Large-scale Losses Research Articles

Innate and adaptive immunity gene expression profiles induced by virulent Aeromonas hydrophila infection in the immune-related organs of channel catfish

Aeromonas hydrophila causes motile Aeromonas septicemia (MAS) in freshwater fish. In recent years, MAS outbreaks due to virulent Aeromonas hydrophila (vAh) have been responsible for large-scale losses within commercial catfish farms in Mississippi and Alabama. The aim of this study was to evaluate immune gene expression in catfish immune-competent tissues during infection with vAh strain ML09-119. Specific pathogen-free catfish fingerlings were intraperitoneally infected with vAh strain ML09-119, and relative expression of thirteen immune-related genes was evaluated from head kidney, spleen, and liver. Our results revealed that vAh was detected 2 h post-infection (hpi) in the head kidney, liver, and spleen. The highest concentration of vAh was detected at 12 hpi, from which point concentrations decreased until clearance at 5 days post-infection (dpi). Gene expression analysis revealed upregulation of pro-inflammatory cytokines and innate immune response (TLR 4 and 5) in the first 24 hpi. Adaptive immune-related genes were upregulated at 7 dpi in the spleen and 14 dpi in the head kidney. Furthermore, immunoglobulin M showed significant upregulation at 14 dpi in the head kidney and 21 dpi in the spleen. In summary, vAh ML09-119 infection induced a strong inflammatory response involving multiple innate immunity genes, proinflammatory cytokines, and chemokines. Surviving catfish were able to clear the infection and produce antibodies and memory cells. Assessment of the immunological response to vAh infection is critical for understanding the pathogen's mechanisms of pathogenesis and developing means for MAS control, including vaccine development and improved treatments.

Risks of generative artificial intelligence (GenAI)-assisted scams on online sharing-economy platforms

The prevalence of scams proliferating via online platforms has been identified as an emerging societal problem resulting in large-scale financial losses for victims. Online scams typically rely for their success on the generation of fake but convincing user profiles to conceal the identities of the scammers from the people being tricked into parting with their money. The increasing sophistication of generative artificial intelligence (GenAI), which can produce outputs indistinguishable from real content, thus carries the risk of being adopted by fraudsters to assist in the enactment of online scams. This article considers the risks of the potential uptake and use of GenAI applications by online scammers operating in the sharing economy, with a focus on homestay-marketplace platforms and, in particular, the largest such platform, Airbnb.

Some questions about a new study of coefficient microcirculation in example in patients with limb-threatening ischemia and intermittent claudication

BACKGROUND: Limb-threatening ischemia is one of more important problem of fundamental and practice modern angiology. Despite the development of modern diagnostic and therapeutic approaches good results, Limb-Threatening Ischemia deserves special attention and occupies a special place among medical, biomedical and socio-economic problems. A comprehensive study will make it possible to develop new approaches, the result of which should be a decrease in the total number of diseases, as well as a minimization of complications leading to large-scale tissue losses and amputations. OBJECTIVE: We set out to develop a new non-invasive method for studying microcirculation in patients with limb-threatening ischemia. METHODS: We investigated control group and adults of both sexes (age: 50–75 years) with a confirmed diagnosis of the claudication. Men and women in ratio 1 : 1 in control and target groups. RESULTS: The research results showed that the original microcirculation coefficient calculated according to our algorithm in patients with intermittent claudication was 70% better than in the control group and 120% of patients were worse than in patients with Limb-Threatening Ischemia. CONCLUSION: Our research team created an original so called coefficient of microcirculation. In order to determine it, we studied the blood flow in the pulsations on the left and right side of the posterior tibial arteries of the legs at rest (names of lines SIN F, DEX F) and during post-ischemic hyperemia in the same lines. The study of microcirculation based on the microcirculation coefficient is very informative. The importance of this methodology for studying the total peripheral blood flow and arteriolar response is due to fundamental and clinical developments, as well as financial costs. Considering the technique described in the article can be easily mastered by medical personnel, it is easy to use in the inpatient and outpatient sectors, its use does not require large areas of the medical facility, the equipment is not expensive and the test itself does not cause inconvenience to the patient, then we can recommend including the described method is included in the list of functional diagnostic tests as a direct and quantitative method for determining microcirculation (without special costs on the part of medical institutions, insurance companies and the patient).

Characterising the distribution of mangroves along the southern coast of Vietnam using multi-spectral indices and a deep learning model

Mangroves are an ecologically and economically valuable ecosystem that provides a range of ecological services, including habitat for a diverse range of plant and animal species, protection of coastlines from erosion and storms, carbon sequestration, and improvement of water quality. Despite their significant ecological role, in many areas, including in Vietnam, large scale losses have occurred, although restoration efforts have been underway. Understanding the scale of the loss and the efficacy of restoration requires high resolution temporal monitoring of mangrove cover on large scales. We have produced a time series of 10-m-resolution mangrove cover maps using the Multispectral Instrument on the Sentinel 2 satellites and with this tool measured the changes in mangrove distribution on the Vietnamese Southern Coast (VSC). We extracted the annual mangrove cover ranging from 2016 to 2023 using a deep learning model with a U-Net architecture based on 17 spectral indices. Additionally, a comparison of misclassification by the model with global products was conducted, indicating that the U-Net architecture demonstrated superior performance when compared to experiments including multispectral bands of Sentinel-2 and time-series of Sentinel-1 data, as shown by the highest performing spectral indices. The generated performance metrics, including overall accuracy, precision, recall, and F1-score were above 90 % for entire years. Water indices were investigated as the most important variables for mangrove extraction. Our study revealed some misclassifications by global products such as World Cover and Global Mangrove Watch and highlighted the significance of our study for local analysis. While we did observe a loss of 34,778 ha (42.2 %) of mangrove area in the region, 47,688 ha (57.8 %) of new mangrove area appeared, resulting in a net gain of 12,910 ha (15.65 %) over the eight-year period of the study. The majority of new mangrove areas were concentrated in Ca Mau peninsulas and within estuaries undergoing recovery programs and natural recovery processes. Mangrove loss occurred in regions where industrial development, wind farm projects, reclaimed land, and shrimp pond expansion is occurring. Our study provides a theoretical framework as well as up-to-date data for mapping and monitoring mangrove cover change that can be readily applied at other sites.

MANAGEMENT OF DISASTER THROUGH KNOWLEDGE MANAGEMENT SYSTEM: A CASE STUDY ON SAMBALPUR DISTRICT, ODISHA

Odisha is one of the hazard-prone state and suffers both natural and man-made hazards extensively threatening millions of lives and causing large-scale losses. Disaster management in India also saw a paradigm shift after 2002 from-the humanitarian relief and rehabilitation of victims to the holistic management of disasters viewpoint. The present research addressed this absence of information related to hazards and disasters affecting the district-level Sambalpur. This research aimed to find out some aid in the management of disasters at the district level by collecting and synthesizing all related information about disasters that occur in Sambalpur using an integrated approach. The research used publicly available tools as well as data from credible sources (e.g., government reports). To full-fill the aim, the research compiled information about disasters in the Sambalpur district and prepares a spatial database. Based on socio-economic factors-population density and working population, exposure of the blocks was calculated. Risk and exposure to it assisted in making a vulnerability map of the district at the block level. Based on vulnerability, blocks were prioritized for the policy-makers to ease their management in pre-during and post-disaster situations. The result of this paper would assist policy makers to take proper and quick decisions for the management of the blocks making their management efficient and effective and it could be replicated for other districts of Odisha and India.

Adaptation strategies of agriculture to climate change and natural disasters

Climate change and agricultural production are processes characterised by substantial mutual influence. Global warming threatens the productivity of agricultural production and food security on a global scale. The purpose of the study is to develop practical adaptation strategies for the impact of climate change and natural disasters on agroeconomic processes. The study uses general scientific methods of cognition: system analysis and synthesis, concretisation, deduction, generalisation, abstraction, and formalisation. In the course of the study, the main factors of the impact of climate change on the agroeconomic sector of production are identified, preventive measures and measures to level climate risks are systematised. Based on the conducted research, a system of tools is developed as part of the strategy for adapting agriculture to climate change and natural phenomena, and the main approaches to assessing the cost of natural disasters for agroeconomics are recommended. The main measures to optimise the ecological and economic situation in the agronomic sector in the context of global warming and effective measures to prevent large-scale losses are developed. The results obtained can be used to conduct research on the dynamics of economic processes during global climate change, develop an appropriate adaptation strategy at the regional and national levels, and form an integrated approach to assessing the cost of natural disasters.

