Landcover-based detection of rapid impacts of extreme storm on coastal landscape.

On September 24, 2022, Post-Tropical Hurricane Fiona made landfall in Atlantic Canada and caused unprecedented damages to the coastal communities and ecosystems therein. The aftermath triggered local government and communities in Prince Edward Island (PEI), Canada to rethink current policies and practices for coastal protection in the context of climate change. This historic hazard represents the escalating frequency and intensity of extreme weather events that globally threaten coastal regions, accelerating coastal erosion and endangering communities. This study employs landcover-based detection to assess rapid storm impact of Fiona on coastline of PEI using Sentinel-2 satellite images, to gauge the efficacy of landcover-based detection and quantify storm-induced coastal environmental changes. Our results indicate that, following Fiona, over 51 km2 coastal land loss due to the erosion at beach foreshore and inundation at tidal flat, and over 11 km2 sand dune loss mainly on the PEI north shore. This constitutes a 3.5 % loss of coastal land resources within the 1798 km2 PEI coastal zone. Fiona also caused over 194 km2 area in coastal buffer zone showed temporal fluid-mud from the eroded sediments of sand dunes, cliffs, and tidal flats, suggesting the significant sediment loss from vertical structures in addition to the direct retreat. The landcover-based method can be regarded as a valuable tool for the storm impacts on coastal environments. Based on the coastal change pattern, more sustainable coastal protection and adaptation measures should be developed, focusing on reducing hydrodynamic intensity and improving erosion capacity, with consideration of the increasing likelihood of more intense and frequent storm events in a warming climate.

Climate Change Impacts on Potato Storage.

In this study, we present a comprehensive literature review of the potential impacts of climate change on potato storage. Potato preservation can help reduce food loss and waste while increasing long-term food security, as potatoes are one of the most important crops worldwide. The review's results suggest climate change can negatively affect potato storage, especially tuber sprouting and diseases in storage chambers. Lower Sielianinov coefficient values (indicating dry and hot conditions) during the vegetative season of potato growing can lead to earlier sprouting. For instance, a decrease of 0.05 in the Sielianinov coefficient during the growing season results in tubers stored at 3 °C sprouting 25 days earlier and tubers stored at 5 °C experiencing a 15-day reduction in dormancy. This is due to the fact that the dry and hot climate conditions during the vegetation period of potato planting tend to shorten potato tubers' natural dormancy, which further leads to earlier sprouting during storage. Furthermore, high Sielianinov coefficient values may lead to worse disease situations. The results also suggest that research about the impacts of climate change on potato storage is very limited at the current stage, and further studies are needed to address the key knowledge gaps identified in this study.

Climate change impacts on oyster aquaculture - Part I: Identification of key factors.

Oysters are enriched with high-quality protein and are widely known for their exquisite taste. The production of oysters plays an important role in the local economies of coastal communities in many countries, including Atlantic Canada, because of their high economic value. However, because of the changing climatic conditions in recent years, oyster aquaculture faces potentially negative impacts, such as increasing water acidification, rising water temperatures, high salinity, invasive species, algal blooms, and other environmental factors. Although a few isolated effects of climate change on oyster aquaculture have been reported in recent years, it is not well understood how climate change will affect oyster aquaculture from a systematic perspective. In the first part of this study, we present a systematic review of the impacts of climate change and some key environmental factors affecting oyster production on a global scale. The study also identifies knowledge gaps and challenges. In addition, we present key research directions that will facilitate future investigations.

Coastal erosion and climate change: A review on coastal-change process and modeling.

Coastal erosion is a normal process of nature. However, the rate of coastal erosion, and the frequency and intensity of coastal flooding events, are now on the rise around the world due to the changing climate. Current responses to coastal erosion are primarily determined by site-specific factors, such as coastal elevation, coastal slope, coastal features, and historical coastline change rate, without a systematic understanding of the coastal-change processes in the context of climate change, including spatiotemporal changes in sea level, regional changes in wave climate, and sea ice coverage. In the absence of a clear understanding of the coastal-change processes, most of the current coastal responses have been built upon a risky assumption (i.e., the present-day coastal change will persist) and are not resilient to future climate change. Here, we conduct a literature review to summarize the latest scientific understanding of the coastal-change processes under climate change and the potential research gaps towards the prediction of future coastal erosion. Our review suggests that a coupled coastal simulation system with a nearshore wave model (e.g., SWAN, MIKE21, etc.) can play a critical role in both the short-term and long-term coastal risk assessment and protective measure development.

Lactobacillus Plantarum NC8 and its metabolite acetate alleviate type 1 diabetes via inhibiting NLRP3.

A healthy organism is the result of host-microbiome co-evolution. Microbial metabolites can also stimulate immune cells to reduce intestinal inflammation and permeability. Gut dysbiosis will lead to a variety of autoimmune diseases, such as Type 1 diabetes (T1D). Most of probiotics, such as Lactobacillus casei, Lactobacillus reuteri, Bifidobacterium bifidium, and Streptococcus thermophiles, can improve the intestinal flora structure of the host, reduce intestinal permeability, and relieve symptoms of T1D patients if ingested above probiotics in sufficient amounts. Lactobacillus Plantarum NC8, a kind of Lactobacillus, whether it has an effect on T1D, and the mechanism of it regulating T1D is still unclear. As a member of the inflammatory family, NLRP3 inflammasome can enhance inflammatory responses by promoting the production and secretion of proinflammatory cytokines. Many previous studies had shown that NLRP3 also plays an important role in the development of T1D. When the NLRP3 gene is deleted, the disease progression of T1D will be delayed. Therefore, this study investigated whether Lactobacillus Plantarum NC8 can alleviate T1D by regulating NLRP3. The results demonstrated that Lactobacillus Plantarum NC8 and its metabolites acetate play a role in T1D by co-modulating NLRP3. Lactobacillus Plantarum NC8 and acetate can reduce the damage of T1D in the model mice, even if orally administered them in the early stage of T1D. The number of Th1/Th17 cells in the spleen and pancreatic lymph nodes (PLNs) of T1D mice were significantly reduced by oral Lactobacillus Plantarum NC8 or acetate. The expression of NLRP3 in the pancreas of T1D mice or murine macrophages of inflammatory model were significantly inhibited by treatment with Lactobacillus Plantarum NC8 or acetate. In addition, the number of macrophages in the pancreas were significantly reduced by the treatment with Lactobacillus Plantarum NC8 or acetate. In summary, this study indicated that the regulatory mechanism of Lactobacillus Plantarum NC8 and its metabolite acetate to T1D maybe via inhibiting NLRP3 and provides a novel insights into the mechanism of the alleviated role of probiotics to T1D.

Evaluation and projection of precipitation extremes under 1.5°C and 2.0°C GWLs over China using bias-corrected CMIP6 models.

China is facing an increasing challenge from severe precipitation-related extremes with accelerating global warming. In this study, using a bias-corrected CMIP6 ensemble, future responses of precipitation extreme indices at 1.5°C and 2.0°C global warming levels (GWLs) under the SSP245, SSP370 and SSP585 scenarios are investigated. Despite different change magnitudes, extreme precipitation events will be more frequent and more intense over China as a whole under higher emissions and GWLs. The increase in annual total precipitation could attribute to a sharp increase in the intensity and days of very heavy precipitation in future global warming scenarios. Limiting global warming to 1.5°C and low emission pathways (i.e., SSP245) instead of 2°C and high emission pathways (i.e., SSP585) would have substantial benefits for China in terms of reducing occurrences of extreme precipitation events.

Assessing Future Climate Change Impacts on Potato Yields - A Case Study for Prince Edward Island, Canada.

Crop yields are adversely affected by climate change; therefore, it is crucial to develop climate adaptation strategies to mitigate the impacts of increasing climate variability on the agriculture system to ensure food security. As one of the largest potato-producing provinces in Canada, Prince Edward Island (PEI) has recently experienced significant instability in potato production. PEI's local farmers and stakeholders are extremely concerned about the prospects for the future of potato farming industries in the context of climate change. This study aims to use the Decision Support System for Agrotechnology Transfer (DSSAT) potato model to simulate future potato yields under the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios (including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The study evaluates the combined effects of changing climatic conditions at local scales (i.e., warming temperature and changing precipitation patterns) and increasing carbon dioxide (CO2) concentration in the atmosphere. The results indicate future significant declines in potato yield in PEI under the current farming practices. In particular, under the high-emission scenarios (e.g., SSP3-7.0 and SSP5-8.5), the potato yield in PEI would decline by 48% and 60% in the 2070s and by 63% and 80% by 2090s; even under the low-emission scenarios (i.e., SSP1-1.9 and SSP1-2.6), the potato yield in PEI would still decline by 6-10%. This implies that it is important to develop effective climate adaptation measures (e.g., adjusting farming practices and introducing supplemental irrigation plans) to ensure the long-term sustainability of potato production in PEI.

