Satellite-Based Global Sea Surface Oxygen Mapping and Interpretation with Spatiotemporal Machine Learning.

The assessment of dissolved oxygen (DO) concentration at the sea surface is essential for comprehending the global ocean oxygen cycle and associated environmental and biochemical processes as it serves as the primary site for photosynthesis and sea-air exchange. However, limited comprehensive measurements and imprecise numerical simulations have impeded the study of global sea surface DO and its relationship with environmental challenges. This paper presents a novel spatiotemporal information embedding machine-learning framework that provides explanatory insights into the underlying driving mechanisms. By integrating extensive in situ data and high-resolution satellite data, the proposed framework successfully generated high-resolution (0.25° × 0.25°) estimates of DO concentration with exceptional accuracy (R2 = 0.95, RMSE = 11.95 µmol/kg, and test number = 2805) for near-global sea surface areas from 2010 to 2018, uncertainty estimated to be ±13.02 µmol/kg. The resulting sea surface DO data set exhibits precise spatial distribution and reveals compelling correlations with prominent marine phenomena and environmental stressors. Leveraging its interpretability, our model further revealed the key influence of marine factors on surface DO and their implications for environmental issues. The presented machine-learning framework offers an improved DO data set with higher resolution, facilitating the exploration of oceanic DO variability, deoxygenation phenomena, and their potential consequences for environments.

Beneficial associations between outdoor visible greenness at the workplace and metabolic syndrome in Chinese adults.

BACKGROUND: Greenness surrounding residential places has been found to significantly reduce the risk of diseases such as hypertension, obesity, and metabolic syndrome (MetS). However, it is unclear whether visible greenness exposure at the workplace has any impact on the risk of MetS. METHODS: Visible greenness exposure was assessed using a Green View Index (GVI) based on street view images through a convolutional neural network model. We utilized logistic regression to examine the cross-sectional association between GVI and MetS as well as its components among 51,552 adults aged 18-60 in the city of Hangzhou, China, from January 2018 to December 2021. Stratified analyses were conducted by age and sex groups. Furthermore, a scenario analysis was conducted to investigate the risks of having MetS among adults in different GVI scenarios. RESULTS: The mean age of the participants was 40.1, and 38.5% were women. We found a statistically significant association between GVI and having MetS. Compared to the lowest quartile of GVI, participants in the highest quartile of GVI had a 17% (95% CI: 11-23%) lower odds of having MetS. The protective association was stronger in the males, but we did not observe such differences in different age groups. Furthermore, we found inverse associations between GVI and the odds of hypertension, low high-density lipoprotein cholesterol, obesity, and high levels of FPG. CONCLUSIONS: Higher exposure to outdoor visible greenness in the workplace environment might have a protective effect against MetS.

A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas.

The transfer of dissolved silicate (DSi) from land to coastal environments is a crucial part of global biogeochemical cycling. However, the retrieval of coastal DSi distribution is challenging due to the spatiotemporal non-stationarity and nonlinearity of modeling processes and the low resolution of in situ sampling. To explore the coastal DSi changes in a higher spatiotemporal resolution, this study developed a spatiotemporally weighted intelligent method based on a geographically and temporally neural network weighted regression (GTNNWR) model, a Data-Interpolating Empirical Orthogonal Functions (DINEOF) model, and satellite observations. For the first time, the complete surface DSi concentrations of 2182 days at the 500-meter and 1-day resolution in the coastal sea of Zhejiang Province, China, were obtained (Testing R2 = 78.5 %) by using 2901 in situ records with concurrent remote sensing reflectance. The long-term and large-scale distributions of DSi reflected the changes in coastal DSi under the influences of rivers, ocean currents, and biological effects across multiple spatiotemporal scales. Benefiting from the high-resolution modeling, this study found that the surface DSi concentration had at least 2 declines during a diatom bloom process, which can provide crucial signals for the timely monitoring and early warning of diatom blooms and guide the management of eutrophication. It was also indicated that the correlation coefficient between the monthly DSi concentration and the Yangtze River Diluted Water velocities reached -0.462**, quantitatively revealing the significant influence of the terrestrial input. In addition, the daily-scale DSi fluctuations resulting from typhoon transits were finely characterized, which greatly reduces the monitoring cost compared with the field sampling. Therefore, this study developed an effective data-driven-based method to help explore the fine-scale dynamic changes of surface DSi in coastal seas.

Contextual Fear Learning and Extinction in the Primary Visual Cortex of Mice.

Fear memory contextualization is critical for selecting adaptive behavior to survive. Contextual fear conditioning (CFC) is a classical model for elucidating related underlying neuronal circuits. The primary visual cortex (V1) is the primary cortical region for contextual visual inputs, but its role in CFC is poorly understood. Here, our experiments demonstrated that bilateral inactivation of V1 in mice impaired CFC retrieval, and both CFC learning and extinction increased the turnover rate of axonal boutons in V1. The frequency of neuronal Ca2+ activity decreased after CFC learning, while CFC extinction reversed the decrease and raised it to the naïve level. Contrary to control mice, the frequency of neuronal Ca2+ activity increased after CFC learning in microglia-depleted mice and was maintained after CFC extinction, indicating that microglial depletion alters CFC learning and the frequency response pattern of extinction-induced Ca2+ activity. These findings reveal a critical role of microglia in neocortical information processing in V1, and suggest potential approaches for cellular-based manipulation of acquired fear memory.

Ultrafast 3D histological imaging based on a minutes-time scale tissue clearing and multidirectional selective plane illumination microscopy.

Conventional histopathological examinations are time-consuming and labor-intensive, and are insufficient to depict 3D pathological features intuitively. Here we report an ultrafast 3D histological imaging scheme based on optimized selective plane illumination microscopy (mSPIM), a minutes-time scale clearing method (FOCM), and a deep learning-based image enhancement algorithm (SRACNet) to realize histological preparation and imaging of clinical tissues. Our scheme enables 1-minute clearing and fast imaging (up to 900 mm2/min) of 200 µm-thick mouse kidney slices at micron-level resolution. With hematoxylin and eosin analog, we demonstrated the detailed 3D morphological connections between glomeruli and the surrounding tubules, which is difficult to identify in conventional 2D histology. Further, by the preliminary verification on human kidney tissues, this study will provide new, to the best of our knowledge, feasible histological solutions and inspirations in future 3D digital pathology.

[Overexpression of circ_LARP4 inhibits the proliferation and migration of MCF-7 human breast cancer cells and its mechanism].

Objective To investigate the effect of overexpression of circRNA La-associated protein 4 (circ_LARP4) on malignant biological behaviors of MCF-7 breast cancer cells. Methods MCF-7 cells were transfected with circ_LARP4 plasmid pcDNA-circ_LARP4, and the expression of circ_LARP4 was detected by real-time quantitative PCR(qRT-PCR). After circ_LARP4 overexpression, CCK-8 assay was used to detect the proliferation of MCF-7 cells, and mRNAs of ki67, p21, inducible nitric oxide synthase (iNOS) and interleukin-1ß (IL-1ß) were detected by qRT-PCR. The bullet volume of tumor stem cells was observed under microscope, and the number of invaded cells was detected by TranswellTM assay. The expressions of octamer binding transcription factor 4(OCT4), SRY-related high-mobility-group box gene 2 (SOX2), vascular endothelial growth factor (VEGF), epithelial cadherin (E-cadherin) and neural cadherin (N-cadherin) were detected by Western blot. The levels of iNOS and IL-1ß in the supernatant of MCF-7 cells were detected by ELISA. Results Compared with the control group, circ_LARP4 overexpression group showed an upregulation in the expression of circ_LARP4, decreased cell proliferation, and down-regulated expression of ki67. It also reported the up-regulated expression of p21, smaller tumor stem cell bullet size, and decreased the expression of OCT4 and SOX2, together with the decreased number of invaded cells, decreased expression of VEGF and N-cadherin, increased expression of E-cadherin, and decreased levels of iNOS and IL-1ß. Conclusion Overexpression of circ_LARP4 inhibits the proliferation, invasion and stem cell-like characteristics of MCF-7 breast cancer cells, and down-regulates the levels of iNOS and IL-1ß.

A theory-guided graph networks based PM2.5 forecasting method.

The theory-guided air quality model solves the mathematical equations of chemical and physical processes in pollution transportation numerically. While the data-driven model, as another scientific research paradigm with powerful extraction of complex high-level abstractions, has shown unique advantages in the PM2.5 prediction applications. In this paper, to combine the two advantages of strong interpretability and feature extraction capability, we integrated the partial differential equation of PM2.5 dispersion with deep learning methods based on the newly proposed DPGN model. We extended its ability to perform long-term multi-step prediction and used advection and diffusion effects as additional constraints for graph neural network training. We used hourly PM2.5 monitoring data to verify the validity of the proposed model, and the experimental results showed that our model achieved higher prediction accuracy than the baseline models. Besides, our model significantly improved the correct prediction rate of pollution exceedance days. Finally, we used the GNNExplainer model to explore the subgraph structure that is most relevant to the prediction to interpret the results. We found that the hybrid model is more biased in selecting stations with Granger causality when predicting.

A Spatially Weighted Neural Network Based Water Quality Assessment Method for Large-Scale Coastal Areas.

The accurate assessment of large-scale and complex coastal waters is a grand challenge due to the spatial nonstationarity and complex nonlinearity involved in integrating remote sensing and in situ data. We developed a water quality assessment method based on a newly proposed geographically neural network weighted regression (GNNWR) model to address that challenge and obtained a highly accurate and realistic water quality distribution on the basis of the comprehensive index of Chinese Water Quality Classification Standards. Using geostationary ocean color imager (GOCI) data and observations from 1240 water quality sampling sites, we conducted experiments for a typical large-scale coastal area of the Zhejiang Coastal Sea (ZCS), People's Republic of China. The GNNWR model achieved higher prediction performance (average R2 = 84%) in comparison to the widely used models, and the obtained water quality classification (WQC) maps in May of 2015-2017 and August 2015 can depict intuitively reasonable spatiotemporal patterns of water quality in the ZCS. Furthermore, an analysis of WQC maps successfully illustrated how terrestrial discharges, anthropogenic activities, and seasonal changes influenced the coastal environment in the ZCS. Finally, we identified essential regions and provided targeted regulatory interventions for them to facilitate the management and restoration of large-scale and complex coastal environments.

Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability.

Air pollution is a complex process and is affected by meteorological conditions and other chemical components. Numerous studies have demonstrated that data-driven spatio-temporal prediction models of PM2.5 concentration are comparable with the model-driven model. However, data-driven models are usually depending on the statistical correlation between PM2.5 and other factors and have challenges in dealing with causality in complex systems. In this paper, we argue that domain knowledge should be incorporated into data-driven models to enhance prediction accuracy and make the model more physically realistic. We focus on the influence of dynamic wind-field on PM2.5 concentration distribution and fuse the pollution diffusion distance with the deep learning model based on a wind-field surface. In order to model spatial dependence between monitoring stations, which is dynamic and anisotropic because of the wind-field, we proposed a hybrid deep learning framework, dynamic directed spatio-temporal graph convolution networks (DD-STGCN). It expanded the ability to deal with space-time prediction in the continuous and dynamic wind-field. We used a directed graph time-series to describe the vertex state and topological relationship between vertices and replaced traditional Euclidean distance with wind-field diffusion distance to describe the proximity relationship between vertices. Our experiment results demonstrated that the DD-STGCN model achieved a better prediction ability than LSTM, GC-LSTM, and STGCN models. Compared to the best comparison model, MAPE, MAE, and RMSE were improved by 10.2%, 9.7%, and 9.6% in 12 h on an average, respectively. The performance of our model was further tested during a haze period. In the case that two models both considered the effect of wind, compared with the pure data-driven model, our model performed better in prediction distribution and showed the benefit of spatial interpretability provided by domain knowledge.

Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals.

Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 µg/m3, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 µg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.

Double cropping and cropland expansion boost grain production in Brazil.

Brazilian grain production increased more than fourfold from 1980 to 2016. The grain boom was achieved primarily by soybean-corn double cropping and cropland expansion-both show changing spatiotemporal patterns since the 1980s. Here, we quantified the contributions of these two strategies to corn and soybean production in Brazil using municipality-level data from 1980 to 2016. We found the contribution of double cropping to the grain boom steadily increased to 35% and the largest driving force was the increasing demand for grain export. While double cropping dominated the conventional agricultural regions, cropland expansion was still the major strategy in agricultural frontiers such as the Centre-West and Matopiba. The implementation of double cropping offset the equivalent of 76.7 million ha of Brazilian arable land for grain production from 2003 to 2016. Double cropping in Brazil has the potential to help alleviate land burdens in other pantropical countries with increasing global food demand.

Variation in Concentration and Sources of Black Carbon in a Megacity of China During the COVID-19 Pandemic.

Black carbon (BC) not only warms the atmosphere but also affects human health. The nationwide lockdown due to the Coronavirus Disease 2019 (COVID-19) pandemic led to a major reduction in human activity during the past 30 years. Here, the concentration of BC in the urban, urban-industry, suburb, and rural areas of a megacity Hangzhou were monitored using a multiwavelength Aethalometer to estimate the impact of the COVID-19 lockdown on BC emissions. The citywide BC decreased by 44% from 2.30 to 1.29 µg/m3 following the COVID-19 lockdown period. The source apportionment based on the Aethalometer model shows that vehicle emission reduction responded to BC decline in the urban area and biomass burning in rural areas around the megacity had a regional contribution of BC. We highlight that the emission controls of vehicles in urban areas and biomass burning in rural areas should be more efficient in reducing BC in the megacity Hangzhou.

Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques.

Research on the carbon cycle of coastal marine systems has been of wide concern recently. Accurate knowledge of the temporal and spatial distributions of sea-surface partial pressure (pCO2) can reflect the seasonal and spatial heterogeneity of CO2 flux and is, therefore, essential for quantifying the ocean's role in carbon cycling. However, it is difficult to use one model to estimate pCO2 and determine its controlling variables for an entire region due to the prominent spatiotemporal heterogeneity of pCO2 in coastal areas. Cubist is a commonly-used model for zoning; thus, it can be applied to the estimation and regional analysis of pCO2 in the Gulf of Mexico (GOM). A cubist model integrated with satellite images was used here to estimate pCO2 in the GOM, a river-dominated coastal area, using satellite products, including chlorophyll-a concentration (Chl-a), sea-surface temperature (SST) and salinity (SSS), and the diffuse attenuation coefficient at 490 nm (Kd-490). The model was based on a semi-mechanistic model and integrated the high-accuracy advantages of machine learning methods. The overall performance showed a root mean square error (RMSE) of 8.42 µatm with a coefficient of determination (R2) of 0.87. Based on the heterogeneity of environmental factors, the GOM area was divided into 6 sub-regions, consisting estuaries, near-shores, and open seas, reflecting a gradient distribution of pCO2. Factor importance and correlation analyses showed that salinity, chlorophyll-a, and temperature are the main controlling environmental variables of pCO2, corresponding to both biological and physical effects. Seasonal changes in the GOM region were also analyzed and explained by changes in the environmental variables. Therefore, considering both high accuracy and interpretability, the cubist-based model was an ideal method for pCO2 estimation and spatiotemporal heterogeneity analysis.

Modeling spatially anisotropic nonstationary processes in coastal environments based on a directional geographically neural network weighted regression.

Quantifying the spatial association between ecological indicators (e.g., chlorophyll-a) and environmental parameters is crucial for explaining the ecological status in coastal ecosystems. Although global and local regression models have been widely used to estimate spatial relationships in marine environmental processes, spatial anisotropy caused by strong coastal-inland environmental gradients has not been investigated. This is very likely to result in incomprehensive characterization of the coastal ecological status. To better quantify the spatially anisotropic nonstationary relationship in coastal environments, a spatial proximity neural network (SPNN) was proposed in this paper to address the nonlinear effects of spatial anisotropy. A directional geographically neural network weighted regression (DGNNWR) model was accordingly developed by combining a geographically neural network weighted regression (GNNWR) with SPNN to incorporate anisotropic impacts into spatial nonstationarity. Modeling of chlorophyll-a in Zhejiang coastal areas of China in the spring over 2015-2017 was conducted to examine its performance. The results demonstrated that DGNNWR achieved a better fitting accuracy and a more adequate prediction ability than ordinary linear regression (OLR), geographically weighted regression (GWR), GNNWR, and anisotropic-based GWR models. Notably, compared to the best comparison model, the fitting error indicators were declined for more than 30% and the fitted R2 was considerably increased from 0.83 to 0.92 using our proposed DGNNWR. The spatial mapping of parameter estimates confirmed that DGNNWR successfully handled the anisotropic nonstationarity in coastal environments and quantified the main driven parameters of Chl-a. Based on the spatially refined relationship between Chl-a and environmental parameters, we further characterized the spatial and temporal distributions of Chl-a in Zhejiang coastal areas in the spring of 2015-2017, and then investigated the impacts of riverine discharges and ocean currents on the spatiotemporal variations of Chl-a. The findings are crucial to formulate appropriate mitigation strategies for eutrophication and are meaningful for the management of coastal ecosystems.

The total number of prelaryngeal and pretracheal lymph node metastases: is it a reliable predictor of contralateral central lymph node metastasis in papillary thyroid carcinoma?