The COLOSS BEEBOOK – an example of standard methods in insect research

Abstract Honey bees play an essential role in modern agriculture as farm animals and crop pollinators, and they contribute to one third of our diet. Over the last few decades, managed honey bees have faced large-scale losses worldwide. Various causes include the spread of pathogens and parasites, habitat loss and loss of forage, pesticide use, and climate change. Many scientists investigated these issues worldwide separately and independently, often using different methodologies, and this approach might lead to conflicting and sometimes erroneous findings. To provide global and sustainable solutions, a group of bee scientists established COLOSS, a non-profit association for the prevention of honey bee COlony LOSSes. Its mission is to investigate the causes of declining bee health and find effective means to improve the well-being of bees. COLOSS comprises various Core Projects and Task Forces focusing on specific topics identified by the association to receive priority attention. Among the core projects, the COLOSS BEEBOOK is a unique venture aiming to provide a standardised methods manual for studying the honey bee. The project’s goal has been to create a comprehensive collection of established methods and techniques for honey bee research, with the aim of making studies conducted by different groups across the world more comparable. The resulting practical manual contains over 2,000 standardized methods across all fields of honey bee research. It is definitive, but evolving, research manual. There is a strong need for standardisation in the fast-growing field of edible insect farming and research. The COLOSS BEEBOOK stands as a testament to the achievements that can be made when researchers collaborate towards a common goal, and it can serve as an inspiration for the establishment of standardized methods for the mass rearing of edible insects.

Eelgrass (Zostera marina L.) populations are threatened by high sea-surface temperatures and impaired waters on Nantucket Island, USA

Eelgrass (Zostera marina L.) is a key foundation species that provides multiple ecosystem services to shallow coastal and estuarine systems in the Northern Hemisphere. It is estimated that, over the last century, up to 50 % of all Z. marina habitat has been lost along the east coast of the USA due to factors including light reduction, eutrophication, and physical disturbance. Warming sea surface temperatures are also believed to be exacerbating losses and the future of this ecosystem is unclear. Here, we assess Z. marina meadows on Nantucket, an island system located 50 km off-shore of Massachusetts, by using common indicators of seagrass plant health and environmental quality. Our results show that Z. marina meadows on Nantucket Island are thermally stressed and light-limited during parts of their peak growing season. This suggests that sea-surface temperatures are a pivotal factor, along with cultural eutrophication, in observed large-scale losses of Z. marina and that further degradation could be expected in the future as the climate continues to warm. Methods from this study may be used by managers as a guide to assess seagrass ecosystem status in degrading systems.

Calm after the storm? Similar patterns of genetic variation in a riverine foundation species before and after severe disturbance.

In summer 2011, Tropical storms Lee and Irene caused an estimated 90% decline of the submersed aquatic plant Vallisneria americana Michx. (Hydrocharitaceae) in the Hudson River of New York (USA). To understand the genetic impact of such large-scale demographic losses, we compared diversity at 10 microsatellite loci in 135 samples collected from five sites just before the storms with 239 shoots collected from nine sites 4 years after. Although 80% of beds sampled in 2011 lacked V. americana in 2015, we found similar genotypic and genetic diversity and effective population sizes in pre-storm versus post-storm sites. These similarities suggest that despite local extirpations concentrated at the upstream end of the sampling area, V. americana was regionally resistant to genetic losses. Similar geographically based structure among sites in both sampling periods suggested that cryptic local refugia at previously occupied sites facilitated re-expansion after the storms. However, this apparent resistance to disturbance may lead to a false sense of security. Low effective population sizes and high clonality in both time periods suggest that V. americana beds were already small and had high frequency of asexual reproduction before the storms. Dispersal was not sufficient to recolonize more isolated sites that had been extirpated. Chronic low diversity and reliance on asexual reproduction for persistence can be risky when more frequent and intense storms are paired with ongoing anthropogenic stressors. Monitoring genetic diversity along with extent and abundance of V. americana will give a more complete picture of long-term potential for resilience.

EUROPEAN INTEGRATION STRATEGY FOR TOURISM DEVELOPMENT OF UKRAINE

The purpose of this article is to investigate the essence and features of the implementation mechanism of the European integration strategy for the development of tourism in Ukraine.
 Methods of research. The article uses general scientific methods of researching economic phenomena: epistemological analysis, theoretical generalization, methods of analysis and synthesis, induction and deduction, structural-logical analysis, extrapolation, grouping and classification.
 Presentation of the main research material. The tourism industry of Ukraine is one of the first to suffer large-scale losses with the start of a full-scale war in Ukraine. The state budget did not receive almost 35% from the tourism industry, which was caused by the suspension or complete exit of tourist companies from the Ukrainian market of the tourism industry. The European integration process of tourism development in Ukraine consists in the implementation of European norms and standards, the spread of its own cultural, historical, geographical achievements in the EU countries. This will lead to the adaptation of European cultural identity in Ukraine and general integration into the European environment. Therefore, one of the key tasks of the Ukrainian tourism industry is the need to actively continue the implementation of the adopted strategic course for European integration through the mechanism of implementing the European integration strategy for the development of tourism in Ukraine. That is why the mechanism for the implementation of the European integration strategy for the development of tourism in Ukraine is becoming relevant, since only with the help of integration into the European space and the adoption of European laws, standards, norms and rules will Ukraine be able to revive and ensure the appropriate level of safe and high-quality tourist flow of Ukrainian tourists to EU countries and others.
 Conclusions from the conducted research. It was revealed that as a result of comprehensive actions regarding the implementation of the European integration strategy for the development of tourism in Ukraine, the following advantages will occur: the competitiveness of national and regional tourism products will increase with the prospect of expanding to European markets; the quality of life of the population will increase by ensuring socio-economic growth, environmental safety, consolidation of society, providing access to services in the field of tourism according to European standards; the emergence of new jobs and the expansion of the population's opportunities for employment and self-employment; creation of a modern tourist information industry, as well as ensuring information freedom about the tourist resources of Ukraine in the global information European space.
 Keywords: European integration strategy; integration processes; tourism development of Ukraine; tourism industry; mechanism of implementation of European integration strategy; tourism industry; competitiveness; European space; tourist flow.

Is India’s largest fertilizer manufacturer misleading farmers and society using dubious plant and soil science?

Background and AimsThe accessibility and relatively low cost of nitrogen (N) fertilizers have been a gift to humankind and made it possible to feed the exponentially growing world population. The excessive use of N, in combination with a poor N use efficiency (NUE) in crop production, is associated with severe environmental impacts, and the scientific community has repeatedly warned that the safe operating space for N within the planetary boundaries has been exceeded. In the light of these considerations, a global player and India’s largest fertilizer manufacturer, the Indian Farmers Fertilizer Cooperative (IFFCO), has recently developed and patented a nanotechnology-based fertilizer marketed as Nano Urea (liquid). Strikingly, the producers state that it is now possible to replace a 45 kg bag of conventional urea, containing 21 kg N, by foliar application of just 20 g N in the form of nano urea (NU). If so, N from this novel high-tech fertilizer product should be able to increase the NUE of crops more than 1000 times compared to conventional urea. This year, the Indian government and IFFCO announced plans to massively boost production by building 10 new factories, with an annual production capacity of 440 million bottles NU by 2025 and to expand export of the product to another 25 countries, mainly situated in Asia, Africa and South-America. In the marketing of NU, IFFCO states that there is scientific evidence for distinct beneficial properties in terms of higher crop yields and reduced negative environmental impacts. Similar ambitions have recently been presented for Nano Zinc, Nano Copper, and Nano Di-Ammonium-Phosphate, a product that entered the market in March 2023.MethodsIn this Opinion Paper, we compare the claims made by IFFCO scientists to the existing scientific evidence in the field of foliar nanofertilizers.ResultsWe observe that NU is a poorly described product with no scientifically proven effects. The product is marketed with misleading and wrong statements about its fertilizer efficiency, the underlying plant uptake pathways, and the environmental friendliness.ConclusionThe expectations raised by IFFCO are far from reality and may lead to large-scale yield losses with serious consequences for food security and the livelihood of farmers. At the same time, the confidence in innovative sustainable products as well as the science behind them may be threatened. Based on the IFFCO case, and considering the booming emergence of novel nano based fertilizers appearing all over the world these years, it is clear that much more priority should be given to scientifically prove their efficacy and mode of action, before they are launched on the markets.

