Future Models Research Articles

Application of GIS and spatiotemporal analyses in viral infection modelling using multiple datasets – A case study on the SARS-CoV-2 epidemic

Introduction/objectiveViral and infectious diseases such as COVID-19 continue to pose a significant public health threat. In order to create an early warning system for new pandemics or emerging versions of the virus, it is imperative to study its epidemiology. In this study, we created a geospatial model to predict the weekly contagion and lethality rates of COVID-19 in Ireland. MethodsMore than forty parameters including atmospheric pollutants, metrological variables, sociodemographic factors, and lockdown phases were introduced as input variables to the model. The significant parameters in predicting the number of new cases and the death toll were identified. QGIS software was employed to process input data, and a principal component regression (PCR) model was developed using the statistical add-on XLSTAT. Results and conclusionsThe developed models were able to predict more than half of the variations in contagion and lethality rates. This indicates that the proposed model can serve to help prediction systems for the identification of future high-risk conditions. Nevertheless, there are additional parameters that could be included in future models, such as the number of deaths in care homes, the percentage of contagion and mortality among health workers, and the degree of compliance with social distancing.

Research on the innovation of the meta-universe vocational talent training model from the perspective of resource dependence theory

With the proposal of “accelerating digital development and building digital China”, China is doing its best to lay out the transformation and upgrading of education digitization and intelligence. The rapid development of frontier technology has promoted the change of industry-education integration in vocational education. Combining with the background of the meta-universe era, we introduce resource dependency theory to analyze the relationship between society, universities, and government in talent training mode. The frontier technologies such as virtual reality, artificial intelligence, big data, digital twin, etc. are used to explore the integration model and opportunities of vocational education industry-education integration, while innovative descriptions and creative imaginations are made for the future talent cultivation model of metaverse vocational education industry-education integration. Based on the resource dependency theory, the relationship between the metaverse and the integration of industry and education in vocational education is revealed based on “how to cultivate cross-border talents in the metaverse”. There are more possibilities for classroom teaching in vocational education supported by new equipment and cutting-edge technology, which is important for innovative ways of talent cultivation, exploring new modes of talent cultivation, and establishing strategic layouts of talent cultivation.

The long-term survival and climatic distribution of mixotrophic and mycoheterotrophic orchids

Orchid germination depends on their fungal partner; however, there are no broadscale analyses evaluating the actual overlap between orchids and their mycorrhizal symbionts. The aim of this research was to evaluate the importance of mycorrhizal fungi for the occurrence of two species of orchids using ecological niche modeling (ENM). Two sets of future orchid distribution models were created – the first one was based on bioclimatic data only, and the second one included information about the distribution of fungal symbionts. The jackknife test indicated that for both mixotrophic and mycoheterotrophic orchids, the presence of symbiotic fungi is crucial for their occurrence, and ENM analyses revealed that both orchids face habitat loss as a result of predicted changes in climate. In the case of the mixotrophic orchid, the presence of symbiotic fungi can compensate for unfavorable climatic conditions. Problems and limitations in modeling the distributions of species are discussed in terms of the symbiotic relationship.

ScarPath: Standardization of delivering patient-centered integrated scar care.

A pathological scar is a badly healing scar that is painful, itchy, red, thick and causes disability, diminished quality of life and socio-economic burden. Oscare, an aftercare and research center in Antwerp, Belgium, has built up a tradition in late stage translational research in assessment and treatment of pathological scars post burn, post trauma and post-surgery.
 Adequate patient-oriented assessment of physical scar features is crucial in the clinical evaluation and follow-up of scars, in defining scar treatment options for the patient, and for medico-legal reasons (e.g., for reimbursement of treatment and proof of disability). Moreover, scar assessment is an essential component for scientific research focused on evaluating newly developed wound or scar treatment modalities. Patient perception is evaluated with questionnaires, patient reported outcome measures to inform individual patient and scar care. Objective measures should always be combined with PROMs to get a full scope of on the impact and disability. Recent discussions with stakeholders have shown that the time consuming aspect of data collection is one of the important hurdles in new developments of scar research and treatment and is considered as a barrier to integrated care. Moreover, no standardized assessment protocols are available within this field of research.
 We address this gap by introducing a recently developed Electronic Data Capturing (EDC) platform ‘ScarPath’. A user friendly tool for scar assessment and holistic follow-up which will lead to a more standardized method of scar evaluation and enables a better treatment outcome, patient participation and empowerment in assessing evolution of scars during treatment. Moreover, ScarPath creates possibilities for testing new scar treatments in large pragmatic trials by offering a simple and patient oriented scar evaluation tool. In the future mathematical machine learning models based upon the automated data collection will lead to predictions in terms of scar evolution and recommendations for therapy.
 Oscare created ScarPath in collaboration with the University of Antwerp. The content was established in joined agreement and based on the clinical and research experience in our aftercare centre and on both literature evidence and empirical studies. In 2019 ScarPath was enrolled in the workflow in our aftercare center and in 2020 a HORIZON2020 – Digital Health Europe Twinning project was completed. This project enhanced knowledge exchange and training between Oscare and a French rehabilitation center.
 The ScarPath project will benefit patients suffering from scars, a common comorbidity in severe burns and often developed after trauma or surgery. Although we focus on researchers and clinicians to implement ScarPath in their standard clinical trials and big data collection, patients are key partners and the main focus. The results will be beneficial for the scar patient, allowing individualized and optimal scar care. ScarPath is able to assist the patient in the follow-up across multiple professionals and to process care data for multiple patients.

An Integrated Computational Framework for the Neurobiology of Memory Based on the ACT-R Declarative Memory System

Memory is a complex process that spans multiple time-scales and stages, and, as expected, involves multiple brain regions. Traditionally, computational models of memory are either too abstract (Shiffrin & Steyvers, 1997) to be meaningfully connected to a biological substrate, or, when explicitly connected, are narrowly focused on one specific region and process (Blum & Abbott, 1996; Weber et al., 2017). By contrast, a comprehensive model of memory with a plausible neural interpretation would be extremely valuable to drive further research in memory function and dysfunction. In this paper, we attempt to fill in this gap by providing a detailed biological analysis of ACT-R’s declarative memory system. This system, developed over four decades, has evolved into a consistent framework that describes how memories are formed, retrieved, forgotten, mistaken, and merged. Building on existing mappings between some components and their biological counterpart, as well as the existing literature, this paper provides a comprehensive view of how the framework’s various computations map onto different brain regions, their network dynamics and functional connectivity, and biological structure. We also show that these mappings provide further insights and explanations for puzzling findings in the memory disorders literature. Finally, we outline the remaining gaps (such as the transition from episodic to semantic memory) and how they could be addressed by future research and modeling efforts.

Multivariate lesion symptom mapping for predicting trajectories of recovery from aphasia.

Individuals with post-stroke aphasia tend to recover their language to some extent; however, it remains challenging to reliably predict the nature and extent of recovery that will occur in the long term. The aim of this study was to quantitatively predict language outcomes in the first year of recovery from aphasia across multiple domains of language and at multiple timepoints post-stroke. We recruited 217 patients with aphasia following acute left hemisphere ischaemic or haemorrhagic stroke and evaluated their speech and language function using the Quick Aphasia Battery acutely and then acquired longitudinal follow-up data at up to three timepoints post-stroke: 1 month (n = 102), 3 months (n = 98) and 1 year (n = 74). We used support vector regression to predict language outcomes at each timepoint using acute clinical imaging data, demographic variables and initial aphasia severity as input. We found that ∼60% of the variance in long-term (1 year) aphasia severity could be predicted using these models, with detailed information about lesion location importantly contributing to these predictions. Predictions at the 1- and 3-month timepoints were somewhat less accurate based on lesion location alone, but reached comparable accuracy to predictions at the 1-year timepoint when initial aphasia severity was included in the models. Specific subdomains of language besides overall severity were predicted with varying but often similar degrees of accuracy. Our findings demonstrate the feasibility of using support vector regression models with leave-one-out cross-validation to make personalized predictions about long-term recovery from aphasia and provide a valuable neuroanatomical baseline upon which to build future models incorporating information beyond neuroanatomical and demographic predictors.

A Review of the Option Pricing Model and Further Development

The Black Scholes model and binomial tree model have been the main research objects of scholars in the past fifty years. This article summarizes the optimization process of these two classic option pricing models to understand the different directions of optimizing the models and to provide ideas for future model improvement. Improvements from scholars to the Black-Scholes model mainly focus on the basic model and the relevant variables involved in option pricing, while the optimization of the binomial tree model focuses on the reduction of pricing errors as well as the improvement of model fitting speed. Empirical studies of option pricing models have shown that improved models while enhancing the accuracy of pricing in various financial markets, unavoidably increase computational complexity and reduce efficiency. This article also demonstrates the future integration of financial derivatives pricing models in multiple fields by describing the application of real options in the agriculture, technology, and biology industries.

Hybrid stacking ensemble algorithm and simulated annealing optimization for stability evaluation of underground entry-type excavations

The stability of underground entry-type excavations (UETEs) is of paramount importance for ensuring the safety of mining operations. As more engineering cases are accumulated, machine learning (ML) has demonstrated great potential for the stability evaluation of UETEs. In this study, a hybrid stacking ensemble method aggregating support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), multilayer perceptron neural network (MLPNN) and extreme gradient boosting (XGBoost) algorithms was proposed to assess the stability of UETEs. Firstly, a total of 399 historical cases with two indicators were collected from seven mines. Subsequently, to pursue better evaluation performance, the hyperparameters of base learners (SVM, KNN, DT, RF, MLPNN and XGBoost) and meta learner (MLPNN) were tuned by combining a five-fold cross validation (CV) and simulated annealing (SA) approach. Based on the optimal hyperparameters configuration, the stacking ensemble models were constructed using the training set (75% of the data). Finally, the performance of the proposed approach was evaluated by two global metrics (accuracy and Cohen’s Kappa) and three within-class metrics (macro average of the precision, recall and F1-score) on the test set (25% of the data). In addition, the evaluation results were compared with six base learners optimized by SA. The hybrid stacking ensemble algorithm achieved better comprehensive performance with the accuracy, Kappa coefficient, macro average of the precision, recall and F1-score were 0.92, 0.851, 0.885, 0.88 and 0.883, respectively. The rock mass rating (RMR) had the most important influence on evaluation results. Moreover, the critical span graph (CSG) was updated based on the proposed model, representing a significant improvement compared with the previous studies. This study can provide valuable guidance for stability analysis and risk management of UETEs. However, it is necessary to consider more indicators and collect more extensive and balanced dataset to validate the model in future.

The thrombin generation capability of the Chacma baboon (Papio ursinus): implications for haemostatic disease models

Baboon models are often used to investigate haemostatic diseases, such as acquired thrombotic thrombocytopenic purpura or bacterial sepsis-induced disseminated intravascular coagulation, and their potential treatment with novel drugs. Thrombin generation is vital for these models, and an important potential therapeutic target. We investigated the thrombin generation profile of the Chacma baboon (Papio ursinus – a common pre-clinical model) including the effects of sex and ABO blood group. Thrombin generation curves, lag times, peak heights, times-to-peak, velocity indexes and Endogenous Thrombin Potentials (ETPs) of 40 adult Chacma baboons were assessed and compared with normal human plasma, using a low concentration of tissue factor (1 pM) and phospholipids. Reference intervals were calculated, and results compared between O and non-O ABO blood groups, and between males and females. Lag times of all baboons fell within the human reference interval. Most animals (n = 32; 80%) had times-to-peak above, and velocity indexes and peak heights markedly below (n = 27; 68%) the human range. However, 97.5% of baboons had an ETP above the human reference interval, indicating greater overall thrombin generation. ABO blood group had no effect, but males (n = 14; 35%) had less potent thrombin generation than females (n = 26; 65%), with significantly longer lag times (p = 0.0475), lower peak thrombin concentrations (p = 0.0203), and lower ETPs (p = 0.0238). Chacma baboons have greater overall endogenous thrombin generation potentials than humans, which is even more prominent in females. This should be considered when designing future baboon model experiments involving the haemostatic system, or when evaluating novel therapies in these animals.

Multiscale insights into postnatal aortic development.

Despite its vital importance for establishing proper cardiovascular function, the process through which the vasculature develops and matures postnatally remains poorly understood. From a clinical perspective, an ability to mechanistically model the developmental time course in arteries and veins, as well as to predict how various pathologies and therapeutic interventions alter the affected vessels, promises to improve treatment strategies and long-term clinical outcomes, particularly in pediatric patients suffering from congenital heart defects. In the present study, we conducted a multiscale investigation into the postnatal development of the murine thoracic aorta, examining key allometric relations as well as relationships between in vivo mechanical stresses, collagen and elastin expression, and the gradual accumulation of load-bearing constituents within the aortic wall. Our findings suggest that the production of fibrillar collagens in the developing aorta associates strongly with the ratio of circumferential stresses between systole and diastole, hence emphasizing the importance of a pulsatile mechanobiological stimulus. Moreover, rates of collagen turnover and elastic fiber compaction can be inferred directly by synthesizing transcriptional data and quantitative histological measurements of evolving collagen and elastin content. Consistent with previous studies, we also observed that wall shear stresses acting on the aorta are similar at birth and in maturity, supporting the hypothesis that at least some stress targets are established early in development and maintained thereafter, thus providing a possible homeostatic basis to guide future experiments and inform future predictive modeling.

Sim2Real: Generative AI to Enhance Photorealism through Domain Transfer with GAN and Seven-Chanel-360°-Paired-Images Dataset.

This work aims at providing a solution to data scarcity by allowing end users to generate new images while carefully controlling building shapes and environments. While Generative Adversarial Networks (GANs) are the most common network type for image generation tasks, recent studies have only focused on RGB-to-RGB domain transfer tasks. This study utilises a state-of-the-art GAN network for domain transfer that effectively transforms a multi-channel image from a 3D scene into a photorealistic image. It relies on a custom dataset that pairs 360° images from a simulated domain with corresponding 360° street views. The simulated domain includes depth, segmentation map, and surface normal (stored in seven-channel images), while the target domain is composed of photos from Paris. Samples come in pairs thanks to careful virtual camera positioning. To enhance the simulated images into photorealistic views, the generator is designed to preserve semantic information throughout the layers. The study concludes with photorealistic-generated samples from the city of Paris, along with strategies to further refine model performance. The output samples are realistic enough to be used to train and improve future AI models.

A comprehensive data driven study of mechanical properties of concrete with waste-based aggregates: Plastic, rubber, slag, glass and concrete

In an attempt to reduce the consumption of natural resources, much research has focused on the development of mix designs with waste aggregates. While this research has demonstrated feasibility, investigations into the broader mechanical properties of concrete with various types of waste aggregates have been limited. To this end, this paper studies a wide variety of concrete properties with plastic, rubber, slag, glass and recycled concrete aggregates by analysing a large database of 5321 concrete mixes. The effects of waste aggregate type and replacement ratio on mechanical properties, workability and durability are investigated and relationships between the mechanical properties of concrete with various types of waste aggregates are quantified and compared to the existing design codes. The findings from this study can be used to assist in identifying optimum waste aggregate type and replacement ratio based on intended use and the databases compiled will assist in future data-driven modelling approaches.

