Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network.

Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements and MODIS-derived remote sensing reflectance (Rrs) (N = 3916). Using DNN, the annual and intra-annual CTP spatial distributions of the Great Lakes since 2002 were reconstructed. Then, the reconstructions were correlated to nine potential factors, e.g., Chlorophyll-a, snowmelt, and cropland, to explain seasonal and long-term CTP variations. The results showed that DNN reliably estimated CTP from MODIS Rrs, with R2, mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE) of 0.83, 1.05 µg/L, 2.95 µg/L, 9.92%, and 0.13 on the test set. The near-surface CTP in the Great Lakes decreased significantly (p < 0.05) during 2002 - 2022, primarily attributed to cropland reduction, coupled with improvements in basin natural ecosystems. The sensitivity analysis verified the model robustness when confronted with input feature changes < 35%. This result along with the marginal difference between CTP derived from two sensors (R2 = 0.76, MAE = 2.12 µg/L, RMSE = 2.51 µg/L, MAPE = 11.52%, RMSLE = 0.24) suggested the model transferability from MODIS to VIIRS. This transformation facilitated optimal usage of MODIS-related archive and enhanced the continuity of CTP estimation at moderate resolution. This study presents a practical method for spatiotemporal reconstruction of CTP using remote sensing, and contributes to better understandings of driving factors behind CTP variations in the Great Lakes.

FreeGaze: A Framework for 3D Gaze Estimation Using Appearance Cues from a Facial Video.

Gaze is a significant behavioral characteristic that can be used to reflect a person's attention. In recent years, there has been a growing interest in estimating gaze from facial videos. However, gaze estimation remains a challenging problem due to variations in appearance and head poses. To address this, a framework for 3D gaze estimation using appearance cues is developed in this study. The framework begins with an end-to-end approach to detect facial landmarks. Subsequently, we employ a normalization method and improve the normalization method using orthogonal matrices and conduct comparative experiments to prove that the improved normalization method has a higher accuracy and a lower computational time in gaze estimation. Finally, we introduce a dual-branch convolutional neural network, named FG-Net, which processes the normalized images and extracts eye and face features through two branches. The extracted multi-features are then integrated and input into a fully connected layer to estimate the 3D gaze vectors. To evaluate the performance of our approach, we conduct ten-fold cross-validation experiments on two public datasets, namely MPIIGaze and EyeDiap, achieving remarkable accuracies of 3.11° and 2.75°, respectively. The results demonstrate the high effectiveness of our proposed framework, showcasing its state-of-the-art performance in 3D gaze estimation.

Accurate Measurement of Defect Generation Rates in Silicon Carbide Irradiated with Energetic Ions.

In this work, we obtained the Si vacancy generation rates η in SiC nanowire samples irradiated with 1, 3 MeV protons, and 2.8 MeV helium ions using the electrical resistivity measurement, which further indicated an intuitive linear function correlation between η and the nuclear stopping power of the incident ions at a low dpa level with a coefficient of 2.15 × 10-3 eV-1. Prediction through this correlation is consistent with previous work. Besides, the measured value is about 1/2 of the simulation results with the popular SRIM code. Overall, our work provides a feasible way to get the generation rate of a certain irradiation-induced defect by electric measurements, and the correlation obtained is practically useful in various applications.

The SLITRK4-CNPY3 axis promotes liver metastasis of gastric cancer by enhancing the endocytosis and recycling of TrkB in tumour cells.

PURPOSE: Gastric cancer (GC) is a malignant tumour with high mortality, and liver metastasis is one of the main causes of poor prognosis. SLIT- and NTRK-like family member 4 (SLITRK4) plays an important role in the nervous system, such as synapse formation. Our study aimed to explore the functional role of SLITRK4 in GC and liver metastasis. METHODS: The mRNA level of SLITRK4 was evaluated using publicly available transcriptome GEO datasets and Renji cohort. The protein level of SLITRK4 in the tissue microarray of GC was observed using immunohistochemistry. Cell Counting Kit-8, colony formation, transwell migration assays in vitro and mouse model of liver metastasis in vivo was performed to investigate the functional roles of SLITRK4 in GC. Bioinformatics predictions and Co-IP experiments were applied to screen and identify SLITRK4-binding proteins. Western blot was performed to detect Tyrosine Kinase receptor B (TrkB)-related signaling molecules. RESULTS: By comparing primary and liver metastases from GC, SLITRK4 was found to be upregulated in tissues of GC with liver metastasis and to be closely related to poor clinical prognosis. SLITRK4 knockdown significantly abrogated the growth, invasion, and metastasis of GC in vitro and in vivo. Further study revealed that SLITRK4 could interact with Canopy FGF Signalling Regulator 3 (CNPY3), thus enhancing TrkB- related signaling by promoting the endocytosis and recycling of the TrkB receptor. CONCLUSION: In conclusion, the CNPY3-SLITRK4 axis contributes to liver metastasis of GC according to the TrkB-related signaling pathway. which may be a therapeutic target for the treatment of GC with liver metastasis.

Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms.

Remote sensing has long been an effective method for water quality monitoring because of its advantages such as high coverage and low consumption. For non-optically active parameters, traditional empirical and analytical methods cannot achieve quantitative retrieval. Machine learning has been gradually used for water quality retrieval due to its ability to capture the potential relationship between water quality parameters and satellite images. This study is based on Sentinel-2 images and compared the ability of four machine learning algorithms (eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN)) to retrieve chlorophyll-a (Chl-a), dissolved oxygen (DO), and ammonia-nitrogen (NH3-N) for inland reservoirs. The results indicated that XGBoost outperformed the other three algorithms. We used XGBoost to reconstruct the spatial-temporal patterns of Chl-a, DO, and NH3-N for the period of 2018-2020 and further analyzed the interannual, seasonal, and spatial variation characteristics. This study provides an efficient and practical way for optically and non-optically active parameters monitoring and management at the regional scale.

An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery.

The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.

A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing.

Dissolved oxygen (DO) is an effective indicator for water pollution. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales is limited. In this study, the support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements (N = 188) and water temperature of Lake Huron and three other inland waterbodies (N = 282) covering latitude between 22-45 °N. Using the developed models, spatial distributions of the annual and monthly DO variability since 1984 and the annual monthly DO variability since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on long-term DO trends were analyzed. Results showed that the developed SVR-based models had good robustness and generalization (average R2 = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimates by Landsat and MODIS data were highly consistent (average R2 = 0.88). From 1984 to 2019, the oxygen loss in Lake Huron was 6.56%. Air temperature, incident shortwave radiation flux density, and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that using SVR-based models, Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, combining spectrum and water temperature as well as extending the training set to cover more DO conditions could effectively improve model robustness and generalization.

Bioactivities and action mechanisms of active compounds from Hottuynia cordata Thunb on human lung cancer cells.

BACKGROUND: Lung cancer is the leading cause of death in Taiwan for years. Besides the currently used chemotherapy, herbal medicine may play a role in the treatment of lung cancer. Hottuynia cordata Thunb (HC), one of the frequently used herbal medicine in Taiwan, has been widely used in various diseases. Review from literatures, HC has many effects, including anti-inflammatory, anti-viral, anti-bacterial, anti-SARS, and anti-tumor activities. However, there is no literatures describe its active compounds on lung cancer. This present study aims to evaluate the possible effect and action mechanism of active compounds from HC (aristolactam BII, aristolactam AII, and noraristolodione) on lung cancer. A549 lung cancer cell line was used to evaluate the effects of HC on the cell viability and possible anti-tumor effects. METHODS: We used A549 cells in the evaluation of anticancer activity. Cell viability, cell cycle, cell apoptosis and apoptosis related protein expression were studied. RESULTS: Active compounds from HC significantly inhibited A549 cell viability and induced accumulation of cell cycle at S or G2/M phase on A549 cells in a concentration-dependent manner, and induced A549 arrest at S or G2/M phase via increasing p21, p27, p53 and reducing cyclin-E, -A, cyclin-dependent kinase 2 (CDK2), cdc-2 (CDK1) protein expression. Additionally, HC induced A549 cell late apoptosis by up-regulating caspase-3, -8, Bax and decreasing Bcl-2 protein expression. CONCLUSIONS: The anti-tumor effects of aristolactam BII, aristolactam AII, and noraristolodione on human lung carcinoma A549 cells were via cell cycle arrest and apoptosis.

Choice of function-preserving gastrectomy / 中华胃肠外科杂志

As the diagnosis rate of early gastric cancer is increasing annually, the current hot spot in the treatment of early gastric cancer is how to better preserve the anatomical structure and physiological function of the stomach and improve the quality of life of patients after surgery under the premise of tumor eradication. Therefore, function-preserving gastrectomy has been the trend of surgical treatment for early gastric cancer. It is not just a modified gastrectomy, but a better combination of improved surgical techniques and the concept of function-preserving treatment, rather than being limited to traditional surgical treatments. Currently, the routine function-preserving gastrectomy mainly includes pylorus-preserving gastrectomy, proximal gastrectomy and partial gastrectomy combined with endoscopy. In addition, with the deeper understanding of metastasis pattern of gastric cancer and the development and popularization of minimally invasive techniques, laparoscopic and endoscopic cooperative surgery is gradually gaining attention. The application of laparoscopic and endoscopic hybrid surgery combined with sentinel lymph node navigation is anticipated in the treatment of early gastric cancer.

Chiral fermion reversal in chiral crystals.

In materials chiral fermions such as Weyl fermions are characterized by nonzero chiral charges, which are singular points of Berry curvature in momentum space. Recently, new types of chiral fermions beyond Weyl fermions have been discovered in structurally chiral crystals CoSi, RhSi and PtAl. Here, we have synthesized RhSn single crystals, which have opposite structural chirality to the CoSi crystals we previously studied. Using angle-resolved photoemission spectroscopy, we show that the bulk electronic structures of RhSn are consistent with the band calculations and observe evident surface Fermi arcs and helical surface bands, confirming the existence of chiral fermions in RhSn. It is noteworthy that the helical surface bands of the RhSn and CoSi crystals have opposite handedness, meaning that the chiral fermions are reversed in the crystals of opposite structural chirality. Our discovery establishes a direct connection between chiral fermions in momentum space and chiral lattices in real space.