Urban Flood Loss Assessment and Index Insurance Compensation Estimation by Integrating Remote Sensing and Rainfall Multi-Source Data: A Case Study of the 2021 Henan Rainstorm

To address the problems of traditional insurance compensation methods for flood losses, such as difficulty in determining losses, poor timeliness, a complicated compensation process and moral hazard, an urban flood index insurance tiered compensation model integrating remote sensing and rainfall multi-source data was proposed. This paper first extracted the area of water bodies using the Normalized Difference Water Index and estimates the urban flood area loss based on the flood loss model of remote sensing pixels. Second, the tiered compensation mechanism triggered by rainfall was determined, and the urban flood index insurance tiered compensation model was constructed using remote sensing and rainfall multi-source data. Finally, the economic losses and flood insurance compensation in urban flood were estimated. The results show that: (1) the geo-spatial distribution of flood-affected areas by remote sensing inversion is consistent with the actual rainfall characteristics of Henan Province, China; (2) based on the flood losses model of remote sensing pixels, the estimated flood losses for Henan Province are CNY 110.20 billion, which is consistent with the official data (accuracy ≥ 90%); and (3) the proposed model has good accuracy (R2 = 0.98, F = 1379.42, p < 0.05). The flood index insurance compensation in Henan Province is classified as a three-tier payout, with a total compensation of CNY 24,137 million. This paper can provide a new approach to estimate large-scale urban flood losses and the scientific design of flood index insurance products. It can also provide theoretical and technical support to many countries around the world, particularly those with underdeveloped flood insurance systems.

Disaster loss calculation method of urban flood bimodal data fusion based on remote sensing and text

Study regionMajor urban areas in Henan Province of central China. Study focusdata fusion technology is also a key and difficult point in the field of flood research. Remote sensing and text data have different modalities and scales, making fusion difficult. This study proposed a remote sensing and text bimodal data fusion model based on UFCLI, and we validated the spatiotemporal distribution of floods and the calculation results of disaster losses. The results show that through the coupling analysis of remote sensing and text bimodal data, rainstorm and flood events can be fully reproduced in space and time. The proposed UFCLI effectively improves the accuracy of remote sensing single-data inversion for urban flood disaster losses. The flood loss in Henan is 121.98 billion yuan, and the accuracy improvement result is R² increased by 0.08 and MAPE decreased by 0.88. New hydrological insights for the regionIn the case of sudden urban storm flooding with complex spatial and temporal evolution, the traditional hydrological-hydraulic model has many pending parameters, which makes it difficult to accurately calculate large-scale disaster losses. By establishing a theoretical model of bimodal data fusion, we effectively use the complementary spatiotemporal information using remote sensing and text to solve the differences in spatiotemporal scales existing between remote sensing and text data. The timeliness and accuracy of urban flood damage estimation have further improved. Data Availability StatementNot applicable.

RSCAT: Towards zero touch congestion control based on actor–critic reinforcement learning and software-defined networking

Network congestion is a phenomenon present in contemporaneous data centers (DCs) independently of scale and underlying technologies. The small-scale presence of congestion causes information delay for the DC hosted services, while in large-scale data losses or even service downtimes are not uncommon. Several congestion control algorithms out in the market have no network support, and as such, they must act and rely only on the data provided by the servers they are running on. Some algorithms have used network’s feedback to improve the performance, however these solutions depend on tuning parameters for marking queued packets, an arduous task on DCs shared by multiple applications with different workloads. In turn, Software-Defined Networking (SDN) created an opportunity to avoid congestion once the centralized controller can gather ongoing and historical information from all network switches and flows. The data gathered is enormous, and fast-computing algorithms are crucial for decision-making. In this sense, this work proposes Reinforcement Learning and SDN-aided Congestion Avoidance Tool (RSCAT), which uses data classification to determine if the network is congested and actor–critic reinforcement learning to find better Transmission Control Protocol (TCP) parameters. We evaluated RSCAT based on three workloads: a diversified DC traffic simulation; high-performance computing from NAS benchmark; and Hadoop TeraSort application. Our results show RSCAT fundamentals (reinforcement learning, data classification, and SDN) are potential candidates to achieve zero touch network congestion control. Specifically, our experimental analysis shows RSCAT, running alongside with traditional TCP congestion control algorithms like DCTCP and CUBIC, can decrease the flow completion time and applications’ runtimes in several cases, without requiring any software update on DC end-points.

Gravity-Based Community Vulnerability Evaluation Model in Social Networks: GBCVE.

The usage of social media around the world is ever-increasing. Social media statistics from 2019 show that there are 3.5 billion social media users worldwide. However, the existence of community structure renders the network vulnerable to attacks and large-scale losses. How does one comprehensively consider the multiple information sources and effectively evaluate the vulnerability of the community? To answer this question, we design a gravity-based community vulnerability evaluation (GBCVE) model for multiple information considerations. Specifically, we construct the community network by the Jensen-Shannon divergence and log-sigmoid transition function to show the relationship between communities. The number of edges inside community and outside of each community, as well as the gravity index are the three important factors used in this model for evaluating the community vulnerability. These three factors correspond to the interior information of the community, small-scale interaction relationship, and large-scale interaction relationship, respectively. A fuzzy ranking algorithm is then used to describe the vulnerability relationship between different communities, and the sensitivity of different weighting parameters is then analyzed by Sobol' indices. We validate and demonstrate the applicability of our proposed community vulnerability evaluation method via three real-world complex network test examples. Our proposed model can be applied to find vulnerable components in a network to mitigate the influence of public opinions or natural disasters in real time. The community vulnerability evaluation results from our proposed model are expected to shed light on other properties of communities within social networks and have real-world applications across network science.

Emergence and History

Emergence and History

Persistence of a Wild-Type Virulent Aeromonas hydrophila Isolate in Pond Sediments from Commercial Catfish Ponds: A Laboratory Study.

Virulent Aeromonas hydrophila (vAh) is a major bacterial pathogen in the U.S. catfish industry and is responsible for large-scale losses within commercial ponds. Administering antibiotic feeds can effectively treat vAh infections, but it is imperative to discern new approaches and better understand the mechanics of infection for this bacterium. As such, the persistence of vAh in pond sediments was determined by conducting laboratory trials using sediment from four commercial catfish ponds. Twelve chambers contained sterilized sediment, vAh isolate ML-09-119, and 8 L of water maintained at 28 °C and were aerated daily. At 1, 2, 4, 6, and 8 days, and every 7th day post-inoculation for 28 days, 1 g of sediment was removed, and vAh colony forming units (CFU) were enumerated on ampicillin dextrin agar. Viable vAh colonies were present in all sediments at all sampling periods. The vAh growth curve peaked (1.33 ± 0.26 × 109 CFU g-1) at 96 h post-inoculation. The population plateaued between days 14 and 28. No correlations were found between CFU g-1 and physiochemical sediment variables. This study validated the ability of vAh to persist within pond sediments in a laboratory setting. Further research on environmental factors influencing vAh survivability and population dynamics in ponds is needed.

High Resolution Spatio-Temporal Model for Room-Level Airborne Pandemic Spread

Airborne pandemics have caused millions of deaths worldwide, large-scale economic losses, and catastrophic sociological shifts in human history. Researchers have developed multiple mathematical models and computational frameworks to investigate and predict pandemic spread on various levels and scales such as countries, cities, large social events, and even buildings. However, attempts of modeling airborne pandemic dynamics on the smallest scale, a single room, have been mostly neglected. As time indoors increases due to global urbanization processes, more infections occur in shared rooms. In this study, a high-resolution spatio-temporal epidemiological model with airflow dynamics to evaluate airborne pandemic spread is proposed. The model is implemented, using Python, with high-resolution 3D data obtained from a light detection and ranging (LiDAR) device and computing model based on the Computational Fluid Dynamics (CFD) model for the airflow and the Susceptible–Exposed–Infected (SEI) model for the epidemiological dynamics. The pandemic spread is evaluated in four types of rooms, showing significant differences even for a short exposure duration. We show that the room’s topology and individual distribution in the room define the ability of air ventilation to reduce pandemic spread throughout breathing zone infection.

Integrated assessment of agricultural practices on large scale losses of ammonia, greenhouse gases, nutrients and heavy metals to air and water.

To gain insight in the environmental impacts of crop, soil and nutrient management, an integrated model framework INITIATOR was developed predicting: (i) emissions of ammonia (NH3) and greenhouse gases (GHG) from agriculture, including animal husbandry and crop production and (ii) accumulation, leaching and runoff of carbon, nutrients (nitrogen, N, phosphorus, P, and base cations) and metals in or from soils to groundwater and surface water in the Netherlands. Key processes in soil are included by linear or non-linear process formulations to maintain transparency and to enable data availability for spatially explicit application from field up to national level. Calculated national trends in nutrient losses over 2000-2020 compared well with independent estimates and showed a reduction in N and P input of 26 to 33 %, whereas the surplus declined by 33 % for N and 86 % for P due to increased crop yields and reduced inputs. This was accompanied by a reduction of 30-35 % in atmospheric emissions of ammonia and nitrous oxide as well a decline in N and P runoff of 35 and 10 %, respectively, whereas the emission of methane increased with 4 %. Model results compared well with (i) large scale observations of ammonia concentrations in air and nitrate concentrations in upper groundwater and ditch water, (ii) with nitrous oxide emissions and phosphorus adsorption in experiments at field scale and (iii) with metal adsorption in large scale soil datasets. Various mitigation measures were evaluated in view of policy ambitions for climate, soil and environmental quality for 2030, i.e. a reduction of 50 % for NH3, 11-17 % for GHG, 20 % for N runoff and 40 % for P runoff and an ambition of 50 % GHG emission reduction for 2050. The measures focused on a combination of animal feeding, low emission housing and application technologies, improved crop, soil and nutrient management, all being applied with an effectiveness of 100 % and 50 %, respectively. In addition, we evaluated impacts of 50 % livestock reduction, and combination scenarios of measures and livestock reduction. Full implementation of all measures can reduce NH3 emission, N leaching and N runoff by approximately 40-50 % and GHG emissions by approximately 30 %, but there is less potential to reduce P runoff, being <10 %. The combination of a more likely 50 % implementation/effectiveness of measures with 25 % livestock reduction leads to a comparable reduction. Required reductions from Dutch agriculture seem not possible with improved management only, but also requires livestock reduction, especially when the NH3 ambitions at the short term (2030) and the climate ambitions for the long term (2050) should be attained.

The metabolites involved in phenylpropanoid biosynthesis increase the susceptibility of octoploid strawberry to crown rot caused by Colletotrichum siamense

Crown rot caused by Colletotrichum siamense is a serious disease affecting strawberry that leads to large-scale losses, both in their quality and yield, in the Yangtze River Basin, China. Secondary metabolites are known to play pivotal roles in defense responses against invading pathogens, and identifying metabolites involved in crown rot is necessary for understanding induced resistance and developing improved genetic resistance against strawberry crown rot. To explore this research topic, the infection process was verified via observation of the phenotypes and histopathology of susceptible ‘Benihoppe’ strawberry inoculated with C. siamense. Following this, metabolomic analysis was used that identified phenolic acids, flavonoids, and amino acids as the metabolites accumulated in the rotten crown caused by the C. siamense infection process. Furthermore, the transcripts encoding enzymes involved in the ‘phenylpropanoid biosynthesis’ of phenolic acids, trans-cinnamate 4-monooxygenase, caffeoylshikimate esterase, and caffeic acid 3-O-methyltransferase were all upregulated in the rotten crown, as shown by transcriptomic analysis and qRT-PCR. In addition, the metabolites p-coumaric acid, caffeic acid, and ferulic acid in the ‘phenylpropanoid biosynthesis’ pathway all enhanced C. siamense infection, as shown by an exogenous validation experiment. Together, the results demonstrate that the ‘phenylpropanoid biosynthesis’ pathway was induced during crown rot and thus highlight the potentially crucial regulatory role of this pathway in plant immunity.