Assessing the past and future dynamics of the Asian summer monsoon: Insights from palaeomonsoon synthesis and CMIP6 data

The Asian summer monsoon is one of the active synoptic scale weather phenomena, and has significant socioeconomic implications. A vast population relies on the associated precipitation, mostly dominating the agricultural practices of the region. Therefore, it is essential to assess past behavior to understand the present, including future projections. We used palaeomonsoon precipitation synthesis and Coupled Model Intercomparison Project Phase 6 (CMIP6) data to interactively show the dynamics and changes in the summer monsoon for the Asian region throughout the past millennium behavior to understand the present and future projections. In this study, we precisely analyzed and quantified the dynamics of summer precipitation variation throughout the last millennium (LM; 850–1849 CE) at an annual resolution, in which the major climatic events were the Medieval Warm Period (MWP; 900–1300 CE) and Little Ice Age (LIA; 1500–1850 CE). We also analyzed the historical or base climate (HC; 1850–2014 CE) and future monsoons (FM; 2015–2100 CE) using CMIP6 SSP2–4.5 and SSP5–8.5, to project the summer monsoon for Asia and the Indian subcontinent. The findings are encouraging, showing slightly increased precipitation during the MCA and low precipitation during the LIA in Asia. Moreover, the average summer monsoon daily rainfall remained 6.398 ± 0.634 and 6.310 ± 0.649 mm/d for the MCA and LIA, respectively, indicating a relatively slight variation in the summer monsoon precipitation during these climatic phases. In addition, for the twenty-first century, the CIMP6 projection shows increased summer monsoon precipitation over Asia, particularly in the northeast region. Further, the CMIP6 projections for SSP2–4.5 shows 6.457 ± 0.658 mm/d, and for SSP5–8.5 is 6.686 ± 0.837 mm/d for the twenty-first century. Furthermore, the results of Empirical Orthogonal Functions (EOFs) analysis suggest that the monsoon system may become more intense in some regions, whereas other regions may experience reduced precipitation in the Asia-Pacific region, with a regionally heterogeneous rise in heavy rainfall and high moisture throughout most of Asia. Orography, evaporation, moisture content, and circulation all affect the severity of precipitation in addition to fine-scale surface moisture feedback. The findings show that it is essential to consider both the past and the future to accurately estimate local and regional-scale susceptibility to climate change. Moreover, the synthesis of past data and analysis of future projections of the monsoon will provide a basis for reducing the unpredictability of future climate models.

“Prepared to Face the Unexpected”: Dynamic Enterprises in Italy as a Model of Resilience

Businesses that demonstrate organizational flexibility, characterized by dynamic problem-solving skills, in-house production processes, and a robust human capital, appear to be successful and resilient in times of crisis. Drawing on my fieldwork in medium-sized manufacturing companies in Emilia, Northern Italy, from June to November 2021, I investigate ideas and characteristics that shed light on good practices, key ingredients of the success of such companies, and possible future models of resilience. I employ participant observation and provide evidence taken from conversations or episodes observed, especially in one of the companies. Although greater structuring is often sought and considered the resolution of many existing problems, non-structuring is, at the same time, convenient in many circumstances. People are essential components in this dynamic organizational model, and their commitment in the organization goes far beyond contractual obligations. Their motivation stems from personal satisfaction as well as from a sense of responsibility and strong personal commitment.

Empower communities: Integrating scientific and stakeholder-based knowledge to simulate future urban growth scenarios using advanced technology for built-form simulation

This study investigates the possibility of equipping communities with the knowledge, skills, and resources necessary to speed up the implementation of sustainable practices. The goal is to model urbanization by soliciting feedback from different stakeholders within the community. The main objective of this study is to determine how community engagement influences the rate at which sustainability efforts progress in simulation. The research uses a participatory methodology, including stakeholder interviews, meetings, and conversations. The research was conducted in Kurunegala and consisted of three primary phases: framework development, effectiveness testing, and lesson documentation. The framework entailed establishing the computational region, designating predictors, evaluating sub-models, constructing scenarios, and evaluating results. Using CA-Markov cellular automata, agent-based modeling (Net-Logo), the Cellular Automata Python model, and MOLUSCE-QGIS, the study modified the FUTURES model to incorporate stakeholder-based knowledge into urban growth modeling. This study emphasizes the need for bottom-up strategies to achieve long-term sustainability by recognizing the worth of community involvement. When stakeholders in a community are given the opportunity they need to succeed, they are more likely to develop into advocates of transformation and supporters of sustainability initiatives.

Assessment of on-site and remote cardiac rehabilitation in Romania

: Introduction: It is well known that cardiovascular diseases are the leading cause of death worldwide. In Romania in 2015, cardiovascular diseases led to 153.953 deaths, representing 59.3% of deaths from all causes. Most people in Romania today have an unhealthy lifestyle, characterised mainly by a poor nutritious diet, low physical activity and a lack of medical screening. Purpose: Considering these deficits of secondary prevention in the treatment line of the Romanian cardiovascular patient, we aimed through this study to assess the current access of the Romanian population to on-site and remote cardiac rehabilitation programs as well as their opinion regarding the telemedicine for future cardiovascular care modelling. Methods: It was a prospective observational study, which was carried out by means of an online questionnaire composed of 26 items. It was dispersed in the online environment, including Social Media platforms, where it was active for ten weeks. Results: Our online evaluation questionnaire had 1115 participants, with a minimum age of 18 years old, a preponderance of female sex and a predominantly urban background. The study participants were from all educational levels, from low to superior. We observed that most participants have an internet connection at home and use at least one form of technology daily, but most have never used a telemedicine service. Conclusions: In conclusion, the access of cardiovascular patients to on-site cardiac rehabilitation services is extremely low and for remote ones, it is almost non-existent. For a middle-income country like Romania with such a high incidence of cardiovascular disease, it is almost mandatory to provide publicly funded cardiac telerehabilitation services also for the future.

Transpiration rates from mature Eucalyptus grandis × E. nitens clonal hybrid and Pinus elliottii plantations near the Two Streams Research Catchment, South Africa

Abstract. Pine plantations are the dominant species currently planted within the South African commercial forestry industry. Improvements in bio-economy markets for dissolving wood pulp products have seen an expansion in fast-growing Eucalyptus plantations due to their higher productivity rates and better pulping properties than pine. This has raised concerns regarding the expansion of Eucalyptus plantations and how they will affect water resources as they have been reported to have higher water use (quantified using transpiration rates) than pine. We measured transpiration rates (mm yr−1), diameter at breast height (quantified as quadratic mean diameter, Dq, m) and leaf area index of an 8-year-old Eucalyptus grandis × Eucalyptus nitens clonal hybrid (GN) and a 20-year-old Pinus elliottii. Transpiration rates were measured for two consecutive hydrological years (2019/20 and 2020/21) using a heat ratio sap-flow method, calibrated against a lysimeter. In the 2019/20 year, annual transpiration for P. elliottii exceeded GN by 28 %, while for the 2020/21 hydrological year, there was no significant difference between the transpiration of the two species, despite a 17 % and 21 % greater leaf area index for P. elliottii than GN in 2019/20 and 2020/21 measurement years respectively. Quadratic mean diameter increments were statistically similar (p > 0.05) in 2019/20, whereas the 2020/21 year produced significant differences (p<0.05). Tree transpiration is known to be influenced by climatic variables; therefore, a random forest regression model was used to test the level of influence between tree transpiration and climatic parameters. The soil water content, solar radiation and vapour pressure deficit were found to highly influence transpiration, suggesting these variables can be used in future water-use modelling studies. The profile water content recharge was influenced by rainfall events. After rainfall and soil profile water recharge, there was a rapid depletion of soil water by the GN trees, while the soil profile was depleted more gradually at the P. elliottii site. As a result, trees at the GN site appeared to be water stressed (reduced stem diameters and transpiration), suggesting that there was limited access to alternative water source (such as groundwater). The study concluded that previous long-term paired catchment studies indicate that eucalypts use more water than pine; however, periods of soil water stress and reduced transpiration observed in this study must be accommodated in hydrological models. Long-term total soil water balance studies are recommended in the same region to understand the long-term impact of commercial plantations on water resources.

Product and process: New York’s Model Cities vest-pocket housing and rehabilitation programme

ABSTRACT In January 1966, US President Lyndon Johnson proposed the Model Cities programme to ‘improve the quality of urban life’ in the nation’s poorest areas through comprehensive action and citizen participation. That same month, John Lindsay became mayor of New York with a platform to create a more equitable city. Toward this end, Lindsay’s administration rejected earlier urban renewal approaches, prioritizing infill construction on vacant sites and reusing existing buildings, all while including local communities in the planning process. Eugenia Flatow spearheaded this ‘vest-pocket and rehabilitation programme’ as a ‘head start’ to future Model Cities funding. As she commissioned Raymond & May, Walter Thabit, Jonas Vizbaras, and Fisher/Jackson with housing plans for Brooklyn, the Bronx, and Harlem, she was acutely aware of the resulting tension between a desired democratic process and the timely delivery of the product. A close reading of archival materials reveals how these planners responded in very different ways to the prompt. The governmental programme had created a space of possibility for rethinking the relationship of product and process in planning through the specificity of housing design. The plans also highlighted the paradox in attempting to effect socio-economic change through housing supply, one that still resonates today.

Application of MaxEnt modeling to evaluate the climate change effects on the geographic distribution of Lippia javanica (Burm.f.) Spreng in Africa.

Lippia javanica is a typical indigenous plant species mostly found in the higher elevation or mountainous regions in southern, central, and eastern Africa. The ongoing utilization of the species for ethnobotanical applications and traditional uses, coupled with the changing climate, increases the risk of a potential reduction in its geographic distribution range in the region. Herein, we utilized the MaxEnt species distribution modelling to build the L. javanica distribution models in tropical and subtropical African regions for current and future climates. The MaxEnt models were calibrated and fitted using 286 occurrence records and six environmental variables. Temperatures, including temperature seasonality [Bio 4] and the maximum temperature of the warmest month [Bio 5], were observed to be the most significant determinants of L. javanica's distribution. The current projected range for L. javanica was estimated to be 2,118,457 km2. Future model predictions indicated that L. javanica may increase its geographic distribution in western areas of the continent and regions around the equator; however, much of the geographic range in southern Africa may shift southwards, causing the species to lose portions of the northern limits of the habitat range. These current findings can help increase the conservation of L. javanica and other species and combat localized species loss induced by climate change and human pressure. We also emphasize the importance of more investigations and enhanced surveillance of traditionally used plant species in regions that are acutely susceptible to climate change.

The Role of PIVKA-II as a Predictor of Early Hepatocellular Carcinoma Recurrence-Free Survival after Liver Transplantation in a Low Alpha-Fetoprotein Population.

AFP and the RETREAT score are currently used to predict HCC recurrence after LT. However, superior discriminating models are needed for low AFP populations. The aim of this study is to investigate the predictive value of PIVKA-II on recurrence-free survival after LT in a low AFP population and microvascular invasion on explant. A retrospective cohort study including all consecutive patients transplanted for HCC between 1989 and 2019 in the Erasmus MC University Medical Center in Rotterdam, the Netherlands, was used. AFP and PIVKA-II levels were determined in serum samples collected at the time of transplantation. Data on tumor load and microvascular invasion were retrieved from patients' records. The study cohort consisted of 121 patients, with HCC recurrence in 15 patients (12.4%). The median AFP was 7.7 ng/mL (4.4-20.2), and the median PIVKA-II was 72.0 mAU/mL (41.0-213.5). Patients with low AFP (≤8 ng/mL) and PIVKA-II (≤90 mAU/mL) had a 5-year recurrence-free survival of 100% compared to 85.7% in patients with low AFP and high PIVKA-II (p = 0.026). Regardless of the AFP level, patients within the Milan criteria (based on explant pathology) with a low PIVKA-II level had a 5-year recurrence-free survival of 100% compared to patients with a high PIVKA-II level of 81.1% (p = 0.002). In patients with microvascular invasion, the AUC for PIVKA-II was slightly better than the AUC for AFP (0.775 vs. 0.687). The dual model of PIVKA-II ≤ 90 mAU/mL with either AFP ≤ 8 ng/mL or with patients within the Milan criteria identifies patient groups which can be exempted from HCC surveillance after LT in a low AFP population. PIVKA-II may be a better predictor for explant microvascular invasion than AFP and could play a role in future models identifying LT candidates with the highest risk for HCC recurrence.