Effect of radiotherapy on the reproductive and endocrine function of women with gynecological malignant tumors / 中国实用妇科与产科杂志

Radiotherapy is an important method in the treatment of gynecological malignant tumors,but it also causes great damage to female reproductive and endocrine function.This article reviews the effects of radiotherapy on reproductive and endocrine function in women and protective methods.

JA, a new type of polyunsaturated fatty acid isolated from Juglans mandshurica Maxim, limits the survival and induces apoptosis of heptocarcinoma cells.

Juglans mandshurica Maxim (Juglandaceae) is a famous folk medicine for cancer treatment and some natural compounds isolated from it have been studied extensively. Previously we isolated a type of ω-9 polyunsaturated fatty acid (JA) from the bark of J. mandshurica, however little is known about its activity and the underlying mechanisms. In this study, we studied anti-tumor activity of JA on several human cancer cell lines. Results showed that JA is cytotoxic to HepG2, MDA-MB-231, SGC-7901, A549 and Huh7 cells at a concentration exerting minimal toxic effects on L02 cells. The selective toxicity of JA was better than other classical anti-cancer drugs. Further investigation indicated that JA could induce cell apoptosis, characterized by chromatin condensation, DNA fragmentation and activation of the apoptosis-associated proteins such as Caspase-3 and PARP-1. Moreover, we investigated the cellular apoptosis pathway involved in the apoptosis process in HepG2 cells. We found that proteins involved in mitochondrion (cleaved-Caspase-9, Apaf-1, HtrA2/Omi, Bax, and Mitochondrial Bax) and endocytoplasmic reticulum (XBP-1s, GRP78, cleaved-Caspase-7 and cleaved-Caspase-12) apoptotic pathways were up-regulated when cells were treated by JA. In addition, a morphological change in the mitochondrion was detected. Furthermore, we found that JA could inhibit DNA synthesis and induce G2/M cell cycle arrest. The expression of G2-to-M transition related proteins, such as CyclinB1 and phosphorylated-CDK1, were reduced. In contrast, the G2-to-M inhibitor p21 was increased in JA-treated cells. Overall, our results suggest that JA can induce mitochondrion- and endocytoplasmic reticulum-mediated apoptosis, and G2/M phase arrest in HepG2 cells, making it a promising therapeutic agent against hepatoma.

[Relationships between characteristics of ground bryophyte communities and environmental factors in urban area of Chongqing, China].

The present study focused on bryophyte species composition, species diversity and the relationship between bryophyte communities and environmental factors in urban area of Chongqing City, by using biodiversity indices and the canonical correspondence analysis (CCA), based on the data of 44 plots. The results revealed that 86 species belonging to 43 genera and 25 families were found in saxicolous bryophyte communities, while 46 species belonging to 28 genera and 22 families were found in terricolous ones. The diversity indices of both saxicolous and terricolous bryophyte communities from campuses were higher than those of parks, natural scenic resorts, Jinyunshan National Nature Reserve. TWINSPAN classified saxicolous and terricolous bryophyte communities into three and two groups, respectively. CCA results showed canopy density was the major environmental factor of saxicolous bryophyte communities influencing bryophyte distribution in parks and campuses, whereas altitude, relative humidity and human disturbance were the major environmental factors in natural scenic resorts and nature reserve. Soil pH, canopy density and human disturbance were the major environmental factors in terricolous bryophyte communities in parks and campuses, whereas altitude, relative humidity and water content of the soil were the major environmental factors in those of natural scenic resorts and nature reserve.

[Dry matter storage and water soluble sugar content in different age classes rhizomes of Phragmites communis population in dry land habitat of Songnen Plain of China].

Based on the investigation and measurement of Phragmites communis in a single dominant species community in dry land habitat of Songnen Plain, the seasonal variation of dry matter storage and water soluble sugar content in different age classes rhizomes at three growth stages were analyzed. The results showed that at all growth stages, younger age class rhizomes had lower dry matter storage and water soluble sugar content, and there was an obvious difference between younger and older age classes. The dry matter storage and water soluble sugar content in younger age class rhizomes increased rapidly with growth season, and the difference between younger and older age classes reduced gradually. In the whole growth season, all the rhizomes of six age classes kept up the activities in nutrient consumption, re-storage and even overcompensating storage, and the activities of younger age class rhizomes were much higher. The dry matter storage and water soluble sugar content in older age class rhizomes increased with year. There existed extremely significant differences (P < 0.01) in the dry matter storage within and among different age class rhizomes, and the difference was larger within age classes than among age classes. Significant differences (P < 0.05) in water soluble sugar content were also observed among different age class rhizomes. The dry matter storage and water soluble sugar content in P. communis rhizomes increased in quadratic with increasing age class.