Comprehensive Network Analysis Reveals Alternative Splicing-Related lncRNAs in Hepatocellular Carcinoma.

It is increasingly appreciated that long non-coding RNAs (lncRNAs) associated with alternative splicing (AS) could be involved in aggressive hepatocellular carcinoma. Although many recent studies show the alteration of RNA alternative splicing by deregulated lncRNAs in cancer, the extent to which and how lncRNAs impact alternative splicing at the genome scale remains largely elusive. We analyzed RNA-seq data obtained from 369 hepatocellular carcinomas (HCCs) and 160 normal liver tissues, quantified 198,619 isoform transcripts, and identified a total of 1,375 significant AS events in liver cancer. In order to predict novel AS-associated lncRNAs, we performed an integration of co-expression, protein-protein interaction (PPI) and epigenetic interaction networks that links lncRNA modulators (such as splicing factors, transcript factors, and miRNAs) along with their targeted AS genes in HCC. We developed a random walk-based multi-graphic (RWMG) model algorithm that prioritizes functional lncRNAs with their associated AS targets to computationally model the heterogeneous networks in HCC. RWMG shows a good performance evaluated by the ROC curve based on cross-validation and bootstrapping strategies. As a conclusion, our robust network-based framework has derived 31 AS-related lncRNAs that not only validates known cancer-associated cases MALAT1 and HOXA11-AS, but also reveals new players such as DNM1P35 and DLX6-AS1with potential functional implications. Survival analysis further provides insights into the clinical significance of identified lncRNAs.

Evaluating the added values of regional climate modeling over China at different resolutions.

Previous studies have suggested that dynamical downscaling to global climate models can produce improved climate simulations at regional and local scales. However, the expensive computational requirements of dynamical downscaling inevitably add a limit to the spatial resolution of the resulting regional climate simulations. In order to find a balance between computational requirements and simulation improvements, it is extremely important to investigate how the spatial resolution of regional climate simulation affects the added values of dynamical downscaling; yet, it is still not well understood. Therefore, in this study, we conduct long-term climate simulations for the entire country of China with the PRECIS regional climate model at two different spatial resolutions (i.e., 25 and 50 km). The purpose is to evaluate whether a fine-resolution model simulation, given its considerable requirements for computational resources, would add more valuable information for understanding regional climatology than a coarse-resolution model simulation. Our results show that the PRECIS can reasonably reproduce the spatial distribution of seasonal and monthly mean temperature and precipitation over the most of regions in China. However, in the process of downscaling, RCM with higher resolution cannot always produce more accurate output. In regard to precipitation simulations, compared with the host GCM, it is difficult to determine exactly a homogeneous improvement of performance in downscaling, both in terms of spatial patterns as well as magnitude of errors. For interannual variability, variations in temperature are closer to observation than precipitation and the high-resolution R25 has better skills over the northwest than R50. Moreover, except for the west, it is shown that PRECIS is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet days and simple precipitation intensity index in spatial distribution.

CO2 emissions patterns of 26 cities in the Yangtze River Delta in 2015: Evidence and implications.

As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.

Insight into sorption mechanism of phenanthrene onto gemini modified palygorskite through a multi-level fuzzy-factorial inference approach.

