Local Fitting Research Articles

The mediating role of coping in the relationship between perceived health and psychological wellbeing in recurrent urinary tract infection: the rUTI Illness Process Model

ABSTRACT Background: Recurrent urinary tract infection (rUTI) is associated with significant symptom and quality of life burden. Given the unique challenges in diagnostics and management, healthcare disillusionment and stigmatisation which distinguish rUTI from other urological conditions, specific identification of the key illness processes experienced by this patient population is required. This study aimed to identify the unique illness processes and perceptions that contribute to quality of life in rUTI, through perceived health status, psychological wellbeing, and coping. Methods: An international sample of adults living with rUTI (N = 389, 96.9% female) completed a cross-sectional survey comprising the following standardised questionnaires: the EuroQoL EQ-5D-5L, Patient Health Questionnaire 9 (PHQ-9), Generalised Anxiety Disorder 7 (GAD-7), Connor-Davidson Resilience Scale–10 (CD-RISC-10), Pain Catastrophising Scale (PCS). Sociodemographic characteristics were also assessed. Structural equation modelling was conducted to identify the underlying constructs which contributed to psychological wellbeing in rUTI, establishing the ‘rUTI Illness Process Model’. Results: The positive relationship between ‘perceived health status’ and ‘psychological wellbeing’ was partially mediated by ‘rUTI coping’, after controlling for the impact of household income and age (p < .001). The model demonstrated a large effect size (R 2 = .81) and good local and global fit. Overall, rUTI coping skills, boosted by resilience and weakened by pain catastrophising, contribute to a significant proportion of the positive relationship between perceived health status and psychological wellbeing in rUTI. A uniquely vulnerable patient phenotype emerges from this new research, with patients who are younger and/or of lower socioeconomic status at greater risk of poorer rUTI health outcomes and psychological wellbeing, potentially requiring further support. Conclusions: The rUTI Illness Process Model establishes the crucial need to clinically characterise the individualised illness perceptions and metacognitive strategies held by people living with rUTI, revealing that patient-centred interventions targeting illness perceptions and coping strategies require prioritisation to enhance patient outcomes and the patient experience of living with rUTI.

Evaluating Local Model Misspecification with Modification Indices in Bayesian Structural Equation Modeling

Model evaluation is a crucial step in SEM, consisting of two broad areas: global and local fit, where local fit indices are used to modify the original model. In the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are used to select the parameters that can be added to improve the fit. The purpose of this study is to extend the application of MI and SEPC to Bayesian SEM. We present how researchers can estimate posterior distributions of MI and SEPC using a posterior predictive model check (PPMC). We evaluated the effectiveness of these PPMCs with a simulation and found that MI can be used to detect the most relevant added parameters and that SEPC can be used as an effect size. Similar to maximum-likelihood estimation, the SEPC can overestimate the population value. Lastly, we present an example application of these indices.

Real-time ray transfer for lens flare rendering using sparse polynomials

AbstractWhile lens flares are often undesired artifacts of the imaging process, they are also essential for increasing the level of realism in video games and serve as a powerful artistic tool for photography and filmography. For these reasons, computationally reproducing lens flares has always received special attention. Due to the cost of analytical ray tracing, existing solutions are unable to simultaneously achieve the performance needed for real-time environments and retain the ability to simulate arbitrarily complex ghost shapes. Although polynomial optics has been successfully used to increase the efficiency of ray tracing in multiple rendering areas, no complete and validated solution exists that correctly models all aspects of lens flares. This paper presents our polynomial optics-based method for efficiently and accurately ray tracing lens flare ghosts. Our approach successfully models the shape, energy absorption, chromatic effects, and blocking of lens flare rays by partitioning the input domain into local fitting zones. We demonstrate that our model provides a considerable speedup and high accuracy compared to the analytical approach and achieves better fitting speed, output quality, and rendering performance than the naïve application of polynomial optics. The source code for our implementation is available on GitHub.

How to evaluate local fit (residuals) in large structural equation models.

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntaxand output files are freely available to readers.

Value of cardiac enzyme spectrum for the risk assessment of mortality in critically ill children: a single-centre retrospective study

ObjectivesIdentifying high-risk paediatric patients with a poor prognosis and providing timely and adequate treatment are critical. This study aimed to evaluate the effects of different types of cardiac enzyme spectrum...

Selective segmentation of brain abnormalities in colour MRI images using variational model

Early detection of brain abnormalities is vital for enhancing patient outcomes and survival rates. However, accurately identifying and segmenting these abnormalities from MRI images remains a persistent challenge. This study assesses the efficacy of the Selective Local Image Fitting (SLIF) model in segmenting brain abnormalities from colour MRI images and compares its performance with converted greyscale counterparts. The rationale behind this comparison stems from standard practice in image segmentation, where colour images are often converted to greyscale before the segmentation task. Converting the image might degrade data by diminishing its dimensions, potentially affecting segmentation computations. This study intends to evaluate the influence of colour information on segmentation accuracy and efficiency by directly assessing the SLIF model on both colour and converted greyscale images. Segmentation accuracy was evaluated using metrics such as the Dice Similarity Coefficient (DSC), Matthews Correlation Coefficient (MCC), and Intersection-over-Union (IoU). Efficiency was determined by measuring the average elapsed processing time. Experimental results demonstrate that colour MRI brain images outperform their converted greyscale counterparts in segmentation accuracy, as colour providing essential supplementary information for precise abnormality delineation. Despite a slight increase in average elapsed processing time for colour images, the enhanced accuracy justifies this trade-off. These findings emphasize the importance of colour MRI in enhancing diagnostic accuracy, especially in detecting brain abnormalities. This study can be extended in future work to evaluate the segmentation accuracy and efficiency of brain abnormalities in 3D colour and greyscale MRI images.

Efficient variational segmentation with local intensity fitting for noisy and inhomogeneous images

Efficient variational segmentation with local intensity fitting for noisy and inhomogeneous images

Consequences of Local Conspecific Density Effects for Plant Diversity and Community Dynamics.

Conspecific density dependence (CDD) in plant populations is widespread, most likely caused by local-scale biotic interactions, and has potentially important implications for biodiversity, community composition, and ecosystem processes. However, progress in this important area of ecology has been hindered by differing viewpoints on CDD across subfields in ecology, lack of synthesis across CDD-related frameworks, and misunderstandings about how empirical measurements of local CDD fit within the context of broader ecological theories on community assembly and diversity maintenance. Here, we propose a conceptual synthesis of local-scale CDD and its causes, including species-specific antagonistic and mutualistic interactions. First, we compare and clarify different uses of CDD and related concepts across subfields within ecology. We suggest the use of local stabilizing/destabilizing CDD to refer to the scenario where local conspecific density effects are more negative/positive than heterospecific effects. Second, we discuss different mechanisms for local stabilizing and destabilizing CDD, how those mechanisms are interrelated, and how they cut across several fields of study within ecology. Third, we place local stabilizing/destabilizing CDD within the context of broader ecological theories and discuss implications and challenges related to scaling up the effects of local CDD on populations, communities, and metacommunities. The ultimate goal of this synthesis is to provide a conceptual roadmap for researchers studying local CDD and its implications for population and community dynamics.

Basis-Set Limit CCSD(T) Energies for Large Molecules with Local Natural Orbitals and Reduced-Scaling Basis-Set Corrections.

The calculation of density-based basis-set correction (DBBSC), which remedies the basis-set incompleteness (BSI) error of the correlation energy, is combined with local approximations. Aiming at large-scale applications, the procedure is implemented in our efficient local natural orbital-based coupled-cluster singles and doubles with perturbative triples [LNO-CCSD(T)] scheme. To this end, the range-separation function, which characterizes the one-electron BSI in space, is decomposed into the sum of contributions from individual localized molecular orbitals (LMOs). A compact domain is constructed around each LMO, and the corresponding contributions are evaluated only within these restricted domains. Furthermore, for the calculation of the complementary auxiliary basis set (CABS) correction, which significantly improves the Hartree-Fock (HF) energy, the local density fitting approximation is utilized. The errors arising from the local approximations are examined in detail, efficient prescreening techniques are introduced to compress the numerical quadrature used for DBBSC, and conservative default thresholds are selected for the truncation parameters. The efficiency of the DBBSC-LNO-CCSD(T) method is demonstrated through representative examples of up to 1000 atoms. Based on the numerical results, we conclude that the corrections drastically reduce the BSI error using double-ζ basis sets, often to below 1 kcal/mol compared to the reliable LNO-CCSD(T) complete basis set references, while significant improvements are also achieved with triple-ζ basis sets. Considering that the calculation of the DBBSC and CABS corrections only moderately increases the wall-clock time required for the post-HF steps in practical applications, the proposed DBBSC-LNO-CCSD(T) method offers a highly efficient and robust tool for large-scale calculations.

Toward a more culturally nuanced approach to assessing athletic and exercise identities in sport psychology research: a confirmatory factor analysis approach with South Korean male student-athletes

ABSTRACT The identities of “athlete” and “exerciser” share conceptual similarities but can be differentiated by the ways in which their respective objectives, motivations, and intentions significantly diverge. However, there is a gap in the literature addressing the ambiguity and inconsistent use of these terms in the context of South Korea. The linguistic discrepancy in how these terms are conceptualised and operationalised has important implications for the study of athletes’ long-term health and well-being, where a nuanced perspective on identity is necessary for understanding physical activity behaviour and health outcomes through the transition out of competitive sport. Therefore, the purpose of this study was to refine the terminology used in the previously translated versions of the Athletic Identity Measurement Scale and Exercise Identity Scale and examine whether refined questionnaires appropriately represent similar but distinct concepts. Using software program R, this aim was addressed through a two-factor confirmatory factor analysis of the measurement scales within a sample of South Korean male student-athletes (N = 232, Mage = 19.93 ± 1.22 years). Global fit indices of the final model demonstrated acceptable fit χ2 (101) = 208.512, p < .01, CFI = .95, RMSEA = .07 (90% Confidence Interval: .06 – .08), SRMR = .06 and the local fit indices further supported convergent and discriminant validity of the scales within the sample. Recognition of cultural differences and nuances related to the terms “athlete” vs. “exerciser” and “sport” vs. “exercise” is important for fostering a comprehensive understanding of these identities within sport and exercise psychology research.

Translating Between Standardization and Local Fit in Health Information Technology.

The design of health information technology (HIT) requires balancing standardization and local adjustment. Preliminary study findings show that interactions between stakeholder shared attention and HIT translational 'boundary object' features ensure that HIT serves diverse stakeholders' purposes and needs. This can support subsequent implementation and patient safety.

Minimum cost consensus model with variable cost for multi-criteria large-scale group decision-making considering the tolerance of decision-makers

The objective of multicriteria large-scale group decision-making (LSGDM) is to obtain a decision result that gains acceptance from the majority of decision-makers (DMs). The consensus reaching process (CRP) plays a crucial role in guiding DMs to continuously adjust their opinions through feedback adjustment mechanisms to form a widely accepted decision result. In practical decision-making, adjusting opinions incurs costs and involves limited resources. Simultaneously, DMs have tolerance for modifying their opinions. To this end, this paper proposes a minimum cost consensus model (MCCM) with variable cost to reach consensus efficiently within limited resources and DMs’ tolerance. Firstly, a method to measure the combined similarity among DMs is proposed. Then, an extended local fitness maximization (ELFM) algorithm suitable for multicriteria LSGDM problems is proposed for clustering. Furthermore, an objective method of determining the unit adjustment costs for DMs in the MCCM is provided. For the CRP that considers the tolerance of DMs, this paper introduces the MCCM with variable cost to obtain the adjustment opinions of DMs. During the selection process, we utilize the stochastic multicriteria acceptability analysis (SMAA) method to evaluate alternatives and selection. Finally, the feasibility of the proposed method is verified through an illustrative example, and its advantages are emphasized through comparative analysis.

Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable ‘grey-box’ AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale ‘world model’ by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.

Beyond cyanogenesis: Temperature gradients drive environmental adaptation in North American white clover (Trifolium repens L.).

