Human Survival Research Articles

Prediction of PM2.5 concentration based on a CNN-LSTM neural network algorithm

Fine particulate matter (PM2.5) is a major air pollutant affecting human survival, development and health. By predicting the spatial distribution concentration of PM2.5, pollutant sources can be better traced, allowing measures to protect human health to be implemented. Thus, the purpose of this study is to predict and analyze the PM2.5 concentration of stations based on the integrated deep learning of a convolutional neural network long short-term memory (CNN-LSTM) model. To solve the complexity and nonlinear characteristics of PM2.5 time series data problems, we adopted the CNN-LSTM deep learning model. We collected the PM2.5data of Qingdao in 2020 as well as meteorological factors such as temperature, wind speed and air pressure for pre-processing and characteristic analysis. Then, the CNN-LSTM deep learning model was integrated to capture the temporal and spatial features and trends in the data. The CNN layer was used to extract spatial features, while the LSTM layer was used to learn time dependencies. Through comparative experiments and model evaluation, we found that the CNN-LSTM model can achieve excellent PM2.5 prediction performance. The results show that the coefficient of determination (R2) is 0.91, and the root mean square error (RMSE) is 8.216 µg/m3. The CNN-LSTM model achieves better prediction accuracy and generalizability compared with those of the CNN and LSTM models (R2 values of 0.85 and 0.83, respectively, and RMSE values of 11.356 and 14.367, respectively). Finally, we analyzed and explained the predicted results. We also found that some meteorological factors (such as air temperature, pressure, and wind speed) have significant effects on the PM2.5 concentration at ground stations in Qingdao. In summary, by using deep learning methods, we obtained better prediction performance and revealed the association between PM2.5 concentration and meteorological factors. These findings are of great significance for improving the quality of the atmospheric environment and protecting public health.

Spatiotemporal Evolution and Driving Forces of Production-Living-Ecological Space in Arid Ecological Transition Zone Based on Functional and Structural Perspectives: A Case Study of the Hexi Corridor

The rational allocation of land resources is crucial to ensuring human well-being, livelihood, and survival. The study of Production-Living-Ecological Space (PLES) provides new perspectives on land resource allocation. However, few studies have assessed the feasibility of PLES optimization in ecological transition zones. For this study, using the composite functional space classification method, a classification and functional utility scoring system were constructed. Various methods, including dynamic attitude, transfer matrix, and spatial autocorrelation, were employed to characterize the evolution of the quantity and quality of PLES in the Hexi Corridor. Moreover, the mechanisms driving these changes were explored using a geodetector. Our findings revealed that: (1) The distribution of Production-Ecological Space (PES) is higher in the west and south and lower in the east and north. Production-Living Space (PLS) is scattered. Ecological-Production Space (EPS) is mostly distributed in the south or west, whereas Ecological Space (ES) is mainly located in the north and west of the Hexi Corridor. (2) From 1980 to 2020, the area of PES and PLS increased by 2037.84 km2 and 673 km2, respectively; the area of EPS was relatively stable, and the area of ES decreased by 2523.06 km2. (3) The evolution of PLES quality indicated that the high functional utility area of PES and PLS was roughly the same as the expanded functional utility area, whereas the expanded functional utility area of EPS and ES is similar to the median functional utility area. (4) The spatiotemporal evolution of PLES is closely linked to natural, economic, and social factors.

Relationship among micro-pollutants, economic growth, renewable energy, nanotechnology and ecological foot prints of China: evidence from panel data analysis.