A multi-level fuzzy-factorial inference approach was proposed to examine the sorption behavior of phenanthrene on palygorskite modified with a gemini surfactant. Fuzzy set theory was used to determine five experimentally controlled environmental factors with triangular membership functions, including initial concentration, added humid acid dose, ionic strength, temperature, and pH. The statistical significance of factors and their interactions affecting the sorption process was revealed through a multi-level factorial experiment. Initial concentration, ionic strength, and pH were identified as the most significant factors based on the multi-way ANOVA results. Examination of curvature effects of factors revealed the nonlinear complexity inherent in the sorption process. The potential interactions among experimental factors were detected, which is meaningful for providing a deep insight into the sorption mechanisms under the influences of factors at different levels.

Dynamically-downscaled probabilistic projections of precipitation changes: A Canadian case study.

In this study, plausible changes in annual and seasonal precipitation over Ontario, Canada in response to global warming are investigated through a regional climate modeling approach. A high-resolution regional climate model ensemble based upon the Providing REgional Climates for Impacts Studies (PRECIS) model is developed to help explore the possible outcomes of future climate. A Bayesian hierarchical model is then employed to quantify the uncertainties involved in the modeling results and obtain probabilistic projections of precipitation changes at grid point scales. The results show that the projected changes in annual precipitation exhibit a certain degree of spatial variability with the median changes mostly bounded by 0% and 20%, implying that the annual precipitation over Ontario is more likely to increase in the context of global warming. Specifically, the mean changes in annual precipitation for 2030s and 2050s would be ~7.5%, while the annual precipitation for 2080s is likely to increase by an average of ~12.5%. By contrast, the spatial variability of seasonal precipitation changes is more significant, especially for the changes in spring precipitation which may vary from -40% in south and 50% in north. It is reported that there would be a continuous increasing trend in winter, spring, and autumn precipitation from 2030s to 2080s by 5-30%, but summer precipitation is likely to decrease by 5% or even higher to the end of this century. Furthermore, our results suggest that the larger the biases in historical simulations, the more uncertain the future projections will be.

Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada.

Over the recent years, climate change impacts have been increasingly studied at the watershed scale. However, the impact assessment is strongly dependent upon the performance of the climatic and hydrological models. This study developed a two-step method to assess climate change impacts on water resources based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and a Hydrological Inference Model (HIM). PRECIS runs provided future temperature and precipitation projections for the watershed under the Intergovernmental Panel on Climate Change SRES A2 and B2 emission scenarios. The HIM based on stepwise cluster analysis is developed to imitate the complex nonlinear relationships between climate input variables and targeted hydrological variables. Its robust mathematical structure and flexibility in predictor selection makes it a desirable tool for fully utilizing various climate modeling outputs. Although PRECIS and HIM cannot fully cover the uncertainties in hydro-climate modeling, they could provide efficient decision support for investigating the impacts of climate change on water resources. The proposed method is applied to the Grand River Watershed in Ontario, Canada. The model performance is demonstrated with comparison to observation data from the watershed during the period 1972-2006. Future river discharge intervals that accommodate uncertainties in hydro-climatic modeling are presented and future river discharge variations are analyzed. The results indicate that even though the total annual precipitation would not change significantly in the future, the inter-annual distribution is very likely to be altered. The water availability is expected to increase in Winter while it is very likely to decrease in Summer over the Grand River Watershed, and adaptation strategies would be necessary.

An open-source software package for multivariate modeling and clustering: applications to air quality management.

This paper presents an open-source software package, rSCA, which is developed based upon a stepwise cluster analysis method and serves as a statistical tool for modeling the relationships between multiple dependent and independent variables. The rSCA package is efficient in dealing with both continuous and discrete variables, as well as nonlinear relationships between the variables. It divides the sample sets of dependent variables into different subsets (or subclusters) through a series of cutting and merging operations based upon the theory of multivariate analysis of variance (MANOVA). The modeling results are given by a cluster tree, which includes both intermediate and leaf subclusters as well as the flow paths from the root of the tree to each leaf subcluster specified by a series of cutting and merging actions. The rSCA package is a handy and easy-to-use tool and is freely available at http://cran.r-project.org/package=rSCA . By applying the developed package to air quality management in an urban environment, we demonstrate its effectiveness in dealing with the complicated relationships among multiple variables in real-world problems.

Developmental variability and stability in continuous-time host-parasitoid models.