Species that repeatedly evolve phenotypic clines across environmental gradients have been highlighted as ideal systems for characterizing the genomic basis of local environmental adaptation. However, few studies have assessed the importance of observed phenotypic clines for local adaptation: conspicuous traits that vary clinally may not necessarily be the most critical in determining local fitness. The present study was designed to fill this gap, using a plant species characterized by repeatedly evolved adaptive phenotypic clines. White clover is naturally polymorphic for its chemical defence cyanogenesis (HCN release with tissue damage); climate-associated cyanogenesis clines have evolved throughout its native and introduced range worldwide. We performed landscape genomic analyses on 415 wild genotypes from 43 locations spanning much of the North American species range to assess the relative importance of cyanogenesis loci vs. other genomic factors in local climatic adaptation. We find clear evidence of local adaptation, with temperature-related climatic variables best describing genome-wide differentiation between sampling locations. The same climatic variables are also strongly correlated with cyanogenesis frequencies and gene copy number variations (CNVs) at cyanogenesis loci. However, landscape genomic analyses indicate no significant contribution of cyanogenesis loci to local adaptation. Instead, several genomic regions containing promising candidate genes for plant response to seasonal cues are identified - some of which are shared with previously identified QTLs for locally adaptive fitness traits in North American white clover. Our findings suggest that local adaptation in white clover is likely determined primarily by genes controlling the timing of growth and flowering in response to local seasonal cues. More generally, this work suggests that caution is warranted when considering the importance of conspicuous phenotypic clines as primary determinants of local adaptation.

Fitness Landscapes and Evolution of Catalytic RNA.

The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future.

A Meshless And Binless Approach To Compute Statistics In 3D Ensemble PTV

We propose a method to obtain super-resolution of turbulent statistics for three-dimensional ensemble particle tracking velocimetry (EPTV). Our approach is “meshless” because it does not require a grid to compute derivatives, and it is “binless” because it does not require bins to compute local statistics. The method combines the constrained radial basis function (RBF) formalism introduced in Sperotto et al. (Meas Sci Technol, 33:094005, 2022) with a kernel estimate approach for the ensemble average of the RBF regressions. The computational cost for the RBF regression is alleviated with the partition of unity method (PUM). We compare the newly proposed method with traditional binning-based approaches such as Gaussian weighting (Agüí and Jiménez, JFM, 185:447-468, 1987) and local polynomial fitting (Agüera et al, Meas Sci Technol, 27:124011, 2016). The results on experimental data of an underwater jet show a better resolution of spatial gradients of turbulent stresses while at the same time yielding an analytical expression which results in super-resolution of statistical quantities.

Introgression affects Salmo trutta juvenile life-history traits generations after stocking with non-native strains.

Introgression of non-native conspecifics changes the genetic composition of wild populations, potentially leading to loss of local adaptations and fitness declines. However, long-term data from wild populations are still relatively few. Here, we studied the effects of introgression in a Danish brown trout (Salmo trutta, L.) population, subjected to intensive stocking with domesticated hatchery fish of non-native origin. We used wild-caught genetically wild and admixed trout as well as fish from the partly domesticated hatchery strain used for stocking the river up until ~15 years prior to this study, to produce 22 families varying in hatchery/wild admixture. Following a replicated common-garden experiment conducted in fish tanks from first feeding through 23 weeks at 7, 12, and 16°C, we observed a significant positive relationship between family admixture and fish size upon termination, an effect observed through all levels of admixture. Furthermore, the admixture effect was most distinct at the higher rearing temperatures. Although the hatchery strain used for stocking had been in culture for ~7 generations, it had not been deliberately selected for increased growth. These data thus demonstrate: (i) that growth had increased in the hatchery strain even in the absence of deliberate directional selection for this trait, (ii) that the increasing effect of admixture by temperature could represent inadvertent selection for performance in the hatchery strain at higher temperatures, and most significantly, (iii) that despite undergoing up to five generations of natural selection in the admixed wild population, the genetically increased growth potential was still detectable and thus persistent. Our findings suggest that altered growth patterns and potentially their cascading effects are of importance to the severity of hatchery/wild introgression, especially under changing-climate scenarios and are of general significance to conservation practitioners seeking to evaluate long-term effects of intra-specific hybridization including under recovery.