BACKGROUND: Central lymph node (CLN) metastasis in papillary thyroid carcinoma (PTC) is common and being able to predict CLN metastasis helps surgeons determine individualized therapy. However, the relationship between contralateral CLN metastasis and the total number of positive lymph nodes (LNs) in the combined prelaryngeal and pretracheal region remains unclear. This study aimed to investigate whether the total number of positive LNs in the combined prelaryngeal and pretracheal region has clinical significance as a predictor for contralateral CLN metastasis. METHODS: We prospectively enrolled 153 consecutive patients with unifocal PTC >1.0 cm without ultrasonographic evidence of nodal metastasis who underwent total thyroidectomy and prophylactic bilateral CLN dissection from July 2011-May 2013. Patients were divided into three groups according to the total number of positive LNs in the combined prelaryngeal and pretracheal region. RESULTS: Rates of metastasis to ipsilateral and contralateral central compartments in PTC >1.0 cm were 84.3% and 24.2%, respectively. Multivariate analysis showed that ≥3 positive LNs in the combined prelaryngeal and pretracheal region were an independent predictive factor of contralateral CLN metastasis (P < 0.001; odds ratio, 8.585). After a mean follow-up of 24.1 mo, none of these patients had a recurrence in the central or lateral compartment. CONCLUSIONS: Occult metastasis is highly prevalent in the ipsilateral central neck of patients with PTC >1.0 cm, and the total number of prelaryngeal and pretracheal LNs metastases may be a useful indicator to predict contralateral CLN metastasis in patients with unifocal PTC.

Granular cell tumor of the thyroid: Clinical and pathological characteristics of a rare case in a 14-year-old girl.

Granular cell tumors (GCTs) are soft tissue neoplasms that originate in the nervous system, which may arise anywhere in the body. However, GCTs are extremely uncommon in thyroid tumors, with a favorable prognosis. The diagnosis of GCTs is dependent on pathological and immunohistochemical analysis and at present, surgical resection is considered the only suitable treatment. Regular follow-up after surgery is an important way to monitor treatment outcome and recurrence. The present study describes a new pathological type of thyroid GCTs diagnosed by pathology and immunohistochemistry. A 14-year-old female was referred to the West China Hospital of Sichuan University (Chengdu, China), for thyroid incidentaloma. Laboratory examinations were within the normal range. Thyroid sonography demonstrated a solid hypoechoic mass in the right lobe of the thyroid. Fine needle aspiration cytology showed a suspicious malignant tumor and subsequently a total thyroidectomy was performed. Analysis of frozen sections, from obtained samples, did not facilitate a definite diagnosis. Finally, a thyroid benign granular tumor with atypical changes was diagnosed by postoperative pathology and immunohistochemistry. A 14-month post-operative follow-up showed that the patient experienced a stable recovery and had no signs of recurrence or metastasis. The case emphasizes that the diagnosis of thyroid granular cell tumors is predominantly based on postoperative morphology and immunophenotype. The clinical routine for the differential diagnosis may be due to: (i) neoplasms displaying a granular appearance mimicking granular cell tumors, or (ii) differential diagnosis in the pathological category of granular cell tumors. Further accumulation of such rare cases may be of clinical significance in aiding the diagnosis and treatment of GCTs.

Autotransplantation of Inferior Parathyroid glands during central neck dissection for papillary thyroid carcinoma: a retrospective cohort study.

INTRODUCTION: The management of inferior parathyroid glands during central neck dissection (CND) for papillary thyroid carcinoma (PTC) remains controversial. Most surgeons preserve inferior parathyroid glands in situ. Autotransplantation is not routinely performed unless devascularization or inadvertent parathyroidectomy occurs. This retrospective study aimed to compare the incidence of postoperative hypoparathyroidism and central neck lymph node (CNLN) recurrence in patients with PTC who underwent inferior parathyroid glands autotransplantation vs preservation in situ. METHODS: This is a retrospective study which was conducted in a tertiary referral hospital. A total of 477 patients with PTC (pN1) who underwent total thyroidectomy (TT) and bilateral CND with/without lateral neck dissection were included. Patients' demographical characteristics, tumor stage, incidence of hypoparathyroidism, CNLN recurrence and the number of resected CNLN were analyzed. RESULTS: Three hundred and twenty-one patients underwent inferior parathyroid glands autotransplantation (autotransplantation group). Inferior parathyroid glands were preserved in situ among 156 patients (preservation group). Permanent hypoparathyroidism rate was 0.9% (3/321) versus 3.8% (6/156) respectively (p = 0.028). Mean numbers of resected CNLN were 15 ± 3 (6-23) (autotransplantation group) versus 11 ± 3 (7-21) (preservation group) (p < 0.001). CNLN recurrence rate was 0.3% (1/321) versus 3.8% (6/156) respectively (p = 0.003). CONCLUSION: Inferior parathyroid glands autotransplantation during CND of PTC (pN1) might reduce permanent hypoparathyroidism and CNLN recurrence. Further study enrolling more patients with long-term follow-up is needed to support this conclusion.