Suitable Model For Assessment Research Articles

Identification of Land and Potential Production of Willow Biomass Crops Using a Multi-Criteria Land Suitability Assessment

The New York State (NYS) Climate Act aims for net-zero emissions across all economic sectors by 2050, with renewable biofuels playing a key role in this transition. Approximately half of the biomass required for these biofuels is expected to come from purpose-grown sources like willow. To address this demand, we assessed land availability and biomass production potential for willow using a GIS-based fuzzy logic Land Suitability Assessment (LSA) model under three land scenarios: (1) including all cropland, (2) excluding conventional crops, and (3) excluding any cropland. Our findings show that NYS has the potential for between 1.07 and 1.59 million ha for willow cultivation, capable of producing 14.0 to 20.6 million dry Mg of biomass annually. Grassland/pasture accounts for 32–51%, and herbaceous cover for 32–48% of the potential areas. Between 33% and 53% of the area identified was in parcels that were 2–20 ha in size. These results highlight the considerable potential for purpose-grown biomass in NYS, supporting the state’s decarbonization goals and renewable energy transition.

Development of a Cognitive Assessment Checklist for First-Grade Mathematics: Utilizing Hierarchical Cognitive Diagnostic Modeling in Elementary Education

This study aimed to address the need for a comprehensive assessment tool to evaluate the mathematical abilities of first-grade students through cognitive diagnostic assessment (CDA). The primary challenge involved in this endeavor was to delineate the specific cognitive skills and sub-skills pertinent to first-grade mathematics (FG-M) and to determine the most suitable model for assessment. Employing a mixed-methods approach, the research identified nine cognitive attributes essential for FG-M and developed a Q-matrix delineating the relationship between these attributes. A preliminary version of the FG-M checklist comprising 74 items was formulated. Subsequently, the assessment tool was administered to 1018 first-grade students, with their teachers utilizing the FG-M checklist for evaluation. Results indicated a commendable accuracy in classifying the nine cognitive attributes, ranging from 0.68 to 0.92, with an average accuracy of 0.76. Furthermore, the validity of the scores was corroborated by a substantial correlation (r = 0.78) between scores from the FG-M checklist and the arithmetic subtest of the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV). Overall, the study concluded that the scores from the FG-M checklist are reliable for assessing the mathematical skills of first-grade students. The findings underscored the students’ proficiency in fundamental mathematical concepts such as cardinality, addition, subtraction, weight, and statistics. However, areas of weakness were identified in concepts related to multiplication, time, symmetry, and geometry. This research contributes significantly to the advancement of CDA in educational contexts, providing educators with a valuable tool for identifying students’ strengths and weaknesses in mathematical cognition.

Developing a suitability assessment model for Public Private Partnerships: a case in urban China

Spatial heterogeneity is a fundamental aspect of Public Private Partnerships (PPPs). The appropriateness of PPPs applications varies across regions. While the assessment of suitability for PPPs is crucial, it has received limited scholarly attention. This study, using China as s representative case, developed a suitability assessment model and evaluated it across 287 prefecture-level cities in China. The results reveal considerable spatial variation in the suitability of PPPs within the country. Utilizing the natural breakpoint method, this study categorized Chinese cities into four distinct levels of PPP suitability: high, medium, low, and unsuitable. 219 cities were identified as either unsuitable or having low suitability for PPPs, suggesting that PPPs may not be broadly applicable in the current Chinese context. This study not only lays the groundwork for sustainable PPP application in China but also significantly contributes to shaping strategies for successful PPP implementation globally.

Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method

The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development.

DfMA Integrated Assessment Model for Selecting Optimal Design Alternatives in OSC Projects

To select the optimal design alternative in off-site construction (OSC) projects, the building industry has turned to design for manufacturing and assembly (DfMA). However, most DfMA developments in the OSC field until now have been on improving the production process in OSC projects and guideline strategies on how to apply them. The application of DfMA guidelines only provides background knowledge to designers on how to design. However, it cannot inspect whether the DfMA concept is fully reflected in a design draft to examine the suitability to the OSC production environment, and it cannot determine the optimal alternative from among multiple design alternatives. Thus, this study developed an integrated assessment model of OSC-DfMA consisting of the OSC-DfMA production suitability assessment model and the OSC-DfMA production efficiency assessment model to support decision-making for selecting the optimal design alternative of an OSC project. In this study, the scope of the main research was limited to precast concrete (PC)-based OSC projects. Firstly, we developed an OSC-DfMA production suitability assessment model to review whether design drafts are suitable in the OSC production environment by applying checklist and matrix techniques. Secondly, we developed an OSC-DfMA production efficiency assessment model to select an optimal alternative in terms of production efficiency among multiple design drafts. Thirdly, we conducted a case study to validate the usefulness of the OSC-DfMA assessment model developed in this study. Finally, we discuss the possibility of using AI technology to consider the facility capacity and resource constraints during the production of OSC building components. The study results are of practical value in providing the basis for expanding the applicability of DfMA by proposing a DfMA assessment model suitable for OSC contexts.

Suitability analysis for implementing wind and solar farms based AHP method: Case study in Inner Mongolia, China

Abstract. As important green energy, wind power and photovoltaic power have great development prospects. The suitability evaluation of wind and solar power plants is a popular research field, which is related to the sustainable and healthy development of wind and solar power generation. In this paper, based on multiple dimensions such as land types, climatic conditions, topographic features and policy environment, we selected 10 indicators and combined (analytical hierarchy process) AHP method to build a suitability assessment model for evaluating the suitability of solar and wind power in Inner Mongolia, China. The findings revealed that, Inner Mongolia has a great potential to generate wind and solar electricity, for wind power, the category of ‘excellent’ regions covers 83855 km2 and represents 7.10% of the total surface area; for solar power, 7.66% (nearly 90420 km2) are classified as ‘excellent’. The suitability of both solar and wind energy in the western region is considered to have the most suitable development region, parts of Alxa League and Bayannur City have great potential for combined wind and solar power generation. The research results could provide important technical and data support for capacity evaluation and power station location decision.

GIS-based land suitability analysis for the optimal location of integrated multi-trophic aquaponic systems

Aquaponics has witnessed global proliferation and a notable enhancement in sustainability in recent years. Consequently, it assumes paramount importance to delineate optimal locations for its implementation, in fact, the success of an aquaponic facility also depends on its geographical placement, necessitating consideration of many variables encompassing natural resources, socioeconomic factors, infrastructural availability and environmental constraints, whether natural or artificial. This paper focuses on the definition and test in the Emilia-Romagna region (Italy) of a GIS-based multi-criteria land suitability assessment model aimed at allowing the diffusion and environmental integration of innovative integrated multi-trophic aquaponic systems. The process has been implemented with a Weighted Linear Combination (WLC) model, where decisions and criteria were selected via a participatory mechanism involving experts in various fields. The region has been subdivided into 50 × 50 m grid cells, with each grid cell being associated with a value ranging from 0 to 8. In this context, a rating of 0 means unsuitability, while a rating of 1 denotes minimal suitability, and the highest rating of 8 designates maximal suitability. Notably, a substantial portion of the surveyed territory has been found to be completely unsuitable for the establishment of aquaponic facilities. More than 86.4% of the remaining suitable areas were rated 6, 7, or 8, affirming the overall favourability of the Emilia-Romagna region for aquaponic installations. Finally, the veracity and robustness of the results have been tested through a one-at-a-time sensitivity analysis, that has proven the appropriateness of the proposed model.

Structural Insights into the Activation of Human Aryl Hydrocarbon Receptor by the Environmental Contaminant Benzo[a]pyrene and Structurally Related Compounds

The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor belonging to the bHLH/PAS protein family and responding to hundreds of natural and chemical substances. It is primarily involved in the defense against chemical insults and bacterial infections or in the adaptive immune response, but also in the development of pathological conditions ranging from inflammatory to neoplastic disorders. Despite its prominent roles in many (patho)physiological processes, the lack of high-resolution structural data has precluded for thirty years an in-depth understanding of the structural mechanisms underlying ligand-binding specificity, promiscuity and activation of AHR. We recently reported a cryogenic electron microscopy (cryo-EM) structure of human AHR bound to the natural ligand indirubin, the chaperone Hsp90 and the co-chaperone XAP2 that provided the first experimental visualization of its ligand-binding PAS-B domain. Here, we report a 2.75 Å resolution structure of the AHR complex bound to the environmental pollutant benzo[a]pyrene (B[a]P). The structure substantiates the existence of a bipartite PAS-B ligand-binding pocket with a geometrically constrained primary binding site controlling ligand binding specificity and affinity, and a secondary binding site contributing to the binding promiscuity of AHR. We also report a docking study of B[a]P congeners that validates the B[a]P-bound PAS-B structure as a suitable model for accurate computational ligand binding assessment. Finally, comparison of our agonist-bound complex with the recently reported structures of mouse and fruit fly AHR PAS-B in different activation states suggests a ligand-induced loop conformational change potentially involved in the regulation of AHR function.

Global ocean spatial suitability for macroalgae offshore cultivation and sinking

Macroalgae offshore cultivation and sinking is considered a potentially practical approach for ocean-based carbon dioxide removal. However, several considerations need to be resolved to assess the effectiveness and sustainability of this approach. Currently, several studies focus on the area required for climate-relevant carbon sequestration through macroalgae cultivation and sinking without considering realistic, global spatial limitations. This study uses a spatially-explicit suitability assessment model for optimised open-ocean afforestation and sinking site designation. By applying specific maritime, ecological and industrial constraints, two maps are produced: a) suitable areas for macroalgae offshore cultivation and sinking, and b) suitable areas for macroalgae sinking only (for sequestration purposes). These data provide a more realistic approach to quantifying the ocean surface (including the corresponding depths) required for macroalgae offshore cultivation and sinking within a spatially sustainable framework. The resulting maps estimate the respective suitability areas within the EEZs of the world countries. A total area suitable for macroalgae offshore cultivation and sinking is calculated at 10.8M km2, whereas sinking-only areas account for 32.8M km2 of the offshore ocean. The implications of spatial suitability patterns at global and national levels are discussed. We suggest that the concept of ‘grow nearshore, sink offshore’ should be explored as an alternative to offshore cultivation.

Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis

Extensive occurrence of rice sheath blight has been observed in China in recent years due to agricultural practices and climatic conditions, posing a serious threat to rice production. Assessing habitat suitability for rice sheath blight at a regional scale can provide important information for disease forecasting. In this context, the present study aims to propose a regional-scale habitat suitability evaluation method for rice sheath blight in Yangzhou city using multisource data, including remote sensing data, meteorological data, and disease survey data. By combining the epidemiological characteristics of the crop disease and the Relief-F algorithm, some habitat variables from key stages were selected. The maximum entropy (Maxent) and logistic regression models were adopted and compared in constructing the disease habitat suitability assessment model. The results from the Relief-F algorithm showed that some remote sensing variables in specific temporal phases are particularly crucial for evaluating disease habitat suitability, including the MODIS products of LAI (4–20 August), FPAR (9–25 June), NDVI (12–20 August), and LST (11–27 July). Based on these remote sensing variables and meteorological features, the Maxent model yielded better accuracy than the logistic regression model, with an area under the curve (AUC) value of 0.90, overall accuracy (OA) of 0.75, and a true skill statistics (TSS) value of 0.76. Indeed, the results of the habitat suitability assessment models were consistent with the actual distribution of the disease in the study area, suggesting promising predictive capability. Therefore, it is feasible to utilize remotely sensed and meteorological variables for assessing disease habitat suitability at a regional scale. The proposed method is expected to facilitate prevention and control practices for rice sheath blight disease.

A python system for regional landslide susceptibility assessment by integrating machine learning models and its application

Landslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, this study develops a Python system for automatic assessment of regional landslide susceptibility. The Python system implements landslide susceptibility assessment through three modules: geographic data processing, machine learning modeling and result evaluation analysis. For geographic data processing, ten landslide influencing factors can be used to construct an evaluation factor dataset and reclassify the thematic maps based on the frequency ratio method. Four built-in machine learning models (logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM) and extreme gradient boosting (XGBoost)) are integrated into the system to complete susceptibility modeling and calculation. Additionally, receiver operating characteristic (ROC) curves can be automatically generated to evaluate the accuracy. The system was then applied into Lantian County in Shaanxi Province as a demonstration example. The results show that the areas under the ROC curve (AUC) of the four models are 0.838 (LR)、0.882 (SVM)、0.809 (MLP) and 0.812 (XGBoost), respectively, indicating that the SVM model was the most suitable model for landslide susceptibility assessment in Lantian County in the Loess Plateau of China. The system has now been made open source on Github, which can effectively improve the efficiency of regional landslide susceptibility assessment, especially provide tools for data processing and modeling for non-professionals.

Research on a Multi-Species Combined Habitat Suitability Assessment Method for Various Fish Species

To reveal the evolution of habitat distribution for multiple fish species in the lower reaches of the Gongzui Hydropower Station, this study conducted a catch survey to determine the target species of the reach. Based on their suitability curves, a combined suitability assessment model for multiple fish species was constructed. The reliability of the model was verified by combining acoustic observations of flow fields and fish distribution in specific flow conditions. A two-dimensional hydrodynamic model was coupled to quantitatively analyze the distribution characteristics of fish habitat patches under different flow conditions. The results indicate that the correlation coefficient between the multi-species comprehensive suitability index and the number of fish is 0.676, which indicates that the model can better evaluate the distribution of multiple fish habitats in the study river reach; the weighted usable area (WUA) decreased as the discharge increased; from low flow condition (<800 m3/s) to high flow condition (>2000 m3/s), the patch area of suitable habitat decreased from 11,424 m2 to 1268 m2, and the connectivity between patches also showed a downward trend; and the habitat shifted to the near-shore area of the downstream wider and shallower section, which was highly correlated with the migration process of low-depth and low-velocity areas. The model proposed in this study can establish a rapid response between the suitable habitat distribution of multiple fish species and discharge conditions, which can provide a research method for quantitative evaluation of multi-species habitats in river, and make a significant contribution to the sustainable development of riverine fisheries resources and river water ecology.

A novel knowledge management method about integrated grounded theory for performance assessment of green building construction engineering

ABSTRACT The green building construction is an important part of the green building, and setting up a suitable assessment model about green building construction engineering has great significance. This paper combined green construction and project management knowledge for performance assessment of green building construction engineering and used integrated method of grounded theory and G2 (G2 empowerment method) entropy method as a knowledge management strategy to build the performance assessment index set. Firstly, based on the existing literature and semi-structured interview data, grounded theory coding technology is used to identify the influencing factors of green construction performance to determine the assessment index set and corresponding assessment standards and formed the assessment index system. Then, the subjective and objective weighting methods are combined, and the G2 entropy method is used to carry out comprehensive weighting. This method has the advantages of comprehensive weighting and avoids the problem of how to allocate the proportion of subjective and objective weight in traditional comprehensive weighting. Finally, a novel knowledge management comprehensive method of performance assessment about green building construction engineering was presented. The detailed methods and steps presented in this paper can provide reference for performance assessment and knowledge management of green building construction engineering.

Modeling of natural gas self-diffusion in the micro-pores of organic-rich shales coupling sorption and geomechanical effects

A significant amount of the natural gas in shale formations is contained in the micro- and mesopores as dissolved (absorbed) phase and on the surfaces of associated microcracks as (adsorbed) phase. The transport of natural gas in such confined spaces is primarily governed by self-diffusion as could be deduced from Knudsen number. Self-diffusion is governed by the pressure and the space confinement. In this study, realistic kerogen structures possessing both tortuous micropores and larger microcracks were formed and used to assess self-diffusion behavior during the depletion of shale reservoirs through some comprehensive molecular simulation workflow. Analysis of the transport modes revealed transition self-diffusion as the primary transport mechanism in these micropores. The sorption behavior and the mechanical properties were analyzed and incorporated to derive a transition diffusion model that is sensitive to changes in the pore pressure and the stress field. The proposed model was compared and validated against similar work in the literature. The results showed that during a typical production span, a pressure drop influences the sorption profile, the net overburden stress on the pores, and the mean free path, altering the magnitude of self-diffusivity. The calibrated pore scale model produced decent predictive ability with a relative error of 2.5–16%. The implications of structure tortuosity, sorption profile, and pore pressure on the effective diffusion coefficient and gas desorption are discussed in depth. This work provides a novel methodology for studying the effect of coupled multiphysics processes on methane transport in a realistic kerogen geometry, which could be used to calibrate a suitable pore scale model for upscaled reservoir simulation applications and accurate assessment of reservoir dynamics and ultimate recovery.

Land Suitability Assessment for Cotton Cultivation - A Case Study of Kumkurgan District, Uzbekistan

Optimal land allocation for cotton cultivation requires more expert knowledge since we face increasing land degradation and climate change impact in Uzbekistan over the last years. In addition, because of not taking into account important agro-ecological conditions, crop requirements and crop rotation systems, land productivity is declining year by year. But, how can decision making on land allocation for cotton cultivation be supported? Here, a land suitability assessment models which allow to integrate and weight different spatial data (i.e., criteria) in a Geographic Information System (GIS) can be considered a promising tool. In this research, a land suitability assessment model was implemented on one of the arid areas of Uzbekistan (Kumkurgan district of Surkhandarya region) to identify and classify the suitability of lands for cotton cultivation. In terms of classifying land suitability for cotton cultivation, the Suitability Modeler functionality of ArcGIS Pro 2.7 was utilized. Overall, nine input variables related to important criteria of cotton cultivation such as soil texture, soil salinity, irrigation and collector-drainage networks, groundwater level/ mineralization, slope, current land use and temperature were used for a complex analysis of the area. After combining the data, the result was classified into five suitability classes based on the FAO land suitability classification method. The result delivered show that for Kumkurgan district the following: highly suitable (1.8% of the area; 36.2 km2), moderately suitable (11.5% of the area; 230.5 km2), marginally suitable (7.25% of the area; 145.0 km2), currently not suitable (24.3% of the area; 485.8 km2) and permanently not suitable (55.09% 55.09% of the area; 1101.1 km2. Irrigated agricultural land parcel data with vector/shapefile which includes five years of cropping sequences were used for crop rotation system of 2:1 (cotton: wheat/other) to identify exact land parcels for a cotton seeding in the season of 2020-2021 and validating the results. As a result, about 78.5 km2 of the agricultural area was assessed as very suitable for cotton for the 2020-2021 season. Validation revealed an accuracy of the model at 78% with the current state of cotton fields.

Effects of Vitamin E and Selenium-yeast on Cognitive Performance in Pregnant Dams Using Percentage Alternation and Discrimination Index Determinants

Introduction: Oxidative stress has been a hallmark in cognitive disorders; there is a need to mitigate it and a suitable model for cognitive assessment in rodents. This study investigates the effects of Vitamin E and Selenium yeast on the cognitive performance of pregnant dams using percentage alternation and discrimination index determinants. Materials and Methods: Thirty-five Wistar rats were used for this study and grouped into seven (n=5); group 1 (non-pregnant), group 2 (pregnant), group 3 (pregnant + received vitamin E; 100 mg/kg/day) group 4 (pregnant + received selenium-yeast; 0.4 mg/kg/day), group 5 (pregnant + received yeast; 50 mg/kg/day) group 6 (pregnant + received selenium; 0.25 mg/kg/day) and group 7 (pregnant + received vitamin E; 100 mg/kg/day + selenium-yeast; 0.4 mg/kg/day). Discrimination index was evaluated using novel object recognition, apparatus, while percentage alternation was evaluated using Y-maze on day 19. Results: Percentage alternation increased in group 1 and antioxidants treated groups respectively compared to group 2 (p˂0.05). Discrimination index significantly increased in group 1 and antioxidants treated groups compared to group 2 (p˂0.05). Conclusion: The percentage alternation and discrimination index show promising results in the cognitive assessment of pregnant dams. Antioxidant molecules such as vitamin E and selenium-yeast potentiate the indices of cognitive improvement.

Combination of fuzzy-AHP and GIS techniques in land suitability assessment for wheat (Triticum aestivum) cultivation

Land suitability classification is a useful management practice to ensure planned and sustainable use of agricultural lands according to their potentials. The main purposes of this study were to analyze land suitability for bread wheat (Triticum aestivum) cultivation and generate a land suitability map for wheat by integrating the analytical hierarchy (AHP)-fuzzy algorithm with the Geographical Information System (GIS) in the Tozanlı sub-basin located in the upper part of Yeşilırmak Basin, Turkey. Topographic (elevation, slope, aspect) characteristics of the basin and some of physical and chemical properties of soils (texture, pH, electrical conductivity, lime, organic matter, and soil depth) were used as criteria in determining the suitability classes. Ninety-two disturbed soil samples were collected from 0 to 20 cm depth in October 2017 using random sampling method. Weighted overlay spatial analysis in GIS was used to combine different thematic layers to identify areas suitable for wheat production. The fuzzy-AHP suitability assessment model was adapted to determine the weights for topographic and soil properties. The highest specific weights were obtained for soil depth (0.232) and elevation (0.218), while the lowest weight was calculated for aspect (0.042). Highly, moderately, and marginally suitable lands for wheat cultivation cover 2.63, 9.85 and 32.59% of the study area, respectively. In addition, the results indicated that 54.92% of the total area is permanently unsuitable for wheat cultivation. The results revealed that integration of AHP-fuzzy algorithm and GIS techniques is a useful method for accurate evaluation of land suitability in planning for specific crop production and decreasing the negative environmental impacts of agricultural practices.

A study of the suitability assessment of groundwater recharge by water supply in the Yongding River

Due to the effect of climate changes and human activities, groundwater over-exploitation and aquifer depletion have become global issues of concern. Artificial recharge of groundwater through a river has been paid more attention among a variety of artificial intervention measures. It is found that not all rivers or river reaches are suitable for artificial recharge of groundwater owing to the limitations of recharge potential by infiltration and storage capacity of the vadose zone. At present, there is still a lack of widely accepted suitability assessment methods. In this study, the Yongding River in the Beijing Plain section and the underlying aquifer are taken as the study area and the case study is carried out. Moreover, the measured data are used to verify the results. The suitability assessment model (LMBGITSC model) constructed by the index system method includes eight indexes: land use types of riverbed, sediment types of riverbed, channel width, channel topographic slope, medium types of the vadose zone, thickness of the vadose zone, specific yield of the vadose zone and horizontal permeability of the vadose zone. The results show that the suitability of groundwater recharge in the Yongding River changes from good degree to medium degree along the river flow direction (except the reaches carried out anti-seepage engineering), presenting a step-type evolution law. The proposed method is of good applicability and can also provide reference for similar regions.

Are the current IUCN category and CITES listing appropriate for the conservation and management of shortfin mako, Isurus oxyrinchus, in the North Pacific Ocean?

In 2018, the ISC conducted a benchmark stock assessment with a future projection and concluded that the latest stock status of North Pacific shortfin mako was healthy and that its stock abundance would gradually increase within 10 years. Then in 2019, this species was categorized in the North Pacific Ocean as Vulnerable on the IUCN Red List and listed in Appendix II of CITES. The inconsistent outcomes are a controversial issue and raise a fundamental question on why different international organizations had different views on the stock’s current and future declines. To clarify the reasons, this paper reviews the risk assessment conducted by the IUCN and the process of CITES listing, and then, based on the assessment results in 2018, conducts a future projection that incorporates uncertainties in the population trajectory. This projection indicates that the population level does not meet the criteria for the IUCN category of Vulnerable and listing in Appendix II. The results suggest that the IUCN’s simplified methodology of assessment is inappropriate for long-lived, sexually dimorphic species, and that the mechanism of CITES listing is inappropriate from the scientific view of stock assessment because all global stocks, each with a different status, have been treated as one stock. This paper therefore concludes that the IUCN category and CITES listing are both inappropriate for the conservation and management of North Pacific shortfin mako and that such efforts should be implemented by tuna RFMOs based on stock assessment and future projection results derived from a suitable assessment model.

Differential toxicity of abrin in human cell lines of different organ origin

Abrus precatorius is a highly toxic seed containing the poison abrin. Similar in properties to ricin, this toxin binds to ribosomes causing cessation of protein synthesis and cell death. With an estimated human lethal dose of 0.1–1 μg/kg, it has been the cause of fatalities due to accidental and intentional ingestion. In present study, we profiled seven human cell lines of different organ origin, for their sensitivity against abrin toxicity. These cell lines are, A549, COLO 205, HEK 293, HeLa, Hep G2, Jurkat, SH-SY5Y and derived from lung, intestine, kidney, cervix, liver, immune and nervous system respectively. MTT, NR, CVDE and LDH assays have been used to determine their response against abrin toxin. Among these cell lines A549 was the most sensitive cell line while Hep G2 was found least sensitive cell lines. Hep G2 cells are shown to have mitochondrial resistance and delayed generation of oxidative stress compared to A549 cells. Remarkable variation in sensitivity against abrin toxicity prompted the evaluation of Bcl2, Bax and downstream caspases in both cells. Difference in Bcl2 level has been shown to play important role in variable sensitivity. Findings of present study are helpful for selection of suitable cellular model for toxicity assessment and antidote screening.