Linear Statistical Models Research Articles

Generalizability of polygenic prediction models: how is theR2defined on test data?

Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the is a commonly used measure to evaluate prediction accuracy. While the is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Based on large-scale genotype data from the UK Biobank, we compare three definitions of the on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoproteinA, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Our analysis shows that the choice of the definition can lead to considerably different results on test data, making the comparison of values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the based on the mean squared prediction error(MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of can provide valuable complementary information. Awareness of the different definitions of the on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions

BackgroundDetecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge.ResultsHere, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs.ConclusionsHere, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP–SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer.Availability and implementation: https://github.com/scutdy/SSO/blob/master/SEEI.zip.

Volga German surnames and Alzheimer's disease in Argentina: an epidemiological perspective.

The N141I variant (PSEN1 gene) is associated with familial forms of early-onset Alzheimer's disease (AD) in descendants of Volga Germans, whose migration to Argentina is well documented. As a proxy for geographic origin, surnames can be a valuable tool in population studies. The 2015 Argentine Electoral Registry provided geographic data for 30,530,194 individuals, including 326,922 with Volga German surnames. Between 2005 and 2017, the Ministry of Health recorded 4,115,216 deaths, of which 17,226 were attributed to AD and related causes. The study used both diachronic and synchronic data to identify patterns of territorial distribution and co-spatiality, using Moran's I and generalised linear model statistics. The frequency of surnames of Volga German origin accounts for 43.53% of the variation in deaths from AD and three clusters of high non-random frequency were found. Almost 150 years later, people descending from the Volga migration remain highly concentrated and may have a different risk of developing AD. The identification of spatial patterns provides reliable guidance for medical research and highlights the importance of specific health policies for particular populations.

Service quality: perspective of people with type 2 diabetes mellitus and hypertension in rural and urban public primary healthcare centers in Iran

ObjectiveThis study aimed to assess the service quality (SQ) for Type 2 diabetes mellitus (T2DM) and hypertension in primary healthcare settings from the perspective of service users in Iran.MethodsThe Cross-sectional study was conducted from January to March 2020 in urban and rural public health centers in the East Azerbaijan province of Iran. A total of 561 individuals aged 18 or above with either or both conditions of T2DM and hypertension were eligible to participate in the study. The study employed a two-step stratified sampling method in East Azerbaijan province, Iran. A validated questionnaire assessed SQ. Data were analyzed using One-way ANOVA and multiple linear regression statistical models in STATA-17.ResultsAmong the 561 individuals who participated in the study 176 (31.3%) were individuals with hypertension, 165 (29.4%) with T2DM, and 220 (39.2%) with both hypertension and T2DM mutually. The participants’ anthropometric indicators and biochemical characteristics showed that the mean Fasting Blood Glucose (FBG) in individuals with T2DM was 174.4 (Standard deviation (SD) = 73.57) in patients with T2DM without hypertension and 159.4 (SD = 65.46) in patients with both T2DM and hypertension. The total SQ scores were 82.37 (SD = 12.19), 82.48 (SD = 12.45), and 81.69 (SD = 11.75) for hypertension, T2DM, and both conditions, respectively. Among people with hypertension and without diabetes, those who had specific service providers had higher SQ scores (b = 7.03; p = 0.001) compared to their peers who did not have specific service providers. Those who resided in rural areas had lower SQ scores (b = -6.07; p = 0.020) compared to their counterparts in urban areas. In the group of patients with T2DM and without hypertension, those who were living in non-metropolitan cities reported greater SQ scores compared to patients in metropolitan areas (b = 5.09; p = 0.038). Additionally, a one-point increase in self-management total score was related with a 0.13-point decrease in SQ score (P = 0.018). In the group of people with both hypertension and T2DM, those who had specific service providers had higher SQ scores (b = 8.32; p < 0.001) compared to the group without specific service providers.ConclusionStudy reveals gaps in T2DM and hypertension care quality despite routine check-ups. Higher SQ correlates with better self-care. Improving service quality in primary healthcare settings necessitates a comprehensive approach that prioritizes patient empowerment, continuity of care, and equitable access to services, particularly for vulnerable populations in rural areas.

A review of predictive uncertainty estimation with machine learning

Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users’ needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.

Greater Frequency of Cooking Dinner at Home and More Time Spent Cooking Are Inversely Associated With Ultra-Processed Food Consumption Among US Adults

BackgroundCooking at home has been promoted as a strategy to improve diet quality; however, the association between cooking behavior and ultra-processed food intake is unknown. ObjectiveThe objective of this study was to examine associations between frequency of cooking dinner at home and time spent cooking dinner with ultra-processed food intake. DesignCross-sectional, nationally representative data from the 2007-2010 National Health and Nutrition Examination Survey were analyzed. Participants/settingParticipants were 9,491 adults (20 years and older) in the United States. Main outcome measuresThe main outcome measure was the proportion of energy intake (averaged from two 24-hour dietary recalls) from the following 4 Nova food-processing groups: (1) unprocessed or minimally processed foods, (2) processed culinary ingredients, (3) processed foods, and (4) ultra-processed foods. Statistical analyses performedSeparate linear regression models examined associations between cooking frequency and time spent cooking dinner and proportion of energy intake from the 4 Nova food-processing groups, adjusting for sociodemographic characteristics and total energy intake. ResultsUltra-processed foods comprised >50% of energy consumed independent of cooking frequency or time spent cooking. Higher household frequency of cooking dinner and greater time spent cooking dinner were both associated with lower intake of ultra-processed foods (P trends < .001) and higher intake of unprocessed or minimally processed foods (P trends < .001) in a dose–response manner. Compared with cooking 0 to 2 times/wk, adults who cooked dinner 7 times/wk consumed a mean of 6.30% (95% CI –7.96% to –4.64%; P < .001) less energy from ultra-processed foods. Adults who spent more than 90 minutes cooking dinner consumed 4.28% less energy from ultra-processed foods (95% CI –6.08% to –2.49%; P < .001) compared with those who spent 0 to 45 minutes cooking dinner. ConclusionsCooking at home is associated with lower consumption of ultra-processed foods and higher consumption of unprocessed or minimally processed foods. However, ultra-processed food intake is high among US adults regardless of cooking frequency.

Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Dietary Intervention Favorably Influences Physical Functioning: The Women’s Health Initiative Randomized Dietary Modification Trial

BackgroundIn the Women’s Health Initiative Dietary Modification randomized trial, the dietary intervention reduced breast cancer mortality by 21% (P = .02) and increased physical activity as well. ObjectiveTherefore, the aim was to examine whether or not these lifestyle changes attenuated age-related physical functioning decline. DesignIn a randomized trial, the influence of 8 years of a low-fat dietary pattern intervention was examined through 20 years of cumulative follow-up. Participants and settingFrom 1993 to 1998, 48,835 postmenopausal women, ages 50 to 79 years with no prior breast cancer and negative baseline mammogram were randomized at 40 US clinical centers to dietary intervention or usual diet comparison groups (40 out of 60). The intervention significantly reduced fat intake and increased vegetable, fruit, and grain intake. Main outcome measuresIn post hoc analyses, physical functioning, assessed using the RAND 36-Item Short Form Health Survey, evaluated quality or limitations of 10 hierarchical physical activities. Longitudinal physical functioning, reported against a disability threshold (when assistance in daily activities is required) was the primary study outcome. Statistical analyses performedSemiparametric linear mixed effect models were used to contrast physical functioning trajectories by randomization groups. ResultsPhysical functioning score, assessed 495,317 times with 11.0 (median) assessments per participant, was significantly higher in the intervention vs comparison groups through 12 years of cumulative follow-up (P = .001), representing a reduction in age-related functional decline. The intervention effect subsequently attenuated and did not delay time to the disability threshold. Among women in the dietary intervention vs comparison groups, aged 50 to 59 years, who were physically inactive at entry, a persistent, statistically significant, favorable influence on physical functioning with associated delay in crossing the disability threshold by approximately a year was seen (P value for interaction = .007). ConclusionsIn the Women’s Health Initiative Dietary Modification randomized trial, a dietary intervention that significantly reduced breast cancer mortality also significantly reduced age-related functional decline through 12 years, which was attenuated with longer follow-up.

Simplified Whittle Estimators for Spectral Parameters of Stationary Linear Models with Tapered Data

Simplified Whittle Estimators for Spectral Parameters of Stationary Linear Models with Tapered Data

Genome-wide genetic architecture for plant maturity and drought tolerance in diploid potatoes.

Cultivated potato (Solanum tuberosum) is known to be highly susceptible to drought. With climate change and its frequent episodes of drought, potato growers will face increased challenges to achieving their yield goals. Currently, a high proportion of untapped potato germplasm remains within the diploid potato relatives, and the genetic architecture of the drought tolerance and maturity traits of diploid potatoes is still unknown. As such, a panel of 384 ethyl methanesulfonate-mutagenized diploid potato clones were evaluated for drought tolerance and plant maturity under field conditions. Genome-wide association studies (GWAS) were conducted to dissect the genetic architecture of the traits. The results obtained from the genetic structure analysis of the panel showed five main groups and seven subgroups. Using the Genome Association and Prediction Integrated Tool-mixed linear model GWAS statistical model, 34 and 17 significant quantitative trait nucleotides (QTNs) were found associated with maturity and drought traits, respectively. Chromosome 5 carried most of the QTNs, some of which were also detected by using the restricted two-stage multi-locus multi-allele-GWAS haploblock-based model, and two QTNs were found to be pleiotropic for both maturity and drought traits. Using the non-parametric U-test, one and three QTNs, with 5.13%-7.4% phenotypic variations explained, showed favorable allelic effects that increase the maturity and drought trait values. The quantitaive trait loci (QTLs)/QTNs associated with maturity and drought trait were found co-located in narrow (0.5-1kb) genomic regions with 56 candidate genes playