration of particulate organic carbon (POC) and its endmembers provide a basis for the characterisation of lake biogeochemical cycles. Here, a novel remote sensing strategy (the SCPOC algorithm) was developed to determine total POC concentrations, as well as terrestrial and endogenous POC concentrations in lakes. This strategy provides a successful example for the combination of isotope tracer and remote sensing technology. First, we obtained the terrestrial and endogenous POC concentration at the sampling point based on isotope tracing technology. Afterwards, we established a relationship between the phytoplankton absorption coefficient and the endogenous POC concentration (Cend), and applied a semi-analytical algorithm to invert the Cend value. Finally, the POC source ratio model and Cend value were combined to obtain the POC concentration (CPOC) and terrestrial POC (Cter). The results of synchronisation verification based on ocean and land colour instrument (OLCI) images show that the SCPOC algorithm has high Cend, Cter, and CPOC inversion accuracy, with MAPE values of 26.07%, 30.43%, and 42.28%, respectively. In fact, the SCPOC algorithm not only improved the accuracy of lake POC mapping, but also fills the gap of optical retrieval of POC endmember concentrations. Additionally, data from the OLCI images indicated that the studied lakes were dominated by external POC. However, because of the greater contribution of algal blooms to POC, this dominant advantage weakens in summer, although the terrestrial organic carbon carried by rainfall runoff also affects lake POC composition. Different POC sources have different ecological roles in lakes, and the superior POC end-element estimation capability of the SCPOC algorithm can not only be used as a supplement to traditional tracing methods, but also provides accurate spatial data for lake management.

[Remote Sensing Classification of Urban Black-odor Water Based on Decision Tree].

Remote sensing monitoring of black-odor water is an important method for understanding the current status of urban water quality, and comprehensively evaluating the effect of urban water environment treatment. A total of 171 samples were collected in Nanjing, Changzhou, Wuxi, and Yangzhou cities and water quality parameters and optical parameters were measured simultaneously. Based on the analysis of the water color and optical characteristics of the black-odor water and non-black-odor water (denoted as general water), a decision tree was constructed to identify the severe, mild black-odor water, and general water as green and yellow water. The results found that:①According to the water color, the water bodies can be divided into six types. Among them, type 1 to 4 water bodies are black-odor water, which are gray black, dark gray, gray, and light gray water, respectively, and type 5 and 6 water bodies are general water, which are green and yellow water, respectively; ②Type 1 water body contains high contents of non-pigmented particulate matter and colored dissolved organic matter(CDOM), however, the absorption of pigmented particulate matter is not dominant. Type 2 and 5 water bodies are dominated by pigmented particulate matter. Type 3, 4, and 6 water bodies are dominated by non-pigmented particulate matter; ③After water color classification, and according to the differences of the reflection spectrums of the six types of water bodies, the difference of black-odorous water index (DBWI), green-red-nir area water index (G-R-NIR AWI), the green band reflectance and the normalized difference black-odorous water index (NDBWI) were used to construct a decision tree to identify the severe, mild black-odor water, and general water; ④The decision tree was applied to the PlanetScope satellite image of Yangzhou City on April 9, 2019, and 10 synchronous sampling points were used for verification. The overall recognition accuracy reached 80.00%, and the K value reached 0.67. The urban water classification model, after water color classification, can be applied to other similar water bodies, and provides a technical method for the supervision of black-odor water bodies.

Mapping spatio-temporal dynamics of main water parameters and understanding their relationships with driving factors using GF-1 images in a clear reservoir.

Due to eutrophication and water quality deterioration in clear reservoirs, it is necessary to monitor and manage the main water parameters: concentration of total phosphorus (CTP), chemical oxygen demand (CCOD), chlorophyll-a (CChla), total suspended matter (CTSM), and Secchi disk depth (SDD). Five random forest (RF) models are developed to estimate these parameters in Xin'anjiang Reservoir, which is a clear drinking water resource in Zhejiang, China. Then, the spatio-temporal distributions of the parameters over 7 years (2013-2019) are mapped using GaoFen-1 (GF-1) images and the relationships with driving factors are analyzed. Our study demonstrates that the parameters' distributions exhibited a significant spatio-temporal difference in Xin'anjiang Reservoir. Spatially, relatively high CTP, CCOD, CChla, and CTSM but low SDD appear in riverine areas, showing strong evidence of impact from the incoming rivers. Temporally, CChla and CTSM reached high values in summer and winter, whereas SDD and CTP were higher in the summer and autumn, respectively. In contrast, no significant seasonal variations of CCOD could be observed. This may be why CCOD is not sensitive to hydrological or meteorological factors. However, precipitation had a significant impact on CChla, CTP, SDD, and CTSM in riverine areas, though these parameters were less sensitive to meteorological factors. Moreover, the geomorphology of the reservoir and anthropogenic interference (e.g., tourism activities) also have a significant impact on the water quality parameters. This study demonstrates that coupling long-term GF-1 images and RF models could provide strong evidence and new insights to understand long-term dynamics in water quality and therefore support the development of corresponding management strategies for freshwater reservoirs.

An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze.

There are a few studies working on the vertical distribution of TSM, however, understanding the underwater profile of TSM is of great benefit to the study of biogeochemical processes in the water column that still require further research. In this study, three data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2016 and 2018. Based on the in situ optical and biological data, a multivariate linear stepwise regression method was applied for retrieval of the surface horizontal distribution of TSM (TSM0.2) using GOCI (Geostationary Ocean Color Imager) data. Then, the estimation model of vertical structure of underwater TSM was constructed using layer-by-layer recursion. This study drew several crucial findings: (1) the approach proposed in this paper generated very high goodness of fit results, with determination coefficients (R2) of 0.83 (p < 0.001, N = 54), and with smaller prediction errors (the mean absolute percentage error is determined to be 16.34%, the root mean square error is 9.01 mg l-1, and the mean ratio is 1.00, N = 26). (2) The monthly surface TSM and the column mass of suspended matter (CMSM) are affected by both wind speed and precipitation in HZL. In addition, the hourly variation of surface TSM and CMSM are driven by local wind, most especially in spring and winter. (3) Compared with the non-uniform hypothesis, the CMSM derived by conventional vertical uniformity hypothesis was underestimated by almost 10% in HZL during 2016. This should warrant the attention of lake managers and lake environmental evolution researchers.

Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing.

Estimating the proportions of particulate organic carbon (POC) endmembers is essential to fully understand the carbon cycle, the function of aquatic ecosystems, and the migration of contaminants in eutrophic lakes. There is currently no effective remote sensing optical algorithm in the literature to solve this problem. In this study, a POC-source color index (SPOC) was constructed based on the terrestrial and endogenous POC ratios calculated from field-measured stable isotope (δ13CPOC) values. The SPOC algorithm traces the sources of POC by utilizing three spectral bands centered approximately at 560 nm, 674 nm, and 709 nm, covering the intrinsic optical information of different POC sources. At the same time, the SPOC algorithm shows good applicability to Ocean and Land Color Instrument (OLCI), Medium-Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Geostationary Ocean Color Imager (GOCI) image data. The POC sources estimated using the algorithm and monthly OLCI data showed that from March 2018 to January 2019, the POC at the surface of Lake Taihu was mainly terrigenous. In addition, due to multiple factors such as algal blooms, plant physiology, river transport, regional rainfall, and carbon cycling, the distribution of POC sources exhibited strong spatial and temporal heterogeneity. Compared with other methods, it is more convenient to use remote sensing to identify the proportion of POC in different endmembers, which offers a more comprehensive understanding of the energy flows and material circulation in lakes.

Optical classification of inland waters based on an improved Fuzzy C-Means method.

Water optical clustering based on water color information is important for many ecological and environmental application studies, both regionally and globally. The fuzzy clustering method avoids the sharp boundaries in type-memberships produced by hard clustering methods, and thus presents its advantages. However, to make good use of the fuzzy clustering methods on water color spectra data sets, the determination of the fuzzifier parameter (m) of FCM (fuzzy c-means) is the key factor. Usually, the m is set to 2 by default. Unfortunately, this method assigned some membership degrees to non-belonging water type, failing to obtain the unitarity of cluster structure in some cases, especially in inland eutrophic water. To overcome this shortcoming, we proposed an improved FCM method (namely FCM-m) for water color spectra classification by optimizing the fuzzifier parameter. We collected an inland data set containing 1280 in situ spectral data and co-measured water quality parameters with a wide range of biogeochemical variability in China. Using FCM-m, seven spectrally distinct water optical clusters on Sentinel-3 OLCI (Ocean and Land Colour Imager) bands were obtained with the optimized fuzzifier (m=1.36), and the well-performed clustering result is assessed by the validated index (Fuzzy Silhouette Index=0.513). Also, the FCM-m-based soft classification framework was successfully applied to the atmospherically corrected OLCI images, which was evaluated by previous case studies. Besides, by testing FCM-m on three coastal and oceanic data sets, we verified that the optimized m should be adjusted based on the data set itself, and in general, the value gradually approaches 1 with the increase of the band number (or dimension). Finally, the effect of the improved method was tested by Chlorophyll-a concentration estimation. The results show that the algorithm------- blending by FCM-m performs better than that by original FCM, which is mainly because the FCM-m reduces the estimation error from non-belonging clusters by a stricter membership value assignation. To sum up, we believe that FCM-m is an adaptive algorithm, whose R codes are available at https://github.com/bishun945, and needs to be tested by more public data sets.

Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) mediate cell density-dependent proinflammatory responses.

A proper inflammatory response is critical to the restoration of tissue homeostasis after injury or infection, but how such a response is modulated by the physical properties of the cellular and tissue microenvironments is not fully understood. Here, using H358, HeLa, and HEK293T cells, we report that cell density can modulate inflammatory responses through the Hippo signaling pathway. We found that NF-κΒ activation through the proinflammatory cytokines interleukin-1ß (IL-1ß) and tumor necrosis factor α (TNFα) is not affected by cell density. However, we also noted that specific NF-κΒ target genes, such as cyclooxygenase 2 (COX-2), are induced much less at low cell densities than at high cell densities. Mechanistically, we observed that the transcriptional coactivators Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are localized to the nucleus, bind to TEA domain transcription factors (TEADs), recruit histone deacetylase 7 (HDAC7) to the promoter region of COX-2, and repress its transcription at low cell density and that high cell density abrogates this YAP/TAZ-mediated transcriptional repression. Of note, IL-1ß stimulation promoted cell migration and invasion mainly through COX-2 induction, but YAP inhibited this induction and thus cell migration and invasion. These results suggest that YAP/TAZ-TEAD interactions can repress COX-2 transcription and thereby mediate cell density-dependent modulation of proinflammatory responses. Our findings highlight that the cellular microenvironment significantly influences inflammatory responses via the Hippo pathway.

Oligomerization-primed coiled-coil domain interaction with Ubc13 confers processivity to TRAF6 ubiquitin ligase activity.

Ubiquitin ligase TRAF6, together with ubiquitin-conjugating enzyme Ubc13/Uev1, catalyzes processive assembly of unanchored K63-linked polyubiquitin chains for TAK1 activation in the IL-1R/TLR pathways. However, what domain and how it functions to enable TRAF6's processivity are largely uncharacterized. Here, we find TRAF6 coiled-coil (CC) domain is crucial to enable its processivity. The CC domain mediates TRAF6 oligomerization to ensure efficient long polyubiquitin chain assembly. Mutating or deleting the CC domain impairs TRAF6 oligomerization and processive polyubiquitin chain assembly. Fusion of the CC domain to the E3 ubiquitin ligase CHIP/STUB1 renders the latter capable of NF-κB activation. Moreover, the CC domain, after oligomerization, interacts with Ubc13/Ub~Ubc13, which further contributes to TRAF6 processivity. Point mutations within the CC domain that weaken TRAF6 interaction with Ubc13/Ub~Ubc13 diminish TRAF6 processivity. Our results reveal that the CC oligomerization primes its interaction with Ubc13/Ub~Ubc13 to confer processivity to TRAF6 ubiquitin ligase activity.Ubiquitin ligase TRAF6 catalyzes assembly of free polyubiquitin chains for TAK1 activation in the IL-1R/TLR pathways, but the mechanism underlying its processivity is unclear. Here, the authors show that TRAF6 coiled-coil oligomerization domain primes its interaction with Ubc13/Ub~Ubc13 to confer processivity.