Insect host-parasitoid systems are often modeled using delay-differential equations, with a fixed development time for the juvenile host and parasitoid stages. We explore here the effects of distributed development on the stability of these systems, for a random parasitism model incorporating an invulnerable host stage, and a negative binomial model that displays generation cycles. A shifted gamma distribution was used to model the distribution of development time for both host and parasitoid stages, using the range of parameter values suggested by a literature survey. For the random parasitism model, the addition of biologically plausible levels of developmental variability could potentially double the area of stable parameter space beyond that generated by the invulnerable host stage. Only variability in host development time was stabilizing in this model. For the negative binomial model, development variability reduced the likelihood of generation cycles, and variability in host and parasitoid was equally stabilizing. One source of stability in these models may be aggregation of risk, because hosts with varying development times have different vulnerabilities. High levels of variability in development time occur in many insects and so could be a common source of stability in host-parasitoid systems.

[Response of net photosyntheitc rate to light and CO2 in Schisandra chinensis from different region and growing situation].

American CI-340 portable photosynthesis system was applied to compare the response of the net photosyntheitc rate to the light and the CO2 in Schisandra chinensis form different region and growing situation. The result showed the sample plant from Liaoning had higher light compensate point, higher light saturation point, higher maximum Pn value and higher apparent quantum yield than the sample from Jilin, so it can adapt to the changes of the sunlight in a day better. The weak plant from Jilin had lower light compenstate point, higher light saturation point and higher net photosynthetic rate, so it had stronger availability on light. The stronger one was more sensitive to the weak light. The Jilin sample had higher CE and lower CO2 compensate point compared to that from Liaoning, but when the density of CO2 rised to 240 micromol/mol, the Pn of Schisandra chinensis in Liaoning became much higher than that of Jilin. Under the natural CO2 density condition, the plant from Liaoning had higher photosynthesis ability.

[Occupational exposure induced human transmissible highly pathogenic H5N1 avian influenza in one patient].

OBJECTIVE: To describe the clinical manifestations and lung imaging characteristics of the human transmissible highly pathogenic H5N1 avian influenza. METHODS: The clinical manifestations and lung imaging characteristics of human transmissible highly pathogenic H5N1 avian influenza in one patient were reviewed and analyzed. RESULTS: The patient had the clear history of occupational exposure. The fever and symptoms of influenza were prominent at onset and associated with the symptoms of the digestive tract. The laboratory findings comprised the significant decrease of the white blood cell count and the lymphocyte number and the impairment of the liver function and the myocardial enzymes. The disease progressed rapidly and multiple organs including lung, heart, liver and kidneys were involved. It was ineffective to administer anti-fungal, anti-virus and anti-inflammation medicines. It was in vain to use mechanical ventilation and pneumothorax intubation and closed drainage as well as the support therapy. In the X-ray film, the lesions progressed quickly and changed diversely with absorption and development at the same time. The nasal and throat swabs and the gargle specimen were detected with RT-PCR and real time PCR by Chinese Center for Disease Control and Prevention. The results showed that both the specific HA and NA genes of the avian influenza virus H5N1 subtype were positive and in the same time a strain of avian influenza virus A/jiangxi/1/2005H5N1) was separated and obtained from the nasal and throat swabs. The autopsy showed that diffuse injury of alveolus in lungs, DIC and multiple organ injury. CONCLUSION: The human transmissible highly pathogenic H5N1 avian influenza is a lethal disease. The disease progresses rapidly with the absorption and development at the same time in the lungs and unfortunately there are no effective therapeutic measures. The prevention of the contagious disease for the occupationally exposed population should be emphasized.

[RAPD analysis of genuiness on source of Bupleurum chinense].

OBJECTIVE: To identify the genuiness on 5 samples of Bupleurum chinense DC. from cultivating base in Dongfeng County, Jilin Province. METHODS: RAPD was used by 23 primers. RESULTS: The three primers can effectively identify the genuiness of Bupleurum chinense. CONCLUSION: The genuiness identification of Bupleurum chinense can be made with RAPD analysis.

[Study on effect of air-adjustment storing ginseng].

The research of air-adjustment storing ginseng shows that when the concentration of CO2 is above 50% and the concentration of O2 is under 5%, all adults will die in four days; all eggs and larvas will die in fifteen days. During the year of using this method air-adjustment, no vermins and mould have been found.