Sensitivity Index Research Articles

Applying Shapley Effect for Sensitivity Analysis During Reactor Transient

Sensitivity analysis is a critical tool in reactor safety assessments, as it evaluates the impact of uncertainties in input parameters, identifies key factors, and highlights potential safety risks and measures. Conventional sensitivity methods, such as Spearman, Pearson, or Kendall, while straightforward, are typically limited to linear relationships and independent input parameters. Shapley values offer a more advanced, model-agnostic approach to sensitivity analysis, making them particularly valuable in scenarios with dependent parameters or nonlinear systems. This study not only applies variance-based sensitivity methods, including Sobol indices and Shapley values, but also introduces the development of a reduced-order model (ROM) based on deep neural networks (DNNs) combined with Shapley values for time-dependent reactor simulations. This approach addresses the computational challenges of traditional methods, especially in cases involving correlated parameters, providing a more efficient and accurate sensitivity analysis. Sensitivity indices are calculated for the TWIGL benchmark, with two-group cross sections as the input parameters and core power during the ramp reactivity insertion transient as the output. The results demonstrate that Shapley values, combined with the DNN-based ROM, yield robust, accurate, and physically meaningful indices, especially in models with dependent parameters where Sobol indices may lead to over- or underestimation and might even result in negative indices. This highlights the advantages of Shapley values for comprehensive and reliable sensitivity analyses in complex reactor simulations.

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.

The impact of noise exposure, time pressure, and cognitive load on objective task performance and subjective sensory overload and fatigue

ABSTRACT Introduction Sensory hypersensitivity (SHS) refers to an increased sensitivity to sensory stimuli, often leading to sensory overload and adversely affecting daily functioning and well-being. This study examined the effects of three situational triggers – noise, time pressure, and cognitive load – on task performance, sensory overload, and fatigue. Additionally, we sought to explore the associations between these effects and SHS, while accounting for other influencing factors such as personality, coping mechanisms, and anxiety. Method We experimentally tested 105 university students, employing a visuospatial task (the Paper Folding Test, PFT) under eight different conditions, manipulating the three situational triggers. The measured outcomes included task accuracy, average response time, sensory overload, and fatigue. Participants also completed several questionnaires: Highly Sensitive Person Scale (HSPS), Multi-Modal Evaluation of Sensory Sensitivity (MESSY), State and Trait Anxiety Index, Big Five Inventory, and COPE Easy. Results Our findings indicated that sensory overload increased as more situational triggers were introduced, with noise having the most significant impact. However, this increase in sensory overload did not correspond to changes in objective performance measures, such as accuracy and average response time on the PFT, which were primarily influenced by cognitive load (i.e. easy versus difficult items). Additionally, individuals with higher levels of SHS (HSPS and MESSY) reported greater overall sensory overload and fatigue. Nonetheless, the impact of the triggers on sensory overload and fatigue was not exclusive to those with high SHS, and neuroticism, conscientiousness, openness, and trait anxiety were significant predictors of SHS, more so than task-related outcomes. Conclusions Feelings of sensory overload may not necessarily impair cognitive performance, and the impact of situational triggers can be similar for individuals with and without SHS. This implies that the burden of SHS and overall sensory overload may be influenced by other underlying factors leading to an elevation of baseline sensory overload, warranting further investigation.

The Association Between Glucose Variability and Insulin Parameters in Gestational Diabetes Diagnosed After 24 Gestational Weeks

Background/Objectives: Recently, it was reported that glucose variability (GV) calculated using the 75 g oral glucose tolerance test (OGTT) is associated with adverse perinatal outcomes. However, its role in gestational diabetes mellitus (GDM) remains unclear. We investigated the association between GV and insulin parameters in Japanese women diagnosed with GDM after 24 weeks of gestation (late GDM). Methods: A total of 280 mothers with late GDM cared for at Keio University Hospital were included in this study. Using 75 g OGTT, the initial increase and subsequent decrease were calculated as the GV. Results: The initial increase was significantly positively associated with 1 h plasma glucose level (PG) and 2 h PG with 75 g OGTT (p &lt; 0.001), but fasting PG, insulinogenic index (IGI), and homeostasis model assessment—insulin resistance were negatively associated with the initial increase (all p &lt; 0.001). The subsequent decrease was significantly positively correlated with 1 h PG (p &lt; 0.001) but negatively correlated with 2 h PG (p &lt; 0.001), IGI (p = 0.009), and the whole-body insulin sensitivity index derived from the OGTT (p = 0.02). Insulin Secretion-Sensitivity Index-2 was not associated with an initial increase or subsequent decrease. Conclusions: Since the initial increase might reflect insulin secretion and the subsequent decrease might reflect insulin sensitivity in Japanese women with late GDM, GV could alter several insulin parameters. Further studies are required to investigate the usefulness of GV in the management of GDM.

Higher visceral and lower peripheral adiposity characterize fat distribution and insulin resistance in Asian Indian women with Polycystic Ovary Syndrome in Mauritius.

Introduction There are controversies about whether women with polycystic ovary syndrome (PCOS) show a disproportionately higher visceral adiposity, and its relevance to their higher cardiometabolic risks. We investigated in women of Asian Indian descent in Mauritius, a population inherently prone to abdominal obesity, whether those with PCOS will show a more adverse cardiometabolic risk profile that could be explained by abnormalities in fat distribution. Methods Young women newly diagnosed with PCOS (n=25) were compared with a reference control cohort (n =139) for the following measurements made after an overnight fast: body mass index (BMI), waist circumference (WC), body composition by dual-energy x-ray absorptiometry, blood pressure and blood assays for glycemic (glucose, HbA1c, insulin) and lipid (triglycerides, cholesterols) profiles. Results Compared to controls, women with PCOS showed, on average, higher BMI, WC, fat mass and lean mass (p<0.01), but linear regression analyses indicate that, for the same BMI (or same WC), the two groups showed no significant differences in fat mass and lean mass. By contrast, linear regression plots indicate that for the same total fat mass, women with PCOS showed higher trunk, android and visceral fat (p<0.01), no difference in abdominal subcutaneous fat, and lower peripheral (gynoid or limb) fat (p<0.05). Furthermore, the women with PCOS showed higher fasting plasma insulin, insulin resistance (HOMA-IR) index and lower insulin sensitivity index QUICKI (all p<0.001), which were completely or markedly abolished after adjusting for visceral fat or central-to-peripheral fat ratios. Conclusion In Mauritius, young women of Asian Indian descent with PCOS show altered fat distribution characterized by a disproportionately higher visceral (hazardous) adiposity in parallel to lower peripheral (protective) adiposity, which together explain their exacerbated state of hyperinsulinemia and insulin resistance.

Hypomagnesemia, insulin secretion and action in patients without diabetes, 1 year after kidney transplantation

IntroductionHypomagnesemia after kidney transplantation has been reported as a potential risk factor for development of post-transplant diabetes mellitus.MethodsIn kidney transplant recipients undergoing an oral glucose tolerance test during one-year surveillance follow-up we estimated insulin sensitivity with the Matsuda index, a modified Stumvoll index, and HOMA-2IR. First and second phase insulin secretion was assessed using the Stumvoll equation. Participants were categorized into tertiles by plasma magnesium levels, (&amp;lt;0.7, 07–0.78,&amp;gt;0.78 mmol/L).ResultsWe included 208 patients (62% men, median age 51 years). Patients in the lowest compared to the highest magnesium tertile had higher measured GFR (mean 59 vs. 49 mL/min, p = 0.002), tacrolimus trough concentration (mean 6.7 vs. 5.5 μg/L, p &amp;lt; 0.001), and fasting plasma glucose (mean 5.5 vs. 5.3 mmol/L, p = 0.03). There was no significant difference in the Matsuda index between magnesium tertiles, nor in insulin sensitivity assessed by modified Stumvoll index, HOMA-2IR, first or second phase insulin. Results indicate a non-significant trend toward lower disposition index in the lowest vs. highest tertile (p = 0.052).ConclusionIn kidney transplant recipients with lower compared to normal plasma magnesium levels we found a higher fasting plasma glucose but no differences in insulin sensitivity indexes nor dynamic insulin measurements.

