Estimating four-decadal variations of seagrass distribution using satellite data and deep learning methods in a marine lagoon.

Seagrasses are marine flowering plants that inhabit shallow coastal and estuarine waters and serve vital ecological functions in marine ecosystems. However, seagrass ecosystems face the looming threat of degradation, necessitating effective monitoring. Remote-sensing technology offers significant advantages in terms of spatial coverage and temporal accessibility. Although some remote sensing approaches, such as water column correction, spectral index-based, and machine learning-based methods, have been proposed for seagrass detection, their performances are not always satisfactory. Deep learning models, known for their powerful learning and vast data processing capabilities, have been widely employed in automatic target detection. In this study, a typical seagrass habitat (Swan Lake) in northern China was used to propose a deep learning-based model for seagrass detection from Landsat satellite data. The performances of UNet and SegNet at different patch scales for seagrass detection were compared. The results showed that the SegNet model at a patch scale of 16 × 16 pixels worked well, with validation accuracy and loss of 96.3 % and 0.15, respectively, during training. Evaluations based on the test dataset also indicated good performance of this model, with an overall accuracy >95 %. Subsequently, the deep learning model was applied for seagrass detection in Swan Lake between 1984 and 2022. We observed a noticeable seasonal variation in germination, growth, maturation, and shrinkage from spring to winter. The seagrass area decreased over the past four decades, punctuated by intermittent fluctuations likely attributed to anthropogenic activities, such as aquaculture and construction development. Additionally, changes in landscape ecology indicators have demonstrated that seagrass experiences severe patchiness. However, these problems have weakened recently. Overall, by combining remote sensing big data with deep learning technology, our study provides a valuable approach for the highly precise monitoring of seagrass. These findings on seagrass area variation in Swan Lake offer significant information for seagrass restoration and management.

Chronic Sacral Nerve Stimulation Inhibits Visceral Hypersensitivity in Diarrhea-Predominant Irritable Bowel Syndrome Rats Model.

OBJECTIVE: Sacral nerve stimulation (SNS) is emerging as a novel treatment for irritable bowel syndrome (IBS). However, its effects are limited, and the underlying mechanisms remain largely unknown. MATERIALS AND METHODS: In this study, rats were divided into three groups (n = 12 rats per group): 1) the SNS group; 2) the sham SNS group (the sham group for short); and 3) the control group. The SNS and sham groups were exposed to chronic and acute stress to establish an IBS model. Electrode implantation surgery was performed in rats with the IBS model. The SNS group received electrical stimulation for 30 minutes every day for seven days. Abdominal withdrawal reflex (AWR) was used to evaluate the effect of SNS on visceral sensitivity in diarrhea-predominant IBS (IBS-D) rats. The frequency domain of heart rate variability (HRV) was analyzed to assess the effect of SNS on regulating the autonomic function. The expression of transient receptor potential vanilloid 1 (TRPV1) in the colon, spinal cord, and hippocampus was detected by immunohistochemistry to explore the mechanism of SNS in IBS-D rats. RESULTS: Compared with the sham group, AWR scores were significantly decreased under different gas volumes of stimulation of 0.4, 0.6, and 0.8 ml for rectal distention in the SNS group (all p < 0.05). However, there was no significant difference <1.0 ml between the two groups (p > 0.05). Compared with the sham group, the frequency domain indexes of HRV were significantly altered. Normalized low-frequency power and low frequency-to-high frequency ratio were significantly decreased, and normalized high-frequency power was significantly increased in the SNS group (all p < 0.05). Moreover, the expression of TRPV1 in the spinal cord and colon in the SNS group was significantly decreased compared with the sham group (both p < 0.05). These results suggested that chronic SNS not only improved the visceral sensitivity and autonomic dysfunction but also decreased the expression of TRPV1 in the spinal cord-gut tissue in IBS-D rats. CONCLUSION: Chronic SNS was found to have an inhibitory effect on visceral hypersensitivity in IBS-D rats, providing experimental evidence for its potential clinical application in IBS.

Remote sensing detection of seagrass distribution in a marine lagoon (Swan Lake), China.

Seagrass, a submerged flowering plant, is widely distributed in coastal shallow waters and plays a significant role in maintaining marine biodiversity and carbon cycles. However, the seagrass ecosystem is currently facing degradation, necessitating effective monitoring. Satellite remote sensing observations offer distinct advantages in spatial coverage and temporal frequency. In this study, we focused on a marine lagoon (Swan Lake), located in the Shandong Peninsula of China which is characterized by a large and typical seagrass population. We conducted an analysis of remote sensing reflectance of seagrass and other objectives using a comprehensive Landsat satellite dataset spanning from 2002 to 2022. Subsequently, we constructed Seagrass Index I (SSI-I) and Seagrass Index II (SSI-II), and used them to develop a stepwise model for seagrass detection from Landsat images. Validation was performed using in situ acoustic survey data and visual interpretation, revealing the good performance of our model with an overall accuracy exceeding 0.90 and a kappa coefficient around 0.80. The long-term analysis (2002-2022) of the seagrass distribution area in Swan Lake, generated from Landsat data using our model, indicated that the central area of Swan Lake sustains seagrass for the longest duration. Seagrass in Swan Lake exhibits a regular seasonal variation, including seeding in early spring, growth in spring-summer, maturation in the middle of summer, and shrinkage in autumn. Furthermore, we observed an overall decreasing trend in the seagrass area over the past 20 years, while occasional periods of seagrass restoration were also observed. These findings provide crucial information for seagrass protection, marine blue carbon studies, and related endeavors in Swan Lake. Moreover, our study offers a valuable alternative approach that can be implemented for seagrass monitoring using satellite observations in other coastal regions.

Multiple infections of zoonotic pathogens in wild Brandt's voles (Lasiopodomys brandtii).

BACKGROUND: The frequent interactions of rodents with humans make them a common source of zoonotic infections. Brandt's vole is the dominant rodent species of the typical steppe in Inner Mongolia, and it is also an important pest in grassland. OBJECTIVES: To obtain an initial unbiased measure of the microbial diversity and abundance in the blood and intestinal tracts and to detect the pathogens carried by wild Brandt's voles in Hulun Buir, Inner Mongolia. METHODS: Twenty wild adult Brandt's voles were trapped using live cages, and 12 intestinal samples were collected for metagenomic analysis and 8 blood samples were collected for meta-transcriptomic analysis. We compared the sequencing data with pathogenic microbiota databases to analyse the phylogenetic characteristics of zoonotic pathogens carried by wild voles. RESULTS: A total of 122 phyla, 79 classes, 168 orders, 382 families and 1693 genera of bacteria and a total of 32 families of DNA and RNA viruses in Brandt's voles were characterized. We found that each sample carried more than 10 pathogens, whereas some pathogens that were low in abundance were still at risk of transmission to humans. CONCLUSION: This study improves our understanding of the viral and bacterial diversity in wild Brandt's voles and highlights the multiple viral and bacterial pathogens carried by this rodent. These findings may serve as a basis for developing strategies targeting rodent population control in Hulun Buir and provide a better approach to the surveillance of pathogenic microorganisms in wildlife.

Monkeypox outbreaks in the context of the COVID-19 pandemic: Network and clustering analyses of global risks and modified SEIR prediction of epidemic trends.

Background: Ninety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends. Method: Twenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis. Results: Between historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61-7.84 in the early stage and 0.70-4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022. Conclusions: In the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Size-Controllable Eu-MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small-Molecular Identification.

Metal-organic frameworks (MOFs) with the aggregation-induced emission (AIE) activities exhibit potential applications in the fields of energy and biomedical technology. However, the controllable synthesis of MOFs in the varied particle sizes not only affects their AIE activities, but also restricts their application scenarios. In this work, the varied particle sizes of Eu-MOFs are synthesized by adjusting the synthesis process parameters, and their variation rules combining the single factor analysis method with machine learning technology are studied. Based on the R2 score, the gradient boosting decision tree (GBDT) regression model (0.9535) is employed to calculate the weight and correlation between different synthesis process parameters and it is shown that all these parameters have synergic effects on the particle sizes of Eu-MOFs, and the Eu-precursors concentration dominates in their synthesis process. Furthermore, it is indicated that the large size of Eu-MOFs and strong structural stability contribute to their high AIE activities. Finally, a screen-printed pattern is fabricated using the sample of "120-0.3-6," and this pattern exhibits a bright red fluorescence under the UV light. More importantly, this kind of Eu-MOFs can also be used to identify varied ions (Fe3+ , F- , I- , SO42- , CO32- , and PO43- ) and citric acid.

Analysis of laboratory and serological test results in patients with acute brucellosis during follow-up.

BACKGROUND: The laboratory test results and serum-specific antibodies of patients with acute brucellosis initial infection were followed up and analyzed. METHODS: 70 patients in Hohhot City, Inner Mongolia Autonomous Region, with acute brucellosis were followed up for 360 days. Serum samples were collected at 0, 15, 30, 60, 90, 180, and 360 days after diagnosis and analyzed by Rose Bengal plate test (RBPT), colloidal gold test paper (GICA), and test tube agglutination test (SAT). The serum-specific antibodies IgG and IgM were detected. RESULTS: RBPT results: False negative (-) gradually increased with the extension of the course of disease, with the largest change in 30-60 days after diagnosis, and the constituent ratio increased by 12.9%. GICA results: The false negative increased with the course of disease, and the constituent ratio of false negative was 20.0% after 180 days of diagnosis. SAT results: 1:100 positive showed a ladder like decrease with the increase in the course of disease, and the largest decrease was 90-180 days, with a decrease of 34.3% in the constituent ratio. 360 days after diagnosis, the constituent ratio of positive was only 14.3%. During the follow-up period, the IgG average value fluctuated and the average IgM value decreased. CONCLUSION: The false-negative results of RBPT, GICA, and SAT increased with the course of disease, and the false-negative rates were higher than 20% after half a year. IgM level is beneficial to the early diagnosis of brucellosis, while IgG level is helpful to the judgment of brucellosis stage.

Synergistic CO2 reduction effects in Chinese urban agglomerations: Perspectives from social network analysis.

China has released its ambitious target for carbon neutrality by 2060. With decades of top-down energy conservation and pollutant mitigation policies, the techno-mitigation space has gradually shrunk, while more mitigation space is required for a systematic approach. To help to uncover CO2 mitigation effects, location and better pathways from a systematic perspective, this paper combines disparity analysis and social network analysis to investigate the synergistic emissions reduction effect of urban agglomerations in three representative Chinese urban agglomerations, namely the Yangtze River Delta urban agglomeration (YRD), Chengdu-Chongqing urban agglomeration (CY) and Guangdong-Hong Kong-Macao urban agglomeration (GHM). Based on understanding of the carbon emission disparity characteristics of the three urban agglomerations using disparity analysis, this study uses social network analysis to study the synergistic CO2 reductions in each urban agglomeration from three perspectives: overall, individual, and connection. The findings emphasize that CY presented the greatest synergistic development capacity but with weak driving ability, indicating that overall synergistic emission reduction was difficult to achieve in a short period. GHM presented obvious fragmentation between the core and peripheral cities, resulting in a weak synergistic mitigation effect. YRD highlighted a solid synergistic development capacity with strong driving ability by its developed cities, thus generating the greatest potential to reduce CO2 emissions in the short and middle terms. Different cities assume different roles in synergistic CO2 reduction. Our results can be expected to enlighten more regionally oriented CO2 mitigation policy implications from an urban agglomeration perspective.

Dynamics of euphotic zone depth in the Bohai Sea and Yellow Sea.

Euphotic zone depth (Zeu) plays an important role in studies of marine biogeochemical processes and ecosystems. Remote sensing techniques are ideal tools to investigate Zeu distributions because of their advanced observation ability with broad spatial coverage and frequent observation intervals. This study aims to develop a new approach that derives Zeu directly from remote sensing reflectance (Rrs(λ)) values rather than by using other intermediate variables and then reveals the dynamic characteristics of Zeu in the Bohai Sea (BS) and Yellow Sea (YS). To do this, in situ data collected from various seasons were first used to assess the ability of several spectral indicators of Rrs(λ) for deriving Zeu and the optimal spectral indicator was determined to build a Zeu retrieval model. This model was further applied to Geostationary Ocean Color Imager (GOCI) data to study the spatial and temporal variations in Zeu. The results showed that the new Zeu retrieval model performed well with R2, RMSE and MAPE values of 0.843, 4.42 m and 17.9%, respectively. High Zeu levels were generally observed during summer for both coastal and offshore waters while the lowest Zeu values were observed during winter. Changing concentrations of total suspended matter, which are often modulated by sediment resuspension and transportation, are probably the main factor responsible for the spatial and temporal variability of Zeu. These findings provide crucial information for modeling primary production, carbon flux, and heat transfer, etc., in the BS and YS, as well as contribute a useful alternative approach that will be easily implemented to study Zeu from satellite data for other water environments.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision.