Perform Sensitivity Analysis Research Articles

Evaluating the influence of morphological features on the vulnerability of lipid-Rich plaques during stenting.

Lipid-rich atheromas are linked to plaque rupture in stented atherosclerotic arteries. While fibrous cap thickness is acknowledged as a critical indicator of vulnerability, it is likely that other morphological features also exert influence. However, detailed quantifications of their contributions and intertwined effects in stenting are lacking. Therefore, our goal is to assess the impact of plaque characteristics on the fibrous cap stress and elucidate their underlying mechanisms. We analyzed the stent deployment over a 3D patient-specific coronary artery reconstructed from intravascular optical coherence tomography (IVOCT) through finite element methods. Additionally, we performed sensitivity analysis on 78,000 distinct plaque geometries of two-dimensional arteries for verification. Results from the 3D patient-specific model indicate strong correlations between maximum fibrous cap stress and lipid arc (r=0.769), area stenosis (r=0.550), and lumen curvature (r=0.642). Plaques with lipid arcs > 60°, area stenosis > 75%, and lumen curvatures > 5 mm-1 are at increased rupture risk. While we observed a rise in stress with lipid core thickness, it was less representative than other features. Fibrous cap thickness showed a poor correlation, with the sensitivity analysis revealing its significance only when high stretches are induced by other features, likely due to its J-shaped stress-stretch response. Contrary to physiological pressure, the stent expansion generates unique vulnerable features as the stent load-transferring characteristics modify the plaque?s response. This study is expected to prompt further clinical investigations of other morphological features for predicting plaque rupture in stenting.

Education, intelligence, and 20 gastrointestinal disorders: A Mendelian randomization study.

Previous observational studies have suggested that higher levels of education attainment and intelligence (IQ) are associated with better health outcomes in humans. However, the causal link between education attainment and IQ and their association with health outcomes remains unclear. This study aims to investigate the distinct impacts of intelligence and educational attainment on gastrointestinal symptoms. From the genome-wide association between educational attainment and the IQ study database, results were obtained from the FinnGen summary database. We used univariate and multivariate Mendelian randomization (MR) techniques to explore the relationship between exposures and outcomes. To assess the validity of inverse-variance-weighted-based results, we used several supplementary analytical techniques and performed sensitivity analysis. Our multivariate MR study confirmed the findings from univariable analyses and showed a genetically predicted causal association between educational attainment and 8 gastrointestinal disorders, including gastroesophageal reflux disease, chronic gastritis, gastroduodenal ulcer, cirrhosis, cholelithiasis, acute pancreatitis, chronic pancreatitis, and irritable bowel syndrome. Our univariate MR study found an association between IQ and 6 gastrointestinal conditions: gastroesophageal reflux disease, cirrhosis, cholelithiasis, acute pancreatitis, pancreatic malignancy, and irritable bowel syndrome. However, the connection was much weaker in multivariate MR analysis. Our study revealed causal relationships between gastrointestinal disorders and educational attainment. Educational attainment may mediate between intelligence and the impacts on the gastrointestinal system. However, further research is required to understand the underlying pathogenic processes completely.

Mendelian randomization study of causal link from Cerebrospinal fluid metabolomics to neurodegenerative diseases.

To investigate the causal relationships between cerebrospinal fluid (CSF) metabolites and various neurodegenerative diseases (NDDs), we conducted a two-sample Mendelian randomization (MR) analysis. This study utilized summary statistics from genome-wide association studies (GWAS) of CSF metabolites and four common neurodegenerative diseases: Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS). MR methods were employed to determine causal associations, with the inverse variance weighted method as the primary approach. Additionally, different GWAS summary data for NDDs were used to validate the initial results and perform sensitivity analyses to enhance the robustness of the findings. Finally, reverse MR analyses were conducted to assess the possibility of reverse causation. Combining results from the initial and replication phases of MR analysis, we identified potential causal relationships between various CSF metabolites and different NDDs. Specifically, we found potential causal relationships between five CSF metabolites and AD, six CSF metabolites and MS, and thirteen CSF metabolites and ALS. Further sensitivity analyses confirmed the robustness of these associations. Reverse MR analysis indicated causal effects of AD on glucuronate and ALS on acetylcarnitine (C2). Our study, through genetic means, demonstrates close causal associations between the specific types of CSF metabolites and the risk of NDDS (AD, PD, MS, and ALS), providing useful guidance for future clinical researches.

Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations—II: Illustrative Application to Heat and Energy Transfer in the Nordheim–Fuchs Phenomenological Model for Reactor Safety

This work presents an illustrative application of the newly developed “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)” methodology to determine most efficiently the exact expressions of the first- and second-order sensitivities of NODE decoder responses to the neural net’s underlying parameters (weights and initial conditions). The application of the 2nd-FASAM-NODE methodology will be illustrated using the Nordheim–Fuchs phenomenological model for reactor safety, which describes a short-time self-limiting power transient in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted. The representative model responses that will be analyzed in this work include the model’s time-dependent total energy released, neutron flux, temperature and thermal conductivity. The 2nd-FASAM-NODE methodology yields the exact expressions of the first-order sensitivities of these decoder responses with respect to the underlying uncertain model parameters and initial conditions, requiring just a single large-scale computation per response. Furthermore, the 2nd-FASAM-NODE methodology yields the exact expressions of the second-order sensitivities of a model response requiring as few large-scale computations as there are features/functions of model parameters, thereby demonstrating its unsurpassed efficiency for performing sensitivity analysis of NODE nets.

Prestress state, parametric analysis study and genetic algorithm-based tentative design of a novel pentagonal cable dome structure with tri-strut layout

To improve the structural performance and economy of the large-span roof structure from the structural system level, a novel structural form of pentagonal cable dome with tri-strut layout and large central opening is proposed, which can be applied to the large-span stadium ring canopy structure. Different from Fuller's traditional conception of the tensioning whole, this type of system has three struts intersecting at the same chord node, which reduces the amount of ring and diagonal cables, facilitates tensioning construction, and improves overall stability. For this large-opening cable dome, a general formula for calculating the internal force of prestressed state rods was derived based on the nodal equilibrium equations, and the effects of several parameters on the distribution of pre-tension in the cable dome were analyzed and studied to understand the distribution pattern and characteristics of the cable dome pre-stress. A large-opening stadium canopy with a span of 100m is taken as an example. The parameter sensitivity and correlation analysis of the structural performance reveals that the main influence of the economy of this cable dome is the pre-tension level. In contrast, the geometric parameter produces a much lower effect on the structural economy. The optimal design and trade-off analysis of this cable dome structure based on two genetic algorithms show that appropriately increasing the structural vector height and decreasing the thickness while satisfying the stability improves the structural stiffness and economy. The analysis results show that this new type of cable dome has superior technical and economic indicators, and the research in this paper provides a new form and new ideas for the analysis and design, optimization research, and modeling of the cable dome.

Costs during the first year after stroke by degree of functional disability: A societal perspective.

The aim of this study was to estimate societal costs during the first year after stroke by degree of functional disability. Descriptive study of the cumulative costs incurred during 1-year follow-up of a cohort of patients with stroke in Catalonia (Spain) participating in a multicentre, population-based, cluster-randomised trial (RACECAT). Patients were recruited between September 2017 and January 2019. Costs were collected for each patient from stroke onset to 1-year follow-up through hospital accounting records, electronic healthcare records and structured telephone-based interviews at 6 and 12-months follow-up. Disability was assessed using the 90-day modified Rankin Scale (mRS). Healthcare, community care, and patient/family costs were included. We used complete data from 567 eligible participants. Cost data were analysed using generalised linear models (GLMs) with gamma distributions and log link functions. For variables with >10% zero values, two-part models were applied. We performed sensitivity analyses modifying unit costs for patient/family costs. Of the 567 patients included, 53% had ischaemic large vessel oclusion (LVO) stroke, 24% intracranial haemorrhage and 23% ischaemic non-LVO stroke. Mean cost per patient during the first year after stroke was €29,673 ± 28,632, and increased with degree of disability (mRS 0-2: €18,568 ± 12,244; mRS 3: €38,214 ± 28,172; mRS 4-5: €52,859 ± 36,383). Healthcare costs represented the highest proportion of total costs (63%; €18,724/patient) across all disability levels, with index hospitalisation being the highest (€12,319 ± 17,675); however, community care and patient/family costs represented over 40% of total cost in patients with higher disability levels. Our results are in line with other studies; the costs during the first year after stroke are high and increase with disability. These results are valuable for calculating the cost of severe stroke cases.

Redefining low-threshold buprenorphine access in an integrated mobile clinic program: Factors associated with treatment retention

IntroductionThe Spot mobile clinic provides low-threshold buprenorphine integrated with clinical and social services in Baltimore City, MD. In 2021, The Spot modified practices to improve engagement including providing extended prescriptions, reducing frequency of toxicology testing, giving up to six months to stabilize on medication, offering maximum doses (up to 32 mg total) daily, and utilizing telemedicine. This study characterizes care retention by examining both the total time in care and the percentage of time with buprenorphine prescription coverage during these practice changes, and examines factors associated with retention. MethodsThis retrospective cohort study includes patients (n = 341) who received a buprenorphine prescription who initiated care on The Spot mobile clinic from September 2021 to October 2022, with follow-up through October 2023. We utilized the Cox proportional hazards model and Kaplan-Meier survival analyses to assess differences in care retention by the factors of patient demographics and clinical characteristics. Additionally, we performed sensitivity analyses using Poisson regression to examine differences between patients with 80 % or greater time with active prescription coverage versus <80 % of time with active prescription coverage. ResultsAfter practice setting changes, retention in care at 90 days was 60 %. Patients whose maximum daily buprenorphine dose was 28 to 32 mg were 80 % less likely to discontinue treatment over the study period than those prescribed ≤16 mg (adjusted hazard ratio of discontinuation: 0.2 [95 % CI: 0.1–0.3]). Engaging in wound care or hepatitis C treatment was associated with higher retention in care, and individuals experiencing homelessness remained engaged at rates comparable to stably housed patients. ConclusionPractice changes aimed to improve access to patient-centered, low-threshold buprenorphine treatment may increase retention in care. Notably, higher doses of buprenorphine and integrated treatment with wound care and hepatitis C treatment were associated with increased retention. Due to gaps in patient care, retention metrics should incorporate total time in care as well as percentage of time with an active buprenorphine prescription.

Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations—I: Mathematical Framework

This work introduces the mathematical framework of the novel “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-FASAM-NODE). The 1st-FASAM-NODE methodology produces and computes most efficiently the exact expressions of all of the first-order sensitivities of NODE-decoder responses with respect to the parameters underlying the NODE’s decoder, hidden layers, and encoder, after having optimized the NODE-net to represent the physical system under consideration. Building on the 1st-FASAM-NODE, this work subsequently introduces the mathematical framework of the novel “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)”. The 2nd-FASAM-NODE methodology efficiently computes the exact expressions of the second-order sensitivities of NODE decoder responses with respect to the NODE parameters. Since the physical system modeled by the NODE-net necessarily comprises imprecisely known parameters that stem from measurements and/or computations subject to uncertainties, the availability of the first- and second-order sensitivities of decoder responses to the parameters underlying the NODE-net is essential for performing sensitivity analysis and quantifying the uncertainties induced in the NODE-decoder responses by uncertainties in the underlying uncertain NODE-parameters.

Machine learning visualization tool for exploring parameterized hydrodynamics**This manuscript has been authored by Lawrence Livermore National Security, LLC under Contract No. DE-AC52-07NA2 7344 with the US. Department of Energy. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce

Abstract We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding Ο(TB) of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize "what-if" situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.

Immune traits in combination with inflammatory proteins revealing the pathogenesis of autoimmune liver diseases: A Mendelian randomization study

BackgroundPrior observational research has shown relationships between immune cells, inflammatory proteins, and autoimmune liver diseases (AILD), but their causal associations remain controversial. Therefore, we aimed to clarify the causal association between them. MethodsWe carried out a comprehensive Mendelian randomization (MR) analysis to clarify causal associations between 731 immune traits, 91 circulating inflammatory proteins, and AILD, including primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and autoimmune hepatitis (AIH). A two-step MR analysis was used to explore the mediating role of circulating inflammatory proteins. Additionally, we performed sensitivity analyses to evaluate the robustness of the results. ResultsCD27 on IgD+CD24+B cell, CD27 on IgD−CD38dimB cell, CD27 on unswitched memory B cell, CD27 on switched memory B cell, and CD27 on CD24+CD27+B cell were risk factors for PBC. However, we detected protective effects of CD25 on IgD−CD27−B cell against PBC and CD28 on resting CD4+Treg cell against PSC. Circulating CD40, Interleukin-33, and Delta and Notch-like epidermal growth factor-related receptor were protective factors for PBC. Furthermore, CD40 mediated the association between immune traits and PBC, with the mediated proportions ranging from 18.3 % to 35.4 %. Tumor necrosis factor superfamily member 12 was identified as a risk factor for PSC, and monocyte chemotactic protein 3 was identified as a protective factor for PSC. Additionally, PBC and PSC had effects on eleven immune traits, which are suggested to be the consequences of them. We found no causal association between immune traits, circulating inflammatory proteins, and AIH. Sensitivity analyses demonstrated our results were robust. ConclusionsOur results demonstrate the causal roles of immune traits and inflammatory proteins in PBC and PSC, which reveals their pathogenesis. It is necessary to investigate the specific mechanism by which immune cells and inflammatory proteins affecting the occurrence of AILD.