Effect of Relaxation, Lumbopelvic Stabilization, and Strengthening on Pain, Quality of Life, and Lumbopelvic Impairments in Women With Endometriosis

Background: Endometriosis, a chronic gynecological disorder characterized by the presence of endometrial-like tissue outside the uterus, significantly impacts the quality of life and physical functioning of affected women. Conventional treatments involve pharmacological interventions and surgery, which may not always provide long-term relief and are often associated with side effects. Objective: This study aimed to evaluate the effects of relaxation, lumbopelvic stabilization, and strengthening exercises on pain, quality of life, and lumbopelvic impairments in women with endometriosis in Faisalabad, Pakistan. Methods: A quasi-experimental design was employed, involving 40 women diagnosed with endometriosis, recruited from local hospitals and fitness centers. Participants underwent a structured intervention consisting of relaxation techniques, lumbopelvic stabilization, and strengthening exercises, conducted three times per week for seven weeks. Data were collected using the Visual Analogue Scale (VAS) for pain, the Endometriosis Health Profile-30 (EHP-30) for quality of life, and specific physical performance tests for lumbopelvic impairments. Statistical analysis was performed using SPSS version 25, employing repeated measures ANOVA to assess changes from baseline to post-intervention. Results: The intervention resulted in significant reductions in pain (VAS scores decreased, p&lt;0.001), improvements in quality of life (EHP-30 scores improved in all domains, p&lt;0.001 for most domains), and enhanced lumbopelvic function (endurance test scores increased, p&lt;0.001). Conclusion: The study findings suggest that structured physical therapy interventions can significantly improve pain, quality of life, and physical functioning in women with endometriosis. These results support the inclusion of physical therapy as a viable component of comprehensive endometriosis management. Keywords: Endometriosis, Physical Therapy, Quality of Life, Pelvic Pain, Lumbopelvic Stabilization, Strengthening Exercises, Non-Pharmacological Treatment.

A robust reconstruction method based on local Bayesian estimation combined with CURE clustering

Due to their good approximation accuracy and local fitting characteristics, the moving least squares (MLS) and moving total least squares (MTLS) methods are widely used in various engineering fields. However, neither of these two methods is robust and they cannot effectively deal with outliers in measurement data. To eliminate the negative influence of outliers and achieve robust reconstruction, a novel MTLS method is proposed in this paper, which introduces local Bayesian estimation combined with clustering using representatives (CURE) algorithm. In the support domain, this method adopts a two-step process to remove the abnormal points and adjust the weights of discrete points through compound weighting. Bayesian estimation is first performed on discrete points to derive the reference model, and the residuals are calculated as the input of CURE clustering. The points with large residuals are classified into one cluster and removed. The remaining points undergo repeated processing until the iteration concludes. A gradient weight function based on the residuals and a compact support weight function are combined to determine the final estimated value using weighted Bayesian estimation. The simulations and experiments demonstrate that the proposed reconstruction method achieves excellent accuracy and robustness, surpassing several existing methods when handling highly contaminated datasets.

Coupled internal rotations and 14N quadrupole hyperfine structure of 2,4-dimethylpyrrole investigated by microwave spectroscopy and quantum chemistry.

The microwave spectrum of 2,4-dimethylpyrrole was investigated using a Fourier-transform microwave spectrometer in a supersonic expansion. Torsional splittings arising from two inequivalent methyl internal rotors in combination with hyperfine splittings due to the nuclear quadrupole coupling of the 14N nucleus were observed. The experiments were accompanied by quantum chemical calculations. A total of 1561 rotational lines were assigned and fitted in global fits using the programs XIAM and BELGI-Cs-2Tops-hyperfine, both achieved the measurement accuracy of 4kHz. Local separate fits were also performed to verify the correctness of the assignment. Accurate experimental molecular and internal rotation parameters could be deduced and compared to the calculated ones. The barrier to internal rotation of the 2-methyl rotor was determined to be 277.830(26) cm-1, essentially the same as the value of about 280cm-1 found for 2-methylpyrrole but lower than the value of 317cm-1 found for 2,5-dimethylpyrrole. The torsional barrier value of the 4-methyl rotor is 262.210(27) cm-1, slightly higher than the value of 246cm-1 found for 3-methylpyrrole. Benchmarking the rotational constants for 2,4- and 2,5-dimethylpyrrole revealed that the MP2/6-31G(d,p) level could be helpful to guide the assignment of microwave spectra of pyrrole derivatives.