An important concern is the availability of clean drinking water, which is an essential need for human survival. This issue arises due to the existence of hazardous micropollutants originating from various emission sources. Nanotechnology aids in the mitigation of micropollutants by assimilating and counteracting their effects, hence diminishing their influence on water and other ecosystems. The study investigates the relationship between nanotechnological progress, the adoption of renewable energy, environmental consequences, and economic growth in China, using the Environmental Kuznets Curve theory as a conceptual framework. The study employs panel cointegration tests to analyze structural breaks from 2000 to 2020. Nanotechnology is expected to reduce environmental degradation and the presence of micro-pollutants by increasing the use of renewable energy and promoting energy conservation. Nanotechnology is crucial for mitigating micro-pollutants and advancing sustainable development in this specific context. However, the literature also highlights the harmful consequences of nanoparticle emissions caused by nanotechnology on human and environmental health for a long duration, requiring more examination. This research is the first empirical inquiry into the relationship between improvements in nanotechnology, the use of renewable energy, economic growth, and ecological effect, all within the context of the Environmental Kuznets Curve theory. The results confirm the successful incorporation of all components with a focus on long-term outcomes. The findings suggest that the EKC hypothesis is relevant in China. In China, advancements in nanotechnology have a moderating effect on environmental degradation. The use of renewable energy sources in China enhances environmental circumstances. Given the offered empirical evidence, it is advisable for the government to have a leading role in the development of innovative nanotechnologies that have low emissions of nanoparticles. By using this approach, it will be possible to encourage the conservation of energy and the use of renewable sources in a more secure way, hence improving the effectiveness of sustainable development initiatives.

Regional variation in the role of humidity on city-level heat-related mortality.

The rising humid heat is regarded as a severe threat to human survivability, but the proper integration of humid heat into heat-health alerts is still being explored. Using state-of-the-art epidemiological and climatological datasets, we examined the association between multiple heat stress indicators (HSIs) and daily human mortality in 739 cities worldwide. Notable differences were observed in the long-term trends and timing of heat events detected by HSIs. Air temperature (Tair) predicts heat-related mortality well in cities with a robust negative Tair-relative humidity correlation (CT-RH). However, in cities with near-zero or weak positive CT-RH, HSIs considering humidity provide enhanced predictive power compared to Tair. Furthermore, the magnitude and timing of heat-related mortality measured by HSIs could differ largely from those associated with Tair in many cities. Our findings provide important insights into specific regions where humans are vulnerable to humid heat and can facilitate the further enhancement of heat-health alert systems.

Water Potability Prediction Using Machine Learning

Abstract: For human survival, water is an essential and indispensable resource, and preserving its purity is paramount to people's health. Contaminated drinking water can lead to serious health problems, such as cholera, diarrhea, and other waterborne illnesses. Thus, maintaining clean and safe water becomes essential to advancing public health. Recent research indicates that water-related ailments claim the lives of a noteworthy 3,575,000 individuals annually. Thus, a reliable indicator of water potability could significantly lower the prevalence of these illnesses. Machine learning algorithms have emerged as highly effective instruments for precisely and promptly monitoring water resources by accurately forecasting the quality of the water. The Drinking Water dataset on Kaggle is the source of the water samples used in this study, and various algorithms are used to estimate water potability based on these properties. Nine different metrics make up this dataset: pH, hardness, solids, trihalomethanes, sulphates, chloramines, organic carbon, conductivity, and turbidity. We seek to ascertain the potability of drinking water by utilizing a variety of algorithms, including Random Forest, SVM, Decision Tree, and KNN. Among other notable results, the Random Forest algorithm outperforms conventional machine learning models, producing an astounding accuracy of 99.5%. It also performs well, producing an accuracy of 74%. As a result, this study has great potential to supply researchers, water management professionals, and policymakers with accurate data on water quality, increasing the efficacy of water potability monitoring

Religiosity and Carbon Emissions: International Evidence

ABSTRACT Informal institutions can exert important effects on individual behavior, corporate decisions and social outcomes. Against the background in which global warming poses the critical menace to the world economy and human survival, this study draws on the role theory and adopts the OLS regression and IVREG model to examine the impact of religiosity (an informal institution) on carbon emissions at the country level. Our findings reveal that religiosity, proxied by the percentage of religious population within a country, is significantly negatively associated with carbon emissions, validating the important role of religious social norms in carbon emissions reduction. Moreover, the Kyoto Protocol attenuates the negative effect of religiosity on carbon emissions. Our study calls on policy makers and stakeholders to attach the importance to the impact of religiosity on environmental protection and the trade-off between economic development and carbon emissions reduction.

Nonindustrial pretreatment and enzymes can yield sufficient calories from lignocellulosic biomass for human survival

AbstractFollowing a global catastrophe causing reduced sunlight, the environment would become unfavorable for crop growth. Under such conditions, people might need to convert inedible plant biomass into food to meet their daily nutritional requirements. However, the possibility of converting biomass into food under low‐resource conditions has not been thoroughly studied. To address this uncertainty, we evaluated the potential for using resources available in a typical household to extract sugars from willow biomass and meet the carbohydrate needs of an adult. Grinding willow biomass in a household blender for 24 min produced willow particles similar to those produced in a laboratory‐scale Wiley mill. Thermal treatments of these particles with hot water extraction, pressure cooking, or microwaving only extracted 0.5%–0.8% (w/w) glucose from the biomass. Household acid or alkali treatments yielded only 0.5% (w/w) glucose. These sugar yields would be insufficient to provide nutrition to an adult. In contrast, enzymatic hydrolysis of pretreated willow at 50°C for 72 h yielded 2%–8% (w/w) glucose, and pretreating willow with sodium hydroxide and pressure before enzymatic treatment increased glucose yields to 28% (w/w). With this pretreatment approach and subsequent enzymatic conversion, ~1.4 kg of biomass/day could potentially fulfill the energy needs of an adult under post‐catastrophic conditions. We posit that while biomass can be successfully pretreated for enzymatic deconstruction at a household level, producing sufficient enzymes for efficient sugar extraction from inedible plant biomass in a post‐catastrophic environment might not be feasible at the household scale, thus requiring community‐scale infrastructure and coordination.

Aloe vera Cuticle: A Promising Organic Water-Retaining Agent for Agricultural Use

Water is an important resource for both human and environmental survival. However, due to current human practices, we are facing a serious crisis in accessing water. Thus, solutions must be explored to optimize the use of this resource. In the search for an organic water-retaining agent for agricultural use, the techno-functional properties of Aloe vera (Aloe barbadensis Miller) cuticle, an agro-industrial residue generated after gel extraction, were evaluated. The residue was dried and ground. The effects of particle size (180 µm and 250 µm), temperature (10 °C, 20 °C, 30 °C, and 40 °C), and pH (4.5, 6.0, and 7.0) on the solubility and water-holding capacity (WHC) of the obtained product (i.e., hydrogel) were then evaluated. The treatment with the highest WHC was selected and compared with the WHC of a commercial synthetic polyacrylamide gel widely used in agriculture. The effects of KNO3 and Ca(NO3)2 at different concentrations (10 g L−1, 20 g L−1, 30 g L−1, and 40 g L−1) on the WHC of the gels were assessed. Particle size, temperature, and pH interactions had statistically significant effects on solubility, while the WHC was affected by particle size × temperature and pH × temperature interactions. The highest product solubility (75%) was obtained at the smallest particle size (i.e., 180 µm), pH 4.5, and 20 °C. Meanwhile, the highest WHC (18 g g−1) was obtained at the largest particle size (i.e., 250 µm), pH 6.0, and 20 °C. This optimized gel kept its WHC across both salts and their concentrations. In contrast, the commercial gel significantly decreased its WHC with salt concentration. The product elaborated with A. vera cuticle could have bioeconomic potential as a water-retention agent for agricultural use, with the advantage that it is not affected by the addition of salts used for plant fertilization.

Prediction of Dynamics of Riverbank Erosion: A Tale of the Riverine Town Chandpur Sadar

River channel migration often occurs inside the floodplain areas of the river. The structural changes occurring in rivers within floodplain zones serve as a significant ecological signal of their tremendous impact on both ecological and human survival. The aim of the research is to assess the spatial and temporal fluctuations of the Meghna River's bankline from 1990 to 2020 (for 30 years), and to predict the future bankline position. The Landsat imageries from USGS were used for this study. The bankline was extracted from satellite imageries (Landsat) using Modified Normalized Difference Water Index (MNDWI) indices. The variations in bankline dynamics were assessed through the application of Net Shoreline Movement (NSM) and End Point Rate (EPR) analyses using the Digital Shoreline Analysis System (DSAS). Predictive modeling was conducted utilizing the Modified EPR Model, with validation performed via the Root Mean Square Error (RMSE) technique. Adjustment of errors was achieved by employing error coefficients tailored to each individual transect. Over the past three decades, the cumulative river erosion in Chandpur Sadar amounted to 18.03 square kilometers, with the highest Net Shoreline Movement (NSM) recorded at −1979.62 m during the study period. The forecasted maximum Net Shoreline Movement (NSM) is anticipated to be −495.79 m, signifying that the transect is associated with erosion, resulting in landward movement. Conversely, the minimum NSM is projected to be −1.01 m. The most significant movement is expected to occur in Puran Bazar, while the least movement is foreseen in Bakharpur. On average, the NSM across all transects is predicted to be −76.25 m. This study presents a cost-effective approach for evaluating the changes in river erosion over time. This technique may be valuable for policymakers in developing effective plans to reduce the negative impacts of erosion-related risks.

Pleistocene archaeology and environments of the Free State, South Africa

ABSTRACT Pleistocene climate variability is often seen as a major cause of much of the evidence observed in the archaeological and palaeontological record of Africa. While continent-wide climate systems play an important role when testing pan-African human evolutionary processes, a more focused perspective centred on specific ecosystems at a regional level allows a detailed assessment of the different spatiotemporal scales of the proxies used to reconstruct past environments and the ways humans adapted to their change over time. Recent research in the arid interior of South Africa has provided insights into the availability of freshwater in the open landscape, which is a fundamental factor for human survival and the spatiotemporal distribution of which may have had a major influence on adaptive strategies. This article reviews the Pleistocene archaeological and environmental evidence of the Free State province of South Africa, which has produced major localities such as Cornelia-Uitzoek, Florisbad and Rose Cottage Cave, with the aim of providing a starting point for the discussion over freshwater availability with regard to southern Africa’s Grassland Biome. Particular emphasis is given to the description of multi-proxy approaches including the analysis of sediments, faunal remains, enamel stable isotopes, pollens and phytoliths and absolute dating based on trapped-charge methods. The picture that emerges highlights the paucity of Pleistocene datasets in the Free State and the necessity to expand research at open-air sites and improve the chronological resolution of human occupations and palaeoenvironmental proxies.

Response and prediction of ecosystem service values to land use change resulting from underground coal mining in high groundwater table areas: A case study of Jining City, China

Ecosystem services are crucial for human survival and development. Changes in land use can lead to alterations in ecosystem service functions. However, the mechanism of how land use changes resulting from underground coal mining affect ecosystem service remains unclear, especially in areas with high groundwater levels. To address this issue, we studied the spatiotemporal in land use change in Jining City from 2000 to 2020 and employed Markov-PIM (probability integration method)-PLUS (Patch-generate Land Use Simulation) model to simulate land use change and ecosystem service values (ESV) in Jining City by 2030 under four different scenarios. The results indicate that from 2000 to 2020, the area of ecological space increased by 169.99 km2, and the area of living space expanded by 775.28 km2, while the area of production space decreased by 945.27 km2, conversely. These changes led to a 3 million yuan increase in total ESV. From 2020 to 2030, there was a marked decline in ESV under both natural development (ND) and living space preference (LSP) scenarios, with decreases of 0.59 billion yuan and 1.38 billion yuan, respectively. In contrast, in production space preference (PSP) and ecological space preference (ESP) scenarios, ESV increase by 0.22 billion yuan and 0.34 billion yuan. The ESP scenario is an effective strategy to strengthen environmental protection, significantly improve the ESV level of Jining City and meet the development goals of Jining City. Finally, the study concludes with recommendations like comprehensive management, industrial upgrading, and collaborative development for optimizing Jining City's future land use and enhance ESV for urban development.

‘There was a star riding through clouds one night, and I said to the star, “Consume me”’: Post-human extinction in Virginia Woolf’s The Waves

Abstract Human beings are now in a period of the Earth’s history during which their extinction seems ever more probable due to extreme climate change, global pandemics, international conflicts, and lack of long-term intergovernmental or transnational cooperation to tackle these threats to life. Concern for the future of humanity is not, however, a uniquely contemporary phenomenon. Several of Virginia Woolf’s late novels suggest an awareness of the potential for human extinction. Where this concern can be found in The Waves (1931), the text does not express nihilism or panic at the prospect. Instead, the idea of a world without humans is seized as the provocation to continue an experiment which runs throughout Woolf’s writing: to reimagine the relationship between the human and the non-human. By exploring this writerly concern in The Waves, this essay argues that Woolf also redefines human death by reconceptualizing the natural world in ways which undo anthropocentric understandings of the potential extinction of the human species. By re-reading Woolf’s seventh novel through ecocritical practices and alongside ideas from contemporary post-humanist critical theory, the analyses proposed herein suggest that The Waves encourages a reassessment of our position in and response to a human-made environmental crisis which threatens the future of existence, human and non-human, in the twenty-first century.

Determination of Water Quality for Ground Water Near Municipal Dumpsite in Ibadan Southwest Nigeria

Access to clean drinking water is crucial for human health and survival. In vicinities near municipal dumpsites, ensuring groundwater quality is especially vital to safeguard the ecosystem and human health. This study utilized the Entropy Weighted Water Quality Index (EWQI), a novel assessment tool, to evaluate groundwater near the Awotan dumpsite in southwestern Nigeria. Forty-five groundwater samples were collected during rainy and dry seasons and explored for groundwater quality using EWQI. The physicochemical parameters fall within permissible limits for domestic purposes during both seasons. Nonetheless, 2% of the samples surpassed the recommended nitrate limit of 45 mg/L, and all samples had iron levels above the 0.3 mg/L threshold. The calculated EWQI values for these samples ranged from 20 to 528, with 2% deemed excellent, 16% good, 51% medium, 18% poor and 13% extremely poor in terms of water quality for domestic use. The spatial variability in water quality levels means that there are localised impacts of the dumpsite and thus there is need for targeted management strategies. We recommend the government undertake sustainable retrofitting to enhance landfill design, incorporating an efficient leachate collection system to lessen groundwater pollution. Additionally, nearby residents should implement water treatment methods to improve water quality and minimize health risks.

A Review of Exploration and Development Technologies for Coal, Oil, and Natural Gas

Energy is the fundamental prerequisite for human survival and development, as well as the driving force behind the progress of human civilization [...]

Performance Evaluation and Comparison of Deep Neural Network Models for African Soil Properties Prediction

ABSTRACT The significance of soil in agriculture and human survival cannot be overstated. Given its limited supply, improving soil properties is imperative. This study addresses this challenge by predicting five major soil properties—organic carbon, pH values, Mehlich-3 extractable calcium, Mehlich-3 extractable phosphorus, and sand content—utilizing mid-infrared absorbance measurements from the African Soil Information Service (AfSIS) dataset, covering non-desert regions of Africa. With a pressing need for a reliable and efficient model to predict African soil properties using spectral measurements, this study fills a crucial gap, addressing the scarcity of functional soil property databases in Africa. The developed model eliminates costly soil sample preparation and lengthy chemical analysis, applicable in both onsite and laboratory settings for determining soil functional properties. By employing stacked autoencoders for feature dimensionality reduction and combining discrete wavelet transform with two feature selection methods (stepwise regression and random forest) to build robust multi-layer perceptron (MLP) models, the study offers a comprehensive approach. Evaluation metrics including root mean square error (RMSE), scatter index (SI), Variance Accounted For (VAF), Nash-Sutcliffe Model Efficiency (NSE), and Correlation Coefficient (R) demonstrate the superior performance of the MLP model informed by stacked autoencoder-selected features, outperforming models informed by wavelet-transformed features and partial least square regression. This best-performing autoencoder-based model presents a valuable tool for soil scientists tasked with modeling African soil properties.

Next generation plant biostimulants & genome sequencing strategies for sustainable agriculture development.

The best environment for plant growth and development contains certain essential metabolites. A broad category of metabolites known as "plant biostimulants" (PBs) includes biomolecules such as proteins, carbohydrates, lipids, and other secondary metabolites related to groups of terpenes, specific nitrogen-containing compounds, and benzene ring-conjugated compounds. The formation of biomolecules depends on both biotic and abiotic factors, such as the release of PB by plants, animals, and microorganisms, or it can result from the control of temperature, humidity, and pressure in the atmosphere, in the case of humic substances (HSs). Understanding the genomic outputs of the concerned organism (may be plants or others than them) becomes crucial for identifying the underlying behaviors that lead to the synthesis of these complex compounds. For the purposes of achieving the objectives of sustainable agriculture, detailed research on PBs is essential because they aid in increasing yield and other growth patterns of agro-economic crops. The regulation of homeostasis in the plant-soil-microbe system for the survival of humans and other animals is mediated by the action of plant biostimulants, as considered essential for the growth of plants. The genomic size and gene operons for functional and regulation control have so far been revealed through technological implementations, but important gene annotations are still lacking, causing a delay in revealing the information. Next-generation sequencing techniques, such as nanopore, nanoball, and Illumina, are essential in troubleshooting the information gaps. These technical advancements have greatly expanded the candidate gene openings. The secondary metabolites being important precursors need to be studied in a much wider scale for accurate calculations of biochemical reactions, taking place inside and outside the synthesized living cell. The present review highlights the sequencing techniques to provide a foundation of opportunity generation for agricultural sustainability.

« Une Terre au défi de l’habitabilité ». Entretien avec Nathalie Blanc

Faced with an environmental crisis that threatens the survival of humanity, contemporary Western societies need to invent new ways of living on Earth. According to Nathalie Blanc, art and literature, and the research associated with them, have a role to play in this area: because they enable us to develop new imaginaries, they help to establish new relationships between nature and culture. Nathalie Blanc is a geographer, director of research at the CNRS, and director of the Centre des Politiques de la Terre (Université Paris Cité / Sciences Po), which she co-founded in 2019. Her work focuses on nature in the city, and more specifically on the cultural dimension of relations with nature at the heart of local policy projects. Committed to a research-creation approach, she examines the role of art and creativity in environmental mobilisations.

Neuritin 1 Drives Therapeutic Preservation of Retinal Ganglion Cells in an Ex Vivo Human Glaucoma Model.

Purpose: Glaucoma is a leading cause of irreversible blindness. Glaucomatous intraocular pressure (IOP) triggers deleterious effects, including gliosis, optic nerve (ON) axonal retraction, neurotrophic factor deprivation, inflammation, and other pathological events, leading to retinal ganglion cell (RGC) loss. Trophic factor impairment enhances RGC apoptosis susceptibility. Neuritin 1 (NRN1), a neurotrophic protein downstream of various neurotrophins, exhibited RGC protection and regeneration in axotomy models. We evaluated human recombinant NRN1's impact on human RGCs cultured in pressurized conditions within the ex vivo translaminar autonomous system to simulate glaucoma pathogenesis. Methods: Human glaucomatous and non-glaucomatous donor eyes were obtained from eye banks according to the Declaration of Helsinki. Initially, we evaluated NRN1and RGC marker expression in glaucoma and non-glaucomatous retina to determine the NRN1 level and its association with RGC loss. Further, we evaluated NRN1's therapeutic potential by treating pressurized human eyes at normal and high IOP for seven days. Retina, ON, and conditioned medium were analyzed for RGC survival (THY1, RBPMS), gliosis (GFAP), apoptosis (CASP3, CASP7), and extracellular matrix deposition (COLIV, FN) by qRT-PCR and western blotting. Paraphenylenediamine staining assessed ON axonal degeneration, whereas ex vivo electroretinogram assessed retinal activity. Results: Glaucomatous retinas exhibited significant reductions in both NRN1 (*p = 0.007, n = 5) and RGC marker expression (*p = 0.04, n = 5). NRN1 treatment reduced gliosis, extracellular matrix deposition, ON degeneration, and increased retinal activity in pressure-perfused eyes. Conclusions: Our study confirms that NRN1 enhances human RGC survival and improves retinal function in degenerative conditions, substantiating it as a promising candidate for rescuing human RGCs from degeneration.

Effect of Radical Prostatectomy on Survival for Men with High-risk Nonmetastatic Prostate Cancer Features Selected According to STAMPEDE Criteria: An EMPaCT Study

Background and objectiveA meta-analysis of two randomized STAMPEDE platform trials revealed that 3 yr of abiraterone acetate in addition to androgen deprivation therapy and radiation therapy significantly improved metastasis-free and overall survival (OS) in high-risk nonmetastatic prostate cancer (PCa) and should be considered a new standard of care. The aim of our study was to assess long-term cancer-specific survival (CSS) and OS for surgically treated patients with newly diagnosed nonmetastatic node-negative PCa meeting the STAMPEDE criteria for high risk. MethodsThis was a retrospective, multicenter cohort study of patients with European Association of Urology (EAU) high-risk PCa who underwent radical prostatectomy and extended pelvic lymph node dissection. CSS was assessed using cumulative incidence curves and the Kaplan-Meier method was used to evaluate OS. We used a Fine and Gray model to evaluate the prognostic value of STAMPEDE high-risk factors (SHRFs) for CSS, and a Cox proportional-hazards model to assess the association of SHRFs with OS. Key findings and limitationsA total of 2994 patients with EAU high-risk PCa were divided into groups with 0, 1, 2, or 3 SHRFs. The 10-yr survival estimates for patients with 0–1 versus 2–3 SHRFs were 95% versus 82% for CSS and 81% versus 64% for OS (both p < 0.0001). In comparison to patients with 0 SHRFs, hazard ratios were 1.2 (p = 0.5), 3.9 (p < 0.0001), and 5.5 (p < 0.0001) for CSS, and 1.1 (p = 0.4), 2.2 (p < 0.0001), and 2.5 (p = 0.0004) for OS for patients with 1, 2, and 3 SHRFs, respectively. Conclusions and clinical implicationsOur results confirm that the STAMPEDE high-risk criteria identify a subgroup of patients with highly aggressive PCa features and adverse long-term oncological outcomes. This population is likely to benefit most from aggressive multimodal treatment. Nevertheless, we have shown for the first time that surgery remains a viable treatment option for patients with STAMPEDE high-risk PCa. Patient summaryProstate cancer that meets the high-risk definitions from the STAMPEDE trial is an aggressive type of cancer. Our results for long-term cancer control outcomes indicate that surgery is a viable option for the subgroup of patients with this type of prostate cancer.

The Evolution of Forest Landscape Connectivity and Ecological Network Construction: A Case Study of Zhejiang’s Ecological Corridors

As main components of terrestrial ecosystems, forests play irreplaceable roles in maintaining ecological balance and protecting the basic environment for human survival and development. In this study, the dynamic changes in the forest landscape connectivity in Zhejiang province in 2000, 2010, and 2020 were detected by identifying ecological sources and evaluating connectivity indexes based on morphological spatial analysis (MSPA) and a minimum cumulative resistance (MCR) model. The results are as follows: (1) The forest area of Zhejiang increased by 64.88% from 2000 to 2020, indicating that the overall habitat quality has improved and that ecological risks have decreased, which are attributed to Zhejiang’s adherence to comprehensive environmental management. (2) Regions with low connectivity are distributed mainly in the north, where human activities are intensive. The overall pattern of high connectivity in the middle of the region and low connectivity elsewhere demonstrates the uneven distribution of forest landscape connectivity across the province. (3) The extracted ecological corridors have a mesh-like structure that is dense in the middle and slack in the north. Important corridors have disappeared over time, indicating potential issues in maintaining connectivity for species migration. (4) These results can provide optimization strategies for ecological infrastructure planning in Zhejiang province and offer a theoretical reference for the optimization of the ecological network system.