Evaluating flue gas geo-sequestration and EOR in fractured reservoirs through simulated synergistic reservoir characteristics and injection kinetics

The global reliance on hydrocarbon resources and fossil fuels has resulted in a significant surge in carbon dioxide emissions, necessitating urgent measures to mitigate greenhouse gas emissions. Integrating CO2 storage with enhanced oil recovery (EOR) presents a promising solution for reducing emissions and enhancing oil recovery by sequestering CO2 within oil reservoirs, especially in fractured carbonate reservoirs. The research utilized the Eclipse simulator to model different gas injection scenarios in a crude oil reservoir, focusing on assessing the effectiveness of CO2 and flue gas geo-sequestration and EOR, performing sensitivity analyses on reservoir characteristics, and evaluating the impact of varying injection rates on gas storage capacity and oil recovery factor. The findings demonstrated a superior capacity to store flue gas (150 MMSCF) in comparison to CO2 (85 MMSCF) and flue gas injection demonstrated better reservoir pressure maintenance than CO2 injection, while CO2 injection resulted in a higher oil recovery factor of 52% compared to flue gas injection at 36%. Additionally, analysis of reservoir characteristics in gas storage revealed that, an augmentation in reservoir porosity, permeability, and injection rate substantiated an increase in gas storage capacity for both CO2 and flue gas injection. Except for CO2 storage, which displayed a normal distribution trend in the permeability analysis. Additionally, it was elucidated that higher reservoir pressure and temperature in flue gas injection resulted in a reduction of gas storage capacity, while these variables exhibited relative stability in the context of CO2 injection. The scrutiny of gas storage in reservoir characteristics unveiled substantial alterations in porosity and injection rate, signifying their pivotal roles in influencing gas storage capacity. In the EOR study, heightened reservoir pressure, temperature, permeability, and injection rate collectively contributed to an amplified oil recovery for both flue gas and CO2, except CO2 injection demonstrated a normal distribution trend in oil recovery within the permeability analysis. Conversely, higher porosity was associated with a decrease in oil recovery. The findings of this research provide valuable insights into the feasibility and effectiveness of employing flue gas geo-sequestration and EOR in fractured carbonate reservoirs.

Uncertainty quantification of material parameters in modeling coupled metal and high explosive experiments

Experiments involving the coupling of metal and high explosives (HE) are of notable defense-related interest, and we seek to refine the uncertainty quantification associated with models of such experiments. In particular, our focus is on how uncertainty related to the metal constitutive model challenges our ability to infer high explosive model parameters when analyzing focused science experiments. We consider three focused experiments involving an HE accelerating metal: small plate tests with tantalum/LX-14 and tantalum/LX-17 pairings as well as a tantalum/LX-17 cylinder test. For all three models, we perform sensitivity analysis to ascertain the influence of metal strength on the coupled experimental response. Moreover, we calibrate each model in a Bayesian setting and study the quantification of metal strength on the inference of the HE parameters. Based on our results, we offer guidance for future metal/HE experiments.

Experimental study and sensitivity analysis of performance for a hydrogen diaphragm compressor

As the critical infrastructure of hydrogen in transportation, the energy consumption of hydrogen refuelling stations plays a pivotal role in the progress of hydrogen energy within the transportation sector. Volumetric and isentropic efficiencies serve as metrics for evaluating compressor performance. To investigate the extent of factors, including suction pressure, pressure ratio, overflow pressure, and rotational speed, to the efficiencies of the diaphragm compressor, an experimental rig was set up in this study. The volume and energy losses were analysed by studying pressure–volume diagrams. The result shows that elevating suction pressure results in an increased isentropic efficiency. Specifically, raising suction pressure from 0.2 MPa to 0.8 MPa yields a 10.2 % increase in isentropic efficiency. The increase in pressure ratio leads to a reduction in volumetric efficiency but an increase in isentropic efficiency. When the pressure ratio increased from 3 to 7, the volumetric efficiency decreased by 6.5 % in volumetric efficiency, but the isentropic efficiency increased by 9.8 %. Moreover, the escalation in rotational speed corresponds to a decrease in both volumetric and isentropic efficiencies. As the rotational speed increased from 420 r/min to 660 r/min, volumetric efficiency dropped by 9.6 %, and isentropic efficiency experienced a 19.2 % decrease.

Abstract 4138335: A drug target Mendelian randomization study of triglyceride lowering therapies for aortic stenosis

Introduction: Large observational and Mendelian randomization (MR) studies have demonstrated a strong association between elevated triglycerides (TG) and risk of aortic stenosis (AS). However, at present there remains no evidence for the use of lipid-lowering therapies to prevent or treat AS. To address this gap, we used a drug target MR approach to investigate whether emerging TG lowering therapies may reduce the risk of AS. Methods: We collected summary statistics for TG levels and AS from genome-wide association studies including 1,253,277 and 653,867 European participants, respectively. We first performed conventional MR techniques to investigate a causal relationship between TG levels and AS risk. Next, we identified genetic proxies for the TG-lowering therapies fenofibrate, volanesorsen and evinacumab as single nucleotide polymorphisms (SNPs) within 200kb of the target genes ( PPARA, APOC3 and ANGPTL3 , respectively) which were also significantly associated with TG at p&lt;5x10 -8 and clumped using threshold r 2 &lt;0.1. Inverse-variance weighted (IVW) was the primary analysis method. We then performed sensitivity analyses including weighted-median and MR-Egger. Additional outlier-exclusion analyses were performed using MR-PRESSO and Cook’s distance. Finally, we completed sensitivity analyses using a more stringent clumping threshold of r 2 &lt;0.01. Results: Consistent with previous findings we found genetically predicted TG levels were significantly associated with increased risk of AS (IVW: OR 1.28 per standard deviation increase in genetically predicted log TG level, 95% CI 1.18-1.39, p&lt;0.0001). We further found that genetically proxied volanesorsen was significantly associated with reduced risk of AS (OR 0.54, 95% CI 0.39-0.75, p=0.0002), which was robust in all sensitivity analyses. We did not find evidence of a causal relationship between genetically proxied fenofibrate (IVW: OR 0.50, 95% CI 0.09-2.76, p=0.43) or evinacumab (IVW: OR 1.03, 95% CI 0.73-1.44, p=0.88) and risk of AS. Conclusions: Genetically proxied APOC3 inhibition, but not PPARA or ANGPTL3 , was robustly associated with reduced risk of AS. This suggests that APOC3 inhibition may be a therapeutic opportunity for AS management and warrants further research in the clinical trial setting.

Open versus robotic transversus abdominis release for ventral hernia repair: an updated systematic review, meta-analysis, and meta-regression.

Posterior component separation using transversus abdominis release (TAR) is well established as an option for repair of large hernia defects. TAR can be performed robotically (rTAR) or open (oTAR) with limited data to demonstrate benefit and guide decision making. We conducted a systematic review and meta-analysis comparing rTAR and oTAR approaches for ventral hernia repair (VHR). We searched Pubmed, Embase, Cochrane, and Web of Science for studies comparing rTAR and oTAR for VHR. Hybrid rTAR was not included in our analysis. Our primary outcomes were overall postoperative and intraoperative complications, surgical site occurrences (SSO), SSO requiring surgical intervention (SSOPI), surgical site infection (SSI) superficial or deep, and fascial closure. Additional outcomes were operative time (OT), readmission, length of hospital stay (LOS). We performed sensitivity analysis to explore reasons for heterogeneity and outliers, and a proportional meta-analysis of conversion during rTAR. We performed a meta-regression exploring the relationship of BMI, hernia defect and mesh width rTAR/oTAR with the analyzed outcome within each study. 503 studies were screened and seven studies were included in this analysis. Our sample totaled 780 patients, of which 298 (38.2%) underwent rTAR. Defect width ranged between 8.7 to 13.5cm (cm) for rTAR and 10 to 13.5cm for oTAR. Mean mesh area ranged from 66.9 to 980cm2 and from 51.3 to 1344cm2 for rTAR and oTAR respectively. We found lower overall complications (9% versus 24.6%; RR 0.43; 95% CI 0.26 to 0.73; P < 0.01) and intraoperative complication (5.9% versus 9.1%; RR 0.44; 95% CI 0.22 to 0.88; P = 0.02) rates for the rTAR group. There was no difference in fascial closure between the groups (99% versus 94.6%; RR 1.05; 95% CI 0.99 to 1.11; P = 0.11). rTAR presented lower SSI rates (2.5% versus 7.8%; RR 0.33; 95% CI 0.13 to 0.8; P = 0.01). No differences were found in SSO (16.3% versus 13.7%; RR 0.87; 95% CI 0.51 to 1.48; P = 0.6) or SSOPI (5.4% versus 8.9%%; RR 0.5; 95% CI 0.22 to 1.15; P = 0.1) rates. No statistically significant differences were found in superficial SSI (0.76% versus 3%; RR 0.36; 95% CI 0.07 to 1.75; P = 0.21) and deep SSI (0% versus 4.2%; RR 0.23; 95% CI 0.02 to 3.12; P = 0.27). Open surgery presented a lower OT (MD -67.7min; P < 0.001), but robotic surgery showed a reduced LOS (-3.9days; 95% CI -4.8 to -3.1; P < 0.001). No differences were found in readmission and 1year recurrence rates. The proportional meta-analysis showed a conversion to open rate of 6.4 per 100 patients (95% CI 3.3 to 12 patients) during rTAR. Meta-regression presented no statistically significant influences of rTAR/oTAR mesh width and defect width relations and BMI, despite the analysis was limited by the low number of studies. Robotic TAR may be associated with lower intraoperative and postoperative complications, lower SSI, shorter LOS, and longer operative times when compared to oTAR. Given the limitations of the included studies, randomized trials are needed to better evaluate the impact of the robotic-assisted surgery for complex abdominal wall reconstruction. CRD42024540991.

MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures.

Mendelian randomization is being utilized to assess causal effects of polygenic exposures, where many genetic instruments are subject to horizontal pleiotropy. Existing methods for detecting and correcting for horizontal pleiotropy have important assumptions that may not be fulfilled. Built upon the core gene hypothesis, we developed MR Corge for performing sensitivity analysis of Mendelian randomization. MR Corge identifies a small number of putative core instruments that are more likely to affect genes with a direct biological role in an exposure and obtains causal effect estimates based on these instruments, thereby reducing the risk of horizontal pleiotropy. Using positive and negative controls, we demonstrated that MR Corge estimates aligned with established biomedical knowledge and the results of randomized controlled trials. MR Corge may be widely applied to investigate polygenic exposure-outcome relationships. An open-sourced R package is available at https://github.com/zhwm/MRCorge.

A comparison of stochastic and deterministic dynamics of tuberculosis model

Tuberculosis is a deadly infectious disease leading to a major health concern in Southern India while there are major obstacles to controlling its spread. Here, we present a mathematical model with five compartments. We categorize the infected compartment into two subcategories: latent TB-infected individuals and active TB-infected individuals. By introducing white noise in a deterministic system, we formulate a stochastic system. We investigate the model in deterministic and stochastic framework, followed by data calibration using TB infection data from Kanyakumari District in South India from 2019 to 2023. In the deterministic model, we derive the disease-free and endemic equilibrium points, compute the basic reproduction number, and examine their stability. Moreover, we perform sensitivity analysis to evaluate how variations in model parameters affect TB prevalence. In addition, we study the uniqueness of solutions of stochastic model. After that we derive the conditions for disease extinction and stochastic permanence, and execute extensive simulations to capture the variability and randomness in TB transmission dynamics. This study signifies that stochastic dynamics is richer than deterministic one in the way of mitigation TB transmission.

Skimmed milk intake reduces the risk of ER− breast cancer: a Mendelian randomization analysis

BackgroundIn prior observational investigations, it has been demonstrated that the consumption of milk is associated with the incidence of breast cancer (BC). Despite the existence of a two-sample Mendelian randomization (MR) that suggests a causal relationship between milk intake and breast cancer risk, the outcomes still lack a definitive conclusion. This ambiguity may be attributed to variables such as the variety of milk ingested, estrogen levels, the specific type of BC, and potential confounding factors. Therefore, our principal objective is to establish the causal association between the consumption of skimmed milk and full cream milk and the risk of different types of BC through the utilization of two-sample and two-step MR analyses.MethodsIn this study, we analyzed single nucleotide polymorphisms associated with skimmed and full-cream milk consumption in a cohort of 360,806 individuals from European populations through genome-wide association studies. We conducted a two-sample MR analysis using three different methods: inverse variance weighting (IVW) was used as the main analysis, and MR-Egger and Weighted median were used as supplementary analyses to IVW. We also performed sensitivity analysis, which included leave-one-out analysis, Cochran's Q test to detect heterogeneity, and MR-Egger intercept analysis to detect potential biases caused by pleiotropy. We used two-step MR analysis to evaluate potential mediators of associations.ResultsIn the two-sample MR analysis, IVW analysis suggests a potential inverse causal relationship between skimmed milk and BC [OR 0.34, 95% CI (0.12–1.00), P = 0.05]. Subgroup analysis revealed that skimmed milk reduces the risk of estrogen receptor-negative (ER−) breast cancer [OR 0.18, 95% CI (0.04–0.90), P = 0.04], but not estrogen receptor-positive (ER+) breast cancer [OR 0.42, 95% CI (0.15–1.22), P = 0.11]. MR Egger reached similar results, that is, skimmed milk reduces the risk of ER− breast cancer [OR 0.006, 95% CI (0.00–0.70), P = 0.04], but not BC [OR 0.16, 95% CI (0.01–4.66), P = 0.30] and ER+ breast cancer [OR 0.50, 95% CI (0.02–12.61), P = 0.65]. Additionally, we found no causal relationship between full cream milk and BC (P > 0.05). In two-step MR analysis, we found evidence for a mediating role of BMI in the relationship between skimmed milk intake and ER-breast cancer risk.ConclusionOur findings strengthen the evidence for a protective effect of skimmed milk consumption on ER-breast cancer risk. Further two-step MR analyses suggest that this protective effect may partly result from body mass index (BMI). There is no evidence that full cream milk consumption affects the risk of BC.

Performance sensitivity analysis and aerodynamic optimization of compressor cascades by a turbulence adjoint method

Sensitivity, which is useful for evaluating the contributions of input variations to output changes, favors designers exploiting the most sensitive design parameters and completing design optimization in aerospace. This study introduces the turbulence adjoint method due to its low computational cost in calculating sensitivities with high accuracy and then solves the adjoint partial differential equations with respect to both the Reynolds-averaged Navier–Stokes and turbulence model equations with the assistance of an automatic differentiation tool. By utilizing the turbulence adjoint method, the sensitivities of aerodynamic performance to the blade profile modifications of two-dimensional and three-dimensional compressor cascades are calculated, allowing for the investigations of the impact of geometric variations on the changes in the flow field. Ultimately, aerodynamic optimization of the compressor cascades is conducted. After optimization, the adiabatic efficiency of the two-dimensional cascade increases by 3.11%, and it increases by 1.15% for the three-dimensional cascade. The variations in flow fields of both the original and optimized cascades are illustrated to discover the origins of performance improvements. The changes in blade profile are almost consistent with those forecasted from sensitivity analysis, demonstrating the potential superiority of the adjoint method for aerodynamic design.

Analyzing the compressive strength of one-part geopolymers using experiment and machine learning approaches

As the demand for sustainable construction materials grows, one-part geopolymers present a viable alternative due to their potential for enhancing strength while minimizing carbon emissions and costs. However, accurately predicting the compressive strength of these materials poses significant challenges. Traditional predictive methods, including empirical equations and basic regression techniques, often fall short in capturing the complex relationships among compositional variables. This study employs machine learning (ML) techniques to improve the prediction of compressive strength and perform sensitivity analysis for one-part geopolymers. Experimental analyses were conducted to assess compressive strength, microstructure, and pore characteristics, revealing that increased slag replacement rates enhance hardness and porosity, particularly at levels below 60 %. Given the inherent uncertainties in modeling one-part geopolymer strength, six ML models were evaluated using a comprehensive database. The XGB model exhibited excellent performance, achieving an R2 of 0.95 and an RMSE of 5.2 on the test set, with results validated through experimental data. Additionally, feature importance analysis utilizing the SHAP method highlighted slag percentage, activator Na₂O content, and water-cement ratio as critical factors influencing strength. This research provides an effective and interpretable framework for optimizing one-part geopolymer formulations, advancing sustainable practices in construction.