P0520 Association between interoception and Visceral Hypervigilance in patients with Inflammatory Bowel Disease

Abstract Background Due to the relapsing course of inflammatory bowel diseases (IBD) with varying disease activity and symptom burden, patients tend to monitor symptoms and bodily sensations to recognize early signs of relapses. Interoception is the ability to perceive and evaluate internal processes of the body. Visceral hypervigilance (VH) describes anxiety and increased attention towards gastrointestinal symptoms. It has been shown to be associated with psychologic comorbidities and reduced quality of life in patients with IBD1, but the factors causing VH are still unknown. In this study we explored whether changes in interoceptive abilities (accuracy, I-acc; sensibility, I-sens and awareness, I-aw) are associated with VH. Methods Adults with IBD (n=34) performed a heartbeat tracking task (Schandry, 1981) for assessment of I-acc and completed questionnaires to assess VH (Visceral Sensitivity Index, VSI) and I-sens. Correlations between I-acc and I-sens were used for an approximation of I-aw. Associations were examined using t-tests and Pearson correlation. Results A preliminary examination of the current sample (recruitment is ongoing) showed no significant association between VH and I-acc (r=-0.041; p=0.817) or I-sens (r=0.236; p=0.178). Comparison of a low VH (VSI&amp;lt;20) vs high VH (VSI&amp;gt;40) group showed no significant differences in I-acc or I-sens (VSI&amp;lt;20: n=11; VSI&amp;gt;40: n=9; I-sens: t= -1,183; p= 0,126; I-acc: t= 0,47; p=0,322). Moreover I-aw was not significantly different between a high VH and a low VH group (z=0.218, p=0.828). I-acc and I-sens correlated positively (r=0.374 p = 0.018). VH was strongly correlated with symptoms of anxiety (r=0.659, p&amp;lt;0.001) and depression (r=0.618; p&amp;lt;0.001), but I-acc was not significantly associated with symptoms of anxiety and depression (anxiety r=0.188 p=0.328; depression r=0.067, p=0.729). I-sens was associated with symptoms of anxiety (r= 0.371, p = 0.048), but not with symptoms of depression (r=0.32, p=0.091). Conclusion Our preliminary results suggest no associations between VH and objective or subjective interoceptive abilities in patients with IBD. This may be owed to the small sample size with only few patients showing high or low VH. Moreover - even though previous data showed a relationship between perception of cardiac and gastrointestinal signals, and both communicate via the vagal nerve 2-3 - heart-beat tracking may not be the optimal task to measure interoception in patients with IBD. VH and interoception should be examined in larger IBD samples that include individuals with a higher symptom burden. Future studies may also explore further possibilities to assess interoception in IBD patients.

P0438 Altered Visceral Sensitivity in patients with Ulcerative Colitis in remission

Abstract Background Ulcerative colitis (UC) in remission is commonly associated with residual symptoms, despite adequate inflammatory control. Distinguishing between inflammatory and non-inflammatory causes is essential, in order to target therapy and avoid any unnecessary treatment escalation. Once mucosal inflammation has been excluded through endoscopic evaluation, high resolution anorectal manometry (HRAM) along with sensory testing could help symptom assessment. The aim on this study is to explore whether established physiologic diagnostic modalities such as HRAM could add value in investigation of patients with UC in remission. Methods Patients with confirmed UC in remission (after proper endoscopic assessment) were evaluated with Visceral Sensitivity Index (VSI, 15 items, ranging 0-75). They subsequently underwent HRAM with a solid-state catheter followed by the rectal sensory test by a gradual rectal distention according to the IAPWG Protocol, aiming to access rectal physiology and sensory perception. Results A total of 32 eligible adult patients (43% females, mean age 40,8 y.o.) were enrolled. Internal consistency for VSI was excellent (Cronbach’s alpha=0.933). Total mean VSI scores were 43.15 (26.11-60.19). An increased VSI score was reported in 12/37 (37.5%) of patients, whereas 20/32 (62.5%) of patients ranged low. Patients with increased total VSI scored significantly worse in bloating and abdominal distention, anxiety when going to new restaurants and worry, avoiding new foods, belief in a serious illness indication, a frightening abdominal discomfort and vigilance while no significant differences were found in mean rectal pressures and outcomes of balloon inflation (table 1). Conclusion Although a 37% of patients with UC in endoscopic remission score high in VSI, rectal pressures and perception as observed by the HRAM and ballon distention test are non-significant. VSI displays excellent consistency when evaluating patients with UC.

Effect of PCC Material Properties on MEPDG Jointed Plain Concrete Pavement (JPCP) Performance Prediction

This paper focuses on comprehensive sensitivity analyses of various rigid pavement scenarios. Jointed Plain Concrete Pavement (JPCP) sections designed for three traffic levels in each of five climate zones are evaluated in the sensitivity analyses. One-at-a-time (OAT) local sensitivity analysis was implemented using a design limit normalized sensitivity index (NSI) to provide both quantitative and qualitative sensitivity information. All portland cement concrete (PCC) material properties examined showed sensitivity to at least one rigid pavement performance measure. The highest ranked sensitive inputs for JPCP analysis include the PCC strength and stiffness properties and the curling and warping related properties. The findings of this study can provide some practical guidance in PCC material property selection for rigid pavement practitioners in using MEPDG.

Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation

The increasing penetration of distributed generation (DG) brings about great economic and environmental benefits, while also negatively affecting the operation of distribution networks due to its high intermittency. Although distributed energy storage (DES) can effectively deal with the problems caused by massive DG penetrations by decoupling the generation and consumption of electricity, the placement of DES significantly determines the effectiveness of its capabilities. Unfortunately, existing DES placement studies are commonly based on a balanced network model, whereas practical distribution networks are unbalanced. In addition, existing DES placement studies are mostly based on an extreme scenario and rarely consider the operational complexity resulting from the uncertainties of DGs and loads. To address the aforementioned challenges, this paper proposes a hierarchical and sequential DES placement strategy in distribution networks by considering multi-scenario operations. Specifically, the proposed hierarchical framework for DES placement includes three sequential layers: outer, inter, and inner. In the outer layer, a multi-scenario comprehensive loss sensitivity index (MSCLSI) is first introduced to search for the most effective DES placement location. Subsequently, the sizing and scheduling of DES for the selected location are conducted through coordinated optimization across the inter and inner layers, which can be solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP). Finally, a series of detailed simulations are carried out over the IEEE-33 test system and the experimental results demonstrate that the proposed scheme can provide significant effectiveness and superiority compared to the state-of-the-art schemes.

Benthic macroinvertebrate diversity and function in an agricultural constructed wetland affected by agrochemical pressure (Seine-et-Marne, France).

Constructed wetlands (CWs), originally designed to mitigate chemical water pollution, often host noticeable aquatic fauna. However, little is known about the impact of the contaminants circulating within CWs on this local fauna, questioning the role of CWs as ecological refuges or traps. We aimed to assess the potential of an agricultural CWs in northern France to act as an ecological trap for aquatic fauna and the potential consequences on wetland functioning. We made faunistic inventories of benthic macroinvertebrates, using litterbags, from March to June 2022 in two zones within the CWs with contrasting levels of agrochemical contamination and in one unpolluted comparison pond. We calculated community diversity and sensitivity indices (e.g., species at risk, SPEARpesticides index). We measured wetland functioning by monitoring the leaf-litter breakdown. Results showed that pesticide fluxes were related to community composition changes and had negative effects on taxonomic diversity (Shannon index) and functional traits (shredder/scraper feeding mode). The negative link between pesticides and the leaf-litter breakdown was less clear, mainly because of the high level of integration of this response. This study reveals that CWs under agrochemical pressure may act as potential ecological traps for benthic macroinvertebrates and highlights the relevance of studying this group as an early-warning indicator of chemical risk in nature-based solutions.

Derivative based global sensitivity analysis and its entropic link

Variance-based Sobol’ sensitivity is one of the most well-known measures in global sensitivity analysis (GSA). However, uncertainties with certain distributions, such as highly skewed distributions or those with a heavy tail, cannot be adequately characterised using the second central moment only. Entropy-based GSA can consider the entire probability density function, but its application has been limited because it is difficult to estimate. Here we present a novel derivative-based upper bound for conditional entropies, to efficiently rank uncertain variables and to work as a proxy for entropy-based total effect indices. To overcome the non-desirable issue of negativity for differential entropies as sensitivity indices, we discuss an exponentiation of the total effect entropy and its proxy. Numerical verifications demonstrate that the upper bound is tight for monotonic functions and it provides the same input variable ranking as the entropy-based indices for about three-quarters of the 1000 random functions tested. We found that the new entropy proxy performs similarly to the variance-based proxies for a river flood physics model with 8 inputs of different distributions, and these two proxies are equivalent in the special case of linear functions with Gaussian inputs. We expect the new entropy proxy to increase the variable screening power of derivative-based GSA and to complement Sobol’-indices proxy for a more diverse type of distributions.

A Single-Loop Method for Time-Variant Reliability Sensitivity Analysis of Motion Mechanisms Using Bayes’ Theorem

Abstract Time-variant reliability sensitivity (TRS) analysis can measure the effect of input factors on the structure/mechanism failure. The traditional method for TRS analysis employs a nested sampling procedure, with computational cost depending on the number of input factors. To address the above weaknesses, a single-loop method is developed for TRS analysis. Based on Bayes’ theorem, the sensitivity measure is derived and expressed by the difference between the probability density function (PDF) and the failure-conditional PDF. This derivation allows for TRS analysis to be performed with just one set of samples, where the computational complexity does not depend on the number of inputs. Then, the procedures for Monte Carlo simulation (MCS) are listed based on the innovative estimation of the sensitivity index. Three examples involving numerical and engineering problems are employed to validate the proposed strategy, with the direct MCS introduced for comparison. The results reveal that the proposed strategy provides satisfactory TRS analysis while significantly saving computational resources.

Refining PREVENT prediction models for 10-year risk of cardiovascular disease using measures of anxiety and depression

Refining PREVENT prediction models for 10-year risk of cardiovascular disease using measures of anxiety and depression

Deciphering Groundwater-Surface water Interactions using Environmental Tracers in a Tropical Lake, Southwest India

This study focuses on identifying GW-SW interaction locations in a tropical lake - Vellayani Lake (VL), Southwest India, utilizing stable water isotopes (s18O, sD) and chloride mass balance approach. The northern lake region was identified as a critical groundwater discharge “hotspot” with pronounced discharge (2.14×106 - 3.82×106 m³/yr), prompting targeted management interventions. This reaffirms the critical role of groundwater inflow in sustaining the lake’s water balance. Additionally, the application of machine learning (ML) techniques refined the classification of Lacustrine Groundwater Discharge (LGD) and non-LGD sites while predictive modeling utilizing Sensitivity Indices enhanced the understanding of prominent factors influencing lake volume. K-means clustering and Random Forest (RF) classification, achieved high accuracy (90%) and a kappa value of 0.8 in distinguishing groundwater discharge and non-discharge sites. Predictive modeling and sensitivity analysis revealed precipitation as the most influential factor, with a ±20% change causing a 16.69% variation in lake volume. Groundwater discharge exhibited a sensitivity index of 0.5320, further emphasizing its critical role in maintaining lake hydrological balance. This integrated approach provided valuable insights into the critical role of nearshore groundwater recharge in maintaining lake hydrological balance and facilitates the identification of suitable areas for groundwater recharge structures. For practitioners and policymakers, this integrated approach offers a robust framework for identifying critical GW-SW interaction zones, prioritizing groundwater recharge areas, and designing sustainable water management strategies, especially in data-scarce regions, paving the way for improved resource management in similar tropical lake environments.

Comparison of children and adolescents diagnosed with attention deficit hyperactivity disorder with and without autism traits in terms of emotion regulation, clinical characteristics and functionality.

This study aims to demonstrate that children and adolescents diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) who exhibit autism traits have a more severe clinical profile in terms of emotion regulation, clinical features related to ADHD, and functionality, compared to those diagnosed with ADHD without these traits. 50 patients with and 64 patients without autism traits between the ages of 8-16 were recruited for the study among the children and adolescents diagnosed with ADHD. The Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version, DSM-5-2016-Turkish Adaptation (K-SADS-PL-DSM-5-T) was used to exclude the diagnosis of Autism Spectrum Disorder (ASD) and detect comorbid psychiatric diagnosis. The Social Reciprocity Scale (SRS) was completed by parents to determine groups based on autism traits. Children completed the Childhood Anxiety Sensitivity Index (CASI) and the Affective Reactivity Index (ARI). The Strengths and Difficulties Questionnaire (SDQ), Affective Reactivity Index-Parent Report (ARI-P) and Weiss Functional Impairment Rating Scale-Parent Report (WFIRS-P) were completed by the parents. We found that the group with autism traits had significantly more hyperactivity/inattention, conduct problems, emotional problems, and peer problems and significantly more irritability and frequent separation anxiety disorder. Although there was no significant impairment in functionality in either group, the level of impairment was significantly higher in the group with autism traits. Children diagnosed with Attention Deficit Hyperactivity Disorder who exhibit autism traits experience higher levels of irritability and separation anxiety disorder, as well as greater impairment in functionality, compared to those without these traits.

Incorporating genotype information in a precise prediction model for platinum sensitivity in epithelial ovarian cancer.

Develop a predicting model that can help stratify patients with epithelial ovarian cancer (EOC) before platinum-based chemotherapy. 148 patients with pathologically confirmed EOC and with a minimum 5-year follow-up were retrospectively enrolled. Patients were classified into platinum-sensitive and platinum-resistant groups according to treatment responses. The correlation between clinical factors and drug sensitivity was evaluated using statistical tests. Approximately 700,000 single-nucleotide polymorphism (SNP) sites were assessed for association with drug sensitivity via the Genome-wide Association Study (GWAS). LASSO regression and manual selection were employed to reduce the number of variables. A predicting model based on optimized variables was constructed. The predictive ability of the model was assessed using the Kaplan-Meier curve. No statistically significant association was found between clinical factors and drug sensitivity. Sixteen SNPs were preserved after the optimization. A predicting model for drug sensitivity was constructed based on those sixteen SNPs. Coefficients of the synergistic effect for each SNP were determined, and an algorithm of the Drug Sensitivity Index (DSI) was built. The DSI score can successfully distinguish the drug-sensitive or drug-resistant patients with sensitivity, specificity, positive predictive value, and accuracy of 94.7%, 83.3%, 90.8%, and 90.5%, respectively. In both the training set and validating samples, the Kaplan-Meier curve showed that the median PFS and mean OS were significantly differentiated between the predicted sensitive and resistant patients (p-value<0.001). A mathematical model incorporating genotype information could help predict the drug sensitivity of platinum-based chemotherapy before the treatment in EOC patients. A personal chemotherapy could be achieved based on the model.

APPLICATION OF GLOBAL SENSITIVITY ANALYSIS FOR IDENTIFICATION OF PROBABILISTIC DESIGN SPACES

The design space (DS) is defined as the combination of materials and process conditions that provides assurance of quality for a pharmaceutical product. A model-based approach to identify a probability-based DS requires costly simulations across the entire process parameter space (certain) and the uncertain model parameter space and material properties space. We demonstrate that application of global sensitivity analysis (GSA) can significantly reduce model complexity and reduce computational time for identifying and quantifying a probabilistic DS by screening out nonimportant uncertain parameters. The novelty of this approach is that the use of an indicator function, which takes only binary values as a model function, allows application of a straightforward GSA based on Sobol' sensitivity indices and avoids using more costly Monte Carlo filtering or GSA for constrained problems. We consider a chemical reaction example to illustrate how this formulation results in a model reduction and a significant decrease of model runs.

MAXIMIZING REGIONAL SENSITIVITY ANALYSIS INDICES TO FIND SENSITIVE MODEL BEHAVIORS

We address the question of sensitivity analysis for model outputs of any dimension using regional sensitivity analysis (RSA). Classical RSA computes sensitivity indices related to the impact of model input variations on the occurrence of a target region of the model output space. In this work, we take this perspective one step further by proposing to find, for a given model input, the region whose occurrence is best explained by the variations of this input. When it exists, this region can be seen as a model behavior whose occurrence is particularly sensitive to the variations of the model input under study. We name this method mRSA (for maximized RSA). mRSA is formalized as an optimization problem using region-based sensitivity indices. Two formulations are studied, one theoretically and one numerically, using a dedicated algorithm. Using a 2D test model and an environmental model producing time series, we show that mRSA, as a new model exploration tool, can provide interpretable insights on the sensitivity of model outputs of various dimensions.

Indicators of Glucose Metabolism in Children and Adolescents Characterized as Having "Metabolically Healthy" and "Metabolically Unhealthy" Obesity.

Some individuals with obesity may exhibit fewer metabolic disturbances and face a lower long-term risk of complications; however, the existence of this so-called "metabolically healthy obesity" (MHO) compared to "metabolically unhealthy obesity" (MUO) remains controversial. We hypothesized that children with MHO might have a more favorable profile than children with MUO. Markers of glucose metabolism and insulin sensitivity were compared between children and adolescents diagnosed with MHO and MUO. This study recruited prospectively 104 children and adolescents (aged 6-16 years, 47 boys) with obesity. All participants underwent an oral glucose tolerance test (OGTT), and a comparative analysis was performed on HOMA-IR, QUICKI, insulin sensitivity index (ISI), insulinogenic index (IGI), disposition index (DI), and oral disposition index (oDI). Glucose metabolism indices were compared in these subgroups according to pubertal status. Forty-seven children (45.2%) were diagnosed with MHO. The whole-body ISI differed significantly between the MHO and MUO groups (4.02 vs. 2.7, p < 0.01). The IGI was statistically lower in the MHO group compared to MUO (1.26 vs. 1.54, p < 0.01), while neither the DI nor the oDI differed significantly. A higher ISI (4.5 vs. 3.9, p < 0.01) was observed in prepubertal MHO individuals compared to MHO adolescents. Children classified as MHO according to the more recent criteria exhibit a more favorable metabolic profile than those with MUO. However, a completely healthy profile was not demonstrated in the MHO group, as many crucial metabolic profile parameters were comparable to those observed in the MUO group. The findings of this study indicate that all children with obesity, irrespective of whether they are categorized as having MUO or MHO, necessitate close monitoring.