Local Fitting Research Articles

Abstract PR018: Adaptive local fitness landscapes for aneuploid karyotypes

Abstract Aneuploidy, which occurs in most solid tumors, dramatically alters the expression of many genes which in turn has broad phenotypic effects. Recent evidence suggests that specific aneuploid karyotypes confer a selective advantage. However, various questions remain unanswered regarding the magnitude of these selective advantages, as well as the repeatability and predictability of karyotype evolution. We developed ALFA-K (Adaptive Local Fitness landscapes for Aneuploid Karotypes) – the first approach to infer karyotype fitness landscapes in a context-dependent manner. Based on the dynamics of just a few subclones derived from longitudinal single cell sequencing data, our method can extrapolate the fitness of thousands of karyotypes. We validate the predictive power of our method on data from several P53 deficient cell lines which exhibited substantial subclonal evolution across multiple passages in vivo or in vitro. Based on topological analyses of the inferred fitness landscapes we investigated how mis-segregation rate itself shapes the stringency of karyotype evolution and whether karyotype evolution is subject to diminishing returns epistasis, sign epistasis or reciprocal sign epistasis. We adapted metrics from geo-statistics to study the fitness landscape of a P53 deficient 184-hTERT diploid breast epithelial cell line that underwent whole genome doubling (WGD) and found support for the hypothesis that WGD benefits tumor cells by enhancing their robustness against chromosomal instability. The stark phenotypic consequences of karyotype changes emphasize the value of predicting the evolutionary trajectory of karyotype composition when exposing cells to a new environment. Our work contributes to accumulating evidence that karyotype evolution is predictable. Citation Format: Richard J. Beck, Noemi Andor. Adaptive local fitness landscapes for aneuploid karyotypes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR018.

A large-effect fitness trade-off across environments is explained by a single mutation affecting cold acclimation

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Local and systemic replicative fitness for viruses in specialist, generalist, and non-specialist interactions with salmonid hosts.

Host tissues represent diverse resources or barriers for pathogen replicative fitness. We tested whether viruses in specialist, generalist, and non-specialist interactions replicate differently in local entry tissue (fin), and systemic target tissue (kidney) using infectious hematopoietic necrosis virus (IHNV) and three salmonid fish hosts. Virus tissue replication was host specific, but one feature was shared by specialists and the generalist which was uncommon in the non-specialist interactions: high host entry and replication capacity in the local tissue after contact. Moreover, specialists showed increased replication in systemic target tissues early after host contact. By comparing ancestral and derived IHNV viruses, we also characterized replication tradeoffs associated with specialist and generalist evolution. Compared with the ancestral virus, a derived specialist gained early local replicative fitness in the new host but lost replicative fitness in the ancestral host. By contrast, a derived generalist showed small replication losses relative to the ancestral virus in the ancestral host but increased early replication in the local tissue of novel hosts. This study shows that the mechanisms of specialism and generalism are host specific and that local and systemic replication can contribute differently to overall within host replicative fitness for specialist and generalist viruses.

Limit theorems for local polynomial estimation of regression for functional dependent data

<p>Local polynomial fitting exhibits numerous compelling statistical properties, particularly within the intricate realm of multivariate analysis. However, as functional data analysis gains prominence as a dynamic and pertinent field in data science, the exigency arises for the formulation of a specialized theory tailored to local polynomial fitting. We explored the intricate task of estimating the regression function operator and its partial derivatives for stationary mixing random processes, denoted as $ (Y_i, X_i) $, using local higher-order polynomial fitting. Our key contributions include establishing the joint asymptotic normality of the estimates for both the regression function and its partial derivatives, specifically in the context of strongly mixing processes. Additionally, we provide explicit expressions for the bias and the variance-covariance matrix of the asymptotic distribution. Demonstrating uniform strong consistency over compact subsets, along with delineating the rates of convergence, we substantiated these results for both the regression function and its partial derivatives. Importantly, these findings rooted in reasonably broad conditions that underpinned the underlying models. To demonstrate practical applicability, we leveraged our results to compute pointwise confidence regions. Finally, we extended our ideas to the nonparametric conditional distribution, and obtained its limiting distribution.</p>

Improved Fall Detection Method Based on Local Elliptical Fitting of Human Skeletal Points

Improved Fall Detection Method Based on Local Elliptical Fitting of Human Skeletal Points

A Mixed-Method Approach to Understand Venue Stimuli-Local Image Fit at Soccer Events: The Case of FC Cincinnati

The present study investigated the effectiveness of sport venue stimuli and local image fit in the case of FC Cincinnati at Nippert Stadium. A convergent parallel mixed-methods design was employed to collect and analyze both quantitative and qualitative data, enabling the connection between the two components and facilitating the interpretation of the results. The findings demonstrated that establishing sensory image fit between Nippert Stadium and Cincinnati was achievable, resulting in positive impacts according to both the quantitative and qualitative results. These conclusions, supported by the resource-based view approach, emphasize the importance of organizations leveraging local resources to create value and enhance memorable fan experiences.In practical terms, the applied sensory Image fit scale provides venue managers with a powerful tool to assess the current status of stadium operations in terms of sensory stimuli and guide the design and development of venue stimuli for continuous improvement.

Distribution Fitting for Combating Mode Collapse in Generative Adversarial Networks.

Mode collapse is a significant unsolved issue of generative adversarial networks (GANs). In this work, we examine the causes of mode collapse from a novel perspective. Due to the nonuniform sampling in the training process, some subdistributions may be missed when sampling data. As a result, even when the generated distribution differs from the real one, the GAN objective can still achieve the minimum. To address the issue, we propose a global distribution fitting (GDF) method with a penalty term to confine the generated data distribution. When the generated distribution differs from the real one, GDF will make the objective harder to reach the minimal value, while the original global minimum is not changed. To deal with the circumstance when the overall real data is unreachable, we also propose a local distribution fitting (LDF) method. Experiments on several benchmarks demonstrate the effectiveness and competitive performance of GDF and LDF.

Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds.

Parametric splines are popular tools for precision optical metrology of complex freeform surfaces. However, as a promising topologically unconstrained solution, existing T-spline fitting techniques, such as improved global fitting, local fitting, and split-connect algorithms, still suffer the problems of low computational efficiency, especially in the case of large data scales and high accuracy requirements. This paper proposes a speed-improved algorithm for fast, large-scale freeform point cloud fitting by stitching locally fitted T-splines through three steps of localized operations. Experiments show that the proposed algorithm produces a three-to-eightfold efficiency improvement from the global and local fitting algorithms, and a two-to-fourfold improvement from the latest split-connect algorithm, in high-accuracy and large-scale fitting scenarios. A classical Lena image study showed that the algorithm is at least twice as fast as the split-connect algorithm using fewer than 80% control points of the latter.

A sample of dust attenuation laws for Dark Energy Survey supernova host galaxies

Context. Type Ia supernovae (SNe Ia) are useful distance indicators in cosmology, provided their luminosity is standardized by applying empirical corrections based on light-curve properties. One factor behind these corrections is dust extinction, which is accounted for in the color–luminosity relation of the standardization. This relation is usually assumed to be universal, which can potentially introduce systematics into the standardization. The “mass step” observed for SN Ia Hubble residuals has been suggested as one such systematic. Aims. We seek to obtain a more complete view of dust attenuation properties for a sample of 162 SN Ia host galaxies and to probe their link to the mass step. Methods. We inferred attenuation laws toward hosts from both global and local (4 kpc) Dark Energy Survey photometry and composite stellar population model fits. Results. We recovered a relation between the optical depth and the attenuation slope, best explained by differing star-to-dust geometry for different galaxy orientations, which is significantly different from the optical depth and extinction slope relation observed directly for SNe. We obtain a large variation of attenuation slopes and confirm these change with host properties, such as the stellar mass and age, meaning a universal SN Ia correction should ideally not be assumed. Analyzing the cosmological standardization, we find evidence for a mass step and a two-dimensional “dust step”, both more pronounced for red SNe. Although comparable, the two steps are not found to be completely analogous. Conclusions. We conclude that host galaxy dust data cannot fully account for the mass step, using either an alternative SN standardization with extinction proxied by host attenuation or a dust-step approach.

Adaptation of the Climate Anxiety Scale in Indonesian version: The sample of young adults

The negative emotional impact of climate change has been reported in numerous studies. However, the research on the topic in Indonesia is limited, partly due to the absence of a valid scale relating to the Indonesian context. This study aims to adapt and evaluate the psychometric properties of the Climate Anxiety Scale. The adaptation of the scale into Indonesian was made concerning the International Translating Commission. The study involved 306 young people aged 18 to 35 (M= 21.01, 80.4% female) from February to June 2023. Psychometric property analysis consisted of internal consistency, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). The results indicate satisfactory reliability (Cronbach’s α = .91; McDonald’s ω = .91). Although most items (apart from FI5) behaved similarly to the original 2-factor structure based on EFA, they did not achieve a reasonable fit based on CFA. Therefore, the authors carefully made modifications based on modified indices of the 2-factor structure to achieve reasonable local fit measurements. The authors recommend examining the original structure using different sample categories and approaches (e.g., criterion validity) in the Indonesian sample.

Predicting Bone Adaptation in Astronauts during and after Spaceflight.

A method was previously developed to identify participant-specific parameters in a model of trabecular bone adaptation from longitudinal computed tomography (CT) imaging. In this study, we use these numerical methods to estimate changes in astronaut bone health during the distinct phases of spaceflight and recovery on Earth. Astronauts (N = 16) received high-resolution peripheral quantitative CT (HR-pQCT) scans of their distal tibia prior to launch (L), upon their return from an approximately six-month stay on the international space station (R+0), and after six (R+6) and 12 (R+12) months of recovery. To model trabecular bone adaptation, we determined participant-specific parameters at each time interval and estimated their bone structure at R+0, R+6, and R+12. To assess the fit of our model to this population, we compared static and dynamic bone morphometry as well as the Dice coefficient and symmetric distance at each measurement. In general, modeled and observed static morphometry were highly correlated (R2> 0.94) and statistically different (p < 0.0001) but with errors close to HR-pQCT precision limits. Dynamic morphometry, which captures rates of bone adaptation, was poorly estimated by our model (p < 0.0001). The Dice coefficient and symmetric distance indicated a reasonable local fit between observed and predicted bone volumes. This work applies a general and versatile computational framework to test bone adaptation models. Future work can explore and test increasingly sophisticated models (e.g., those including load or physiological factors) on a participant-specific basis.

Nuclear Quantum Effects Made Accessible: Local Density Fitting in Multicomponent Methods.

The simulation of nuclear quantum effects (NQEs) is crucial for an accurate description of systems and processes involving light nuclei, such as hydrogen atoms. Within the last years, the importance of those effects has been highlighted for a vast range of systems with tremendous implications in chemistry, biology, physics, and materials sciences. However, while electronic structure theory methods have become routine tools for quantum chemical investigations, there is still a lack of approaches to address NQEs that are computationally accessible and straightforward to use. To address this, we present the first combination of the nuclear-electronic orbital Hartree-Fock approach with both local and density fitting approximations (LDF-NEO-HF). This results in a low-order scaling approach that enables the inclusion of NQEs for large systems within a fraction of a day and for small to medium size systems in minutes. Moreover, we demonstrate the qualitative accuracy and robustness of our approach to retrieve NQEs for three real-use cases motivated by chemical, biological, and materials science applications.

A community partitioning algorithm for cyberspace

Community partitioning is an effective technique for cyberspace mapping. However, existing community partitioning algorithm only uses the topological structure of the network to divide the community and disregards factors such as real hierarchy, overlap, and directionality of information transmission between communities in cyberspace. Consequently, the traditional community division algorithm is not suitable for dividing cyberspace resources effectively. Based on cyberspace community structure characteristics, this study introduces an algorithm that combines an improved local fitness maximization (LFM) algorithm with the PageRank (PR) algorithm for community partitioning on cyberspace resources, called PR-LFM. First, seed nodes are determined using degree centrality, followed by local community expansion. Nodes belonging to multiple communities undergo further partitioning so that they are retained in the community where they are most important, thus preserving the community’s original structure. The experimental data demonstrate good results in the resource division of cyberspace.

Inference of transmission dynamics and retrospective forecast of invasive meningococcal disease.

The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.

Approaches to learning mathematics: preliminary evidence of a concise, valid, and reliable instrument.

We assess students' approaches to learning mathematics not only to predict students' learning outcomes but also for its crucial utilities in the teaching and learning process. These utilities range from evaluating effective instructional interventions, determining students with learning difficulties, and comparing teaching and learning experience in higher education. However, measures of the constructs have raised validity concerns among researchers. A root cause of these validity concerns is traceable to the failure of these measures to account for the content-specificity of approaches to learning. Building on a previously developed general measure of the constructs, I designed this study to bridge this gap by developing and validating approaches to learning mathematics questionnaire (ALMQ). 352 first-year engineering students who gave voluntary consent participated in the study. The students were mainly males with ages ranging from 15 years to 29 years. The average age was 20.67 years, and its standard deviation was 2.164. I analysed the generated data using confirmatory factor analysis and judged the consistency of hypothesised models with the generated data using a combination of criteria. The findings revealed a two-factor ALMQ with seven items which demonstrated an excellent global and local fit of the generated data. The standardised factor loadings for all the items were above 0.68 with an average of 0.73 showing the high strengths of the items in measuring their respective constructs. I also found a reliability coefficient of 0.81 for deep approaches, 0.77 for surface approaches, and 0.72 for the two-factor ALMQ. These findings suggest preliminary evidence of the validity and reliability of ALMQ. I discussed the practical implications of the findings for educators, policymakers, and researchers interested in improving the mathematics learning experience.

Fluctuating selection maintains distinct species phenotypes in an ecological community in the wild

Species' phenotypic characteristics often remain unchanged over long stretches of geological time. Stabilizing selection-in which fitness is highest for intermediate phenotypes and lowest for the extremes-has been widely invoked as responsible for this pattern. At the community level, such stabilizing selection acting individually on co-occurring species is expected to produce a rugged fitness landscape on which different species occupy distinct fitness peaks. However, even with an explosion of microevolutionary field studies over the past four decades, evidence for persistent stabilizing selection driving long-term stasis is lacking. Nonetheless, biologists continue to invoke stabilizing selection as a major factor explaining macroevolutionary patterns. Here, by directly measuring natural selection in the wild, we identified a complex community-wide fitness surface in which four Anolis lizard species each occupy a distinct fitness peak close to their mean phenotype. The presence of local fitness optima within species, and fitness valleys between species, presents a barrier to adaptive evolutionary change and acts to maintain species differences through time. However, instead of continuously operating stabilizing selection, we found that species were maintained on these peaks by the combination of many independent periods among which selection fluctuated in form, strength, direction, or existence and in which stabilizing selection rarely occurred. Our results suggest that lack of substantial phenotypic evolutionary change through time may be the result of selection, but not persistent stabilizing selection as classically envisioned.

Saliency-based variational active contour model for image with intensity inhomogeneity

&lt;span&gt;Variational active contour model (ACM) is used to segment or subdivide an image into the desired object. This segmentation technique in region-based ACM can be divided into two classes: global segmentation and selective segmentation. Selective segmentation, in which only a particular desired object is segmented from an input image, is preferable to the global model because the selective segmentation model proves to be very useful, especially in medical image analysis. However, when it comes to segmenting an image with inhomogeneous intensity, these models seem to give unsatisfactory results. In this paper, we propose a new variational selective ACM mainly used for segmentation of images with inhomogeneous intensity, by incorporating saliency image map and local image fitting ideas. In addition, the euler-lagrange equation (EL) was provided to solve the proposed model. A total of thirty sets of medical images were used to test the model. Numerical results show that the suggested model outperforms other existing models, with the hausdorff distance of the proposed model being 47.78% less than the competing model, and the dice and jaccard values being around 17.54% and 33.65% higher, respectively, than the competing model.&lt;/span&gt;

Local image fitting-based active contour for vector-valued images

&lt;span&gt;Variational active contour seeks to segment or extract desired object boundaries for further analysis. The model can be divided into global segmentation and selective segmentation. Selective segmentation, which focuses on segmenting a particular object, is preferable to the global model. Recently, a number of selective segmentation models have been developed to precisely extract an object on grayscale images. Nevertheless, if the input image is vector-valued (colour), these models merely convert it to a grayscale image, resulting in data loss owing to the reduction in image dimension. Furthermore, they may have poor segmentation performance due to the intensity inhomogeneous images. Therefore, a new model on variational selective active contour for segmenting vector-valued images has been proposed that incorporates the concepts of local image fitting and distance-based fitting terms into a variational minimization energy functional. Moreover, a Gaussian function was used as a regularizer to replace the computationally expensive Total Variation term. Then, the proposed model’s Euler Lagrange equation has been provided to solve the model. When segmenting an object in inhomogeneous intensity images, the result of the proposed model was about 30% more accurate based on the Jaccard value and about 3 times faster than other existing methods.&lt;/span&gt;

Absorbing Markov Chain Saliency Driven Active Contour Model for Digital Image Boundary Extraction

The image segmentation approach using the active contour model (ACM) has achieved notable success in digital image analysis for extracting image boundaries. The region-based ACM can be divided into two classes: global segmentation and selective segmentation. Global segmentation, in which all desired boundaries of targeted objects are extracted from an input image, is preferable to the selective model due to its high utility, particularly in shape analysis. However, when segmenting a digital image with inhomogeneous intensity, the global ACM appears to produce unsatisfactory results. Thus, this research provides a new global ACM for segmenting digital images with an inhomogeneous intensity incorporating an absorption Markov chain (AMC) saliency image map and local image fitting principles. In addition, the Euler-Lagrange (EL) Partial Differential equation for solving the proposed model was provided. Thirty sets of digital images were used to validate the model. The accuracy of the proposed model, as indicated by Dice and Jaccard values, is approximately 3.81% and 10.63% higher, respectively, than that of the competing model, as determined by numerical analysis. In addition, the segmentation process taken by the proposed model is faster than the existing model. The proposed model has high potential to be extended into colour and multiphase formulations in future research.

Grease-lubricated triboelectric instantaneous angular speed sensor integrated with signal processing circuit for bearing fault diagnosis

The triboelectric sensor can be integrated with the rolling bearing to diagnose bearing faults by monitoring the periodic fluctuation of instantaneous angular speed, yet continuous dry friction between materials will reduce its signal-to-noise ratio (SNR). Moreover, the signal processing modules used by previous triboelectric sensors need additional wires to transmit the signal, and thus affect the reliability of the system. In this manuscript, a wireless grease-lubricated triboelectric instantaneous angular speed sensor (GL-TEIASS) integrated with the signal processing circuit for bearing fault diagnosis is proposed based on rotary freestanding triboelectric nanogenerators (TENGs). First, electrical insulating grease is presented as an ideal candidate lubricant used in the GL-TEIASS. Next, by establishing the charge density-dependent electromechanical model of grease-lubricated TENG, optimal structural parameters for maximum signal-to-noise ratio and angular resolution as well as minimum friction torque of the GL-TEIASS can be obtained. Moreover, the kurtosis-guided local polynomial fitting method is employed to extract the signal fluctuation caused by bearing faults. The results show that, compared to dry friction, the mass loss of the polymer under grease lubrication decreases by 91.7 % and the electrical output increases by 4.0 times. The durability of the GL-TEIASS in fault diagnosis is improved by at least 2.8 times compared with that of the sensor operating under dry friction. The error between the angular speed measured by the GL-TEIASS and a commercial sensor is less than 0.731 %, and the SNR of the output signal from in-situ monitoring by the GL-TEIASS can reach 39.1 dB, which is 4.9 times higher than that of vibration signal commonly used in fault diagnosis. The designed GL-TEIASS has good application prospects in self-sensing and self-diagnosis of operation state for smart bearings.