Model Assessment Research Articles

Modelling the social cost of carbon for Malaysia

The interest in the economics of climate change and the need to quantify a more accurate financial value of the damage caused by every additional metric tonne of greenhouse gas (GHG), known as the social cost of carbon (SCC), is growing. Governments and decision-makers have utilised SCC estimations for more than ten years in benefit-cost analysis, using insights from climate science, economics, demography, and other fields. Malaysia has committed to reduce 45% of its GHG emission intensity by Gross Domestic Product (GDP) by 2030 compared to the 2005 level and achieve net-zero as early as the year 2050. However, current estimations of SCC are high and may negatively impact the Malaysian economy and no longer align with the latest research. To achieve its objective of net-zero emissions, the government of Malaysia must capitalise on the country's potential as a carbon sink by increasing conservation efforts in protected areas and arresting deforestation. It will establish an ideal trajectory for the SCC that aligns with its environmental obligations while supporting sustainability in the face of climate change challenges. A price on GHG emissions can help mitigate and adapt to climate change. This study aims to determine the SCC in Malaysia, fill the gap in the downscaled Integrated Assessment Models (IAMs) and develop a new model called the Integrated Climate Model for Malaysia (ICLIM-2024R). The Nordhaus IAM model was downscaled using Malaysian data, including emissions from deforestation and an additional function for carbon sinks. With current policies, the SCC in the business-as-usual scenario (BAU) will be 64 USD/tCO2e in 2025 and 212 USD/tCO2e by 2050. The study recommended pathway is to increase the Totally Protected Area (TPA) of forests, which will yield a lower SCC of 23 USD/tCO2e in 2025 and 152 USD/tCO2e by 2050 compared to BAU, meeting commitments to the Convention of Biological Diversity (CBD) as well as the Paris Agreement. The Malaysian government should urgently leverage its carbon sink potential to set Malaysia's optimal SCC, achieve its net-zero goal, and grow the climate-friendly economy by marketing lower carbon-intensity products.Graphical

Modelling soil - Vegetation - Atmospheric interactions of radon products in a Belgian Scots pine forest site.

A soil-vegetation-atmospheric transfer (SVAT) model for radon and its progeny is presented to improve process-level understanding of the role of forests in taking-up radionuclides from soil radon outgassing. A dynamic system of differential equations couples soil, tree (Scots pine) and atmospheric processes, treating the trees as sources, sinks and conduits between the atmosphere and the soil. The model's compartments include a dual-layer soil column undergoing hydrological and solute transport, the tree system (comprising roots, wood, litter, and foliage) and the atmosphere, with physical processes governing the transfers of water and radon products between these compartments. A dose post-processor calculates dose rates to the trees from internal, external, and surface radiation exposures. The model parameterisation is based on measurement data from the Grote Nete Valley in the Belgian Campine region. The model results suggest that the tree intake of radon progeny is principally from the atmosphere, whereas radium is mainly taken-up from soil by the root uptake process, leading to an additional fraction of ingrown radon progeny in the tree by this route. It is also suggested that atmospheric uptake of radon is an essential mechanism when evaluating the tree uptake of 214Po, 214Pb and 218Po and subsequent decay products. The model also indicates a slow uptake of radionuclides by the tree roots, with timescales in the order of years, leading to different dose rates for young and mature trees. The importance of foliar surface deposition, leading to a dominance of surface doses to the tree needles, is also highlighted. These mechanisms, ignored in most assessment models, are necessary improvements for assessment tools dealing with the impact of radon and its progeny in forests, with regard to legacy sites with 226Ra contamination.

Utilizing alternative in vivo animal models for food safety and toxicity: A focus on thermal process contaminant acrylamide.

Rodent models have traditionally been used to assess the toxicity of food chemicals, but this approach is costly, time-consuming, and raises ethical concerns. Alternatively, non-mammalian models such as Drosophila melanogaster, Danio rerio, and Caenorhabditis elegans have been shown to be suitable for studying the toxicity of food hazards. Their advantages include low cost, short life cycles, adaptability to high-throughput screening, and adherence to the 3R principles of replacement, reduction, and refinement. These models have been extensively studied in the context of acrylamide toxicity, a common food contaminant. This article comprehensively reviews the biological characteristics of non-mammalian models, recent advances and challenges in acrylamide toxicity research using these models, and explores the potential of natural plant compounds in ameliorating acrylamide toxicity. The review aims to guide research using non-mammalian models for food safety assessment.

CREG1 attenuates intervertebral disc degeneration by alleviating nucleus pulposus cell pyroptosis via the PINK1/Parkin-related mitophagy pathway.

Intervertebral disc degeneration (IVDD) is a chronic degenerative disease with a complex pathophysiological mechanism. Increasing evidence suggests that the NOD-like receptor thermal protein domain associated protein 3 (NLRP3)-mediated pyroptosis of nucleus pulposus cells (NPCs) plays a crucial role in the pathological progression of IVDD. Pyroptosis is a novel form of programmed cell death characterized by the formation of plasma membrane pores by gasdermin family proteins, leading to cell swelling, membrane rupture, and the release of inflammatory cytokines, which trigger an inflammatory response. The close relationship between pyroptosis and mitophagy has been previously described in various diseases, but the crosstalk between pyroptosis and mitophagy in IVDD remains unexplored. Cellular repressor of E1A-stimulated genes 1 (CREG1) is a secreted glycoprotein involved in cell differentiation and homeostasis regulation and has been shown to promote lysosomal biogenesis and function. However, the potential role and underlying mechanisms of CREG1 in the progression of IVDD have not yet been reported. In this study, we first observed that CREG1 is downregulated following IVDD and that pyroptosis occurs. Furthermore, CREG1 knockdown inhibited NPC proliferation and exacerbated apoptosis and degeneration. Moreover, we confirmed that CREG1 knockdown induced NLRP3 activation while also leading to mitophagy inhibition and mitochondrial dysfunction in NPCs. CREG1 overexpression ameliorated LPS-induced mitophagy inhibition and mitochondrial dysfunction by promoting PINK1/Parkin-mediated mitophagy, thereby suppressing NLRP3 inflammasome activation. However, these protective effects were reversed by pretreatment with the mitophagy inhibitor cyclosporin A (CsA). In a rat model of IVDD, imaging and histological assessments revealed that CREG1 overexpression effectively alleviated the progression of IVDD. Additionally, CREG1 overexpression reduced the expression of NLRP3, caspase-1, and IL-1β while increasing the expression of collagen II, PINK1 and LC3, delaying the course of IVDD. Overall, this study highlights the importance of the interplay between CREG1-mediated regulation of mitophagy and pyroptosis in the pathogenesis of IVDD, identifying CREG1 as a promising therapeutic target for IVDD treatment.

Possibilistic modeling for fingerprint image quality assessment in Automatic Identification Systems

Possibilistic modeling for fingerprint image quality assessment in Automatic Identification Systems

A549 Alveolar Carcinoma Spheroids as a Cytotoxicity Platform for Carboxyl- and Amine-Polyethylene Glycol Gold Nanoparticles.

Gold nanoparticles (AuNPs) present with unique physicochemical features and potential for functionalization as anticancer agents. Three-dimensional spheroid models can be used to afford greater tissue representation due to their heterogeneous phenotype and complex molecular architecture. This study developed an A549 alveolar carcinoma spheroid model for cytotoxicity assessment and mechanistic evaluation of functionalized AuNPs. A549 spheroids were generated using an agarose micro-mold and were characterized (morphology, acid phosphatase activity, protein content) over 21 culturing days. The 72-h cytotoxicity of carboxyl-polyethylene glycol- (PCOOH-) and amine-polyethylene glycol- (PNH2-) functionalized AuNPs against Day 7 spheroids was assessed by determining spheroid morphology, acid phosphatase activity, protein content, caspase-3/7 activity, and cell cycle kinetics. Spheroids remained stable over the experimental period. Although the A549 spheroids' volume increased while remaining viable over the culturing period, structural integrity decreased from Day 14 onwards. The PCOOH-AuNPs lacked cytotoxicity at a maximum concentration of 1.2 × 1012 nanoparticles/mL with no prominent alteration to the cellular processes investigated, while the PNH2-AuNPs (at a maximum of 4.5 × 1012 nanoparticles/mL) displayed dose- and time-dependent cytotoxicity with associated loss of spheroid compactness, debris formation, DNA fragmentation, and a 75% reduction in acid phosphatase activity. Differentiation between cytotoxic and non-cytotoxic AuNPs was achieved, with preliminary elucidation of cytotoxicity endpoints. The PNH2-AuNPs promote cytotoxicity by modulating cellular kinetics while destabilizing the spheroid ultrastructure. The model serves as a proficient platform for more in-depth elucidation of NP cytotoxicity at the preclinical investigation phase.

Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region

Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region

A human digital twin approach for fatigue-aware task planning in human-robot collaborative assembly

Human-robot collaboration (HRC) has emerged as a pivotal paradigm in manufacturing, integrating the strengths of both human and robot capabilities. Neglecting human physical fatigue may adversely affect worker health and, in extreme cases, may lead to musculoskeletal disorders. However, human fatigue has rarely been considered for decision-making in HRC manufacturing systems. Integrating adaptive decision-making to optimise human fatigue in HRC manufacturing systems is crucial. Nonetheless, real-time perception and estimation of human fatigue and decision-making informed by human fatigue face considerable challenges. To address these challenges, this paper introduces a human digital twin method, a bidirectional communication system for physical fatigue assessment and reduction in human-robot collaborative assembly tasks. The methodology encompasses an IK-BiLSTM-AM-based surrogate model, which consists of inverse kinematics analysis (IK), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), for real-time muscle force estimation integrated with a muscle force-fatigue model for muscle fatigue assessment. An And-Or graph and optimisation model-based HRC task planner is also developed to alleviate physical fatigue via task allocation. The efficacy of this approach has been validated through proof-of-concept assembly experiments involving multiple subjects. The results show that the IK-BiLSTM-AM model achieves a minimum of 8% greater accuracy in muscle force estimation than the baseline methods. The 12-subject assessment results indicate that the task planner effectively reduces the physical fatigue of workers while performing collaborative assembly tasks.

Hypoxia-selective prodrug restrains tumor cells through triggering mitophagy and inducing apoptosis.

Hypoxia is a common feature of various solid tumors, which reduces the sensitivity of tumor cells to both radiotherapy and chemotherapy. However, hypoxia also presents an opportunity for tumor-selective therapy. The prodrug strategy, leveraging the hypoxic nature of the tumor microenvironment, shows significant potential for clinical application. Here we present CHD-1, a hypoxia-activated antitumor prodrug that activates in hypoxic environments, effectively inhibiting hypoxic tumor cells while exhibiting no toxicity to normoxic cells. CHD-1 impairs mitochondrial morphology and membrane potential of hypoxic tumor cells, further triggers excessive mitophagy and induces apoptosis. Moreover, prodrug CHD-1 significantly inhibits HeLa xenograft growth in vivo, and shows lower toxicity than parent molecule in an acute toxicity assessment in animal models. This study introduces a promising hypoxia-activated antitumor prodrug with strong potential for further development in hypoxic tumor therapy.

Fluted pumpkin waste potential as a green solid bioalkaline catalyst for neem seed oil biodiesel synthesis

Developing green routes for biodiesel synthesis is essential for sustaining environmental benignity. Thus, this present study investigated the development of a solid catalyst using fluted pumpkin (Telfairia occidentalis) pod husk as a bio-based raw material via high-temperature furnace heating, ranging from 300 – 1000 oC. The catalyst properties were investigated by conducting surface functional group, surface crystallinity, surface area, and pore distribution analyses. The efficacy of the synthesized catalyst was investigated using 28 randomized experiments carried out on transesterification of esterified neem oil, which established 12:1, 3.5 wt.%, and 60 min as optimal conditions for methanol/neem oil ratio, catalyst dosage, and reaction time, respectively, with a maximum neem seed oil methyl esters (NSOME) yield of 96.71% through response surface methodology. The statistical assessment of the developed model gave a correlation coefficient (R) of 0.9907 and a coefficient determination of 0.9816 (R2). The physicochemical properties of the NSOME agree with American and European standard limits, making it suitable for transportation and energy purposes. The catalyst could be deployed for three reaction cycles with high turnover frequencies. Hence, fluted pumpkin waste and neem oil are suitable as a green route for biodiesel synthesis.

Shear Strengthening of RC Beams with U-Wrapped FRCM Composites: State of the Art and Assessment of Available Analytical Models

Shear Strengthening of RC Beams with U-Wrapped FRCM Composites: State of the Art and Assessment of Available Analytical Models

Development and Assessment of an AI-Based Machine Learning Model for Predicting Urinary Continence and Erectile Function Recovery After Robotic-Assisted Radical Prostatectomy: Insights from a Prostate Cancer Referral Center

IntroductionProstate cancer remains a significant health concern, with radical prostatectomy being a common treatment approach. However, predicting postoperative functional outcomes, particularly urinary continence and erectile function, poses challenges. Emerging artificial intelligence (AI) technologies offer promise in predictive modeling. This study aimed to develop and validate AI-based models to predict continence and potency following nerve-sparing robotic radical prostatectomy (RARP). MethodsA cohort of 8,524 patients undergoing RARP was analyzed. Preoperative variables were collected, and two separate machine-learning Artificial Neural Network (ANN) models were trained to predict continence and potency at 12 months post- surgery. Model performance was assessed using area under the curve (AUC) values, with comparisons made to other machine learning algorithms. Feature importance analysis was conducted to identify key predictors. ResultsThe ANN models demonstrated AUCs of 0.74 for potency and 0.68 for continence prediction, outperforming other algorithms. Feature importance analysis identified variables such as age, comorbidities, and preoperative scores as significant predictors for both outcomes. ConclusionAI-based models show potential in predicting postoperative functional outcomes following RARP. Continued efforts in optimizing models and exploring additional factors are needed to improve predictive accuracy and clinical applicability. Multi-center studies and larger datasets will further contribute to enhancing the value of AI in clinical decision-making for prostate cancer treatment.

Abstract TMP118: Validation and Reliability of the SPAN Score for Assessing Neurological Function in Mouse Stroke Models following MCAO

Introduction: Neuroanatomy-based functional examinations are crucial for understanding the extent and effects of infarcts in stroke studies. Despite the importance of correlating neurological function with brain anatomy, a comprehensive neurological score that correlates with infarction volume and location following middle cerebral artery occlusion (MCAO) in mice is lacking. During the pilot phase of the Stroke Preclinical Assessment Network (SPAN), a new scoring system—SPAN score—was developed. This study aims to evaluate the reliability and validity of the SPAN score, a 9-item tool rated on a 0-3 Likert scale. Methods: The SPAN 2.0 pilot database was utilized for analysis. Reliability was assessed using Cronbach's alpha (α) and intraclass correlation coefficient (ICC) for individual items and the total score. Multivariate regression was performed for item selection and to determine the weight of each item. Concurrent validity was examined by correlating post-operative day 2 infarction volume, as measured by MRI, with the SPAN score. Results: The total SPAN score demonstrated acceptable reliability, with Cronbach's α and ICC both at 0.751. Item-total correlation coefficients of each item ranged from 0.092 to 0.638. The Pearson correlation coefficient between the total SPAN score and infarction volume was 0.539. Notably, spontaneous activity showed a reversed relationship with other items; excluding this item increased Cronbach's α to 0.766. Conclusion: The SPAN score proves to be a reliable and valid tool for neuroanatomy-based functional assessment in mouse stroke models following MCAO. Ongoing analysis will focus on refining the score by reducing the number of items, adjusting the Likert scale, incorporating lesion area, and further exploring item relationships.

GDFold2: A fast and parallelizable protein folding environment with freely defined objective functions.

An important step of mainstream protein structure prediction is to model the 3D protein structure based on the predicted 2D inter-residue geometric information. This folding step has been integrated into a unified neural network to allow end-to-end training in state-of-the-art methods like AlphaFold2, but is separately implemented using the Rosetta folding environment in some traditional methods like trRosetta. Despite the inferiority in prediction accuracy, the conventional approach allows for the sampling of various protein conformations compatible with the predicted geometric constraints, partially capturing the dynamic information. Here, we propose GDFold2, a novel protein folding environment, to address the limitations of Rosetta. On the one hand, GDFold2 is highly computationally efficient, capable of accomplishing multiple folding processes in parallel within the time scale of minutes for generic proteins. On the other hand, GDFold2 supports freely defined objective functions to fulfill diversified optimization requirements. Moreover, we propose a quality assessment (QA) model to provide reliable prediction on the quality of protein structures folded by GDFold2, thus substantially simplifying the selection of structural models. GDFold2 and the QA model could be combined to investigate the transition path between protein conformational states, and the online server is available at https://structpred.life.tsinghua.edu.cn/server_gdfold2.html.

Heat transfer modeling and system performance assessment of an innovative thermosyphon radiator integrated with air source heat pumps for sustainable heating applications

Heat transfer modeling and system performance assessment of an innovative thermosyphon radiator integrated with air source heat pumps for sustainable heating applications

Fish migration modeling and habitat assessment in a complex fluvial system.

Fish migration patterns are driven by hydrodynamic factors, which are essential in aquatic ecology. This study investigated the hydrodynamic drivers of Gymnocypris przewalskii fish migration in two distinct river reaches-a straight reach (SR) and a confluence reach (CR)- in the area of Qinghai Lake, China, using a 3D numerical model, fish density field data, and four predictive models. Thirteen hydrodynamic factors, with a focus on water depth and velocity, were analyzed to identify their influence on fish migration. It was found that in the SR, linear factors of flow velocity and turbulent kinetic energy were most influential, while in the CR, nonlinear factors of water temperature and vortex intensity dominated.For CR, fish migration patterns are also important nonlinear factors. Methods that accurately reveal fish migration patterns, such as Random Forest, offer higher precision for habitat assessment. Our research also shows that fish swimming ability can, to some extent, reflect migration direction. Combining fish swimming ability with traditional linear habitat assessment methods can improve the adaptability of these methods in complex fluvial system. Based on our research findings, we propose a new workflow for fish habitat assessment that integrates both linear and nonlinear predictive methods. This framework provides valuable insights for enhancing fish conservation strategies in various fluvial systems.

Sustainable process modeling and holistic 4E assessment of an innovative CHP plant with renewable hydrogen production based on multi-heat recovery and PEM electrolyzer

Sustainable process modeling and holistic 4E assessment of an innovative CHP plant with renewable hydrogen production based on multi-heat recovery and PEM electrolyzer

Understanding heavy Metal pollution dynamics in Çandarlı Gulf, western Türkiye: Insights from spatial analysis, sediment transport modeling and ecological risk assessment

Understanding heavy Metal pollution dynamics in Çandarlı Gulf, western Türkiye: Insights from spatial analysis, sediment transport modeling and ecological risk assessment

Insulin resistance links dysbiosis of gut microbiota with cognitive impairment in first-episode drug-naïve schizophrenia.

This study aimed to explore the relationship among gut microbiota imbalance, the homeostasis model of assessment of insulin resistance (HOMA-IR) and cognitive impairments in patients with schizophrenia (SCZ). We conducted a case-control study involving 189 first-episode, drug-naïve SCZ patients and 115 healthy controls (HCs). Main methods adopted included metagenomics analysis, glucose metabolism assessment, and cognitive function evaluation using the MATRICS Consensus Cognitive Battery (MCCB). Fecal microbiota composition was analyzed via high-throughput sequencing of 16S ribosomal RNA. Patients with SCZ showed a higher likelihood of developing IR (23 %), compared to HCs (12 %). The IR group exhibited significantly higher levels of fasting blood glucose (FBG), fasting insulin (FINS), HOMA-IR, and homeostasis model assessment-β (HOMA-β), while showing lower insulin sensitivity index (ISI) levels (all p < 0.05). Patients with IR demonstrated lower scores in working memories (WM), verbal learning (HVLT) and reasoning and problem solving (RPS), compared to those without IR. Additionally, microbiota analysis revealed that IR patients had higher abundance of Negativicutes, Streptococcaceae, Enterobacteriaceae, Lachnoclostridium, Dialister, Klebsiella, and Enterobacter, and lower abundance of Flavonifractor and Rikenellaceae. Notably, Negativicutes, Streptococcaceae, Lachnoclostridium, Flavonifractor, and Rikenellaceae were shared between SCZ and IR conditions. Mediation analysis indicated that the relative abundance of Streptococcaceae have an indirect effect on WM through HOMA-IR (β=-0.148, SE=0.067, 95 %CI=-0.280 to -0.020). The study suggests that IR may play a mediating role in the relationship between gut microbiota dysbiosis and cognitive impairments in patients with SCZ, which could point to potential new avenues for therapeutic interventions.

Integrating D-Dimer Thresholds into the Revised Caprini Risk Stratification to Predict Deep Vein Thrombosis Risk in Preoperative Knee Osteoarthritis Patients.

Preoperative patients with knee osteoarthritis have a significantly increased risk of venous thromboembolism (VTE). While the Caprini risk assessment model offers some clinical guidance in predicting deep vein thrombosis (DVT), it has a relatively low predictive accuracy. Enhancing the model by integrating biomarkers, such as D-dimers, can potentially improve its accuracy. In this study, we explored the effectiveness of combining the Caprini risk model with D-dimer levels for individualized DVT risk assessment in patients with knee osteoarthritis. This retrospective cohort study included 1605 knee osteoarthritis patients scheduled for total knee arthroplasty from Peking Union Medical College Hospital, screened between January 2015 and December 2018. A revised Caprini risk stratification model was developed, and a predictive DVT model was developed based on this revised system. The sensitivity, specificity, and the area under the curve (AUC) were used to determine predictive effectiveness of the model. In the revised Caprini risk stratification, the incidence of DVT increased with higher risk levels: 2.52% in the low-risk group (scores 0-2), 2.88% in the moderate-risk group (score 3), 6.47% in the high-risk group (score 4), and 9.09% in the highest-risk group (score ≥ 5). The incidence of DVT was 3.869-fold higher in the highest-risk group and 2.676-fold higher in the high-risk group compared to the low-risk group (p = 0.013 and p = 0.014, respectively). Combining the revised Caprini risk stratification with D-dimer level demonstrated an improved AUC of 0.792, compared to D-dimer level alone (AUC 0.774) and the revised Caprini model alone (AUC 0.598). Furthermore, applying specific D-dimer thresholds across the four Caprini risk stratifications outperformed the combination of the revised Caprini model and D-dimer level in terms of AUC, specificity, and reduction in unnecessary ultrasonography. Using the Youden index, the AUC for the threshold-based method was slightly higher (0.775 vs 0.754, p = 0.310), with significantly better specificity (76.8% vs 63.6%, p < 0.001) and a greater reduction in ultrasound use (74.1% vs 61.4%). At a sensitivity of 85.5%, the differences were modest but still favored the threshold-based approach. At a sensitivity of 100%, the specificity (36.0% vs 24.7%, p < 0.001) and ultrasound reduction (34.8% vs 23.9%) were significantly better. The revised Caprini risk stratification improves preoperative DVT prediction in patients with knee osteoarthritis. Incorporating specific D-dimer thresholds into the four-level Caprini risk model enhances specificity and reduces unnecessary ultrasonography, outperforming both the use of individual indicators and the combination of the revised Caprini model with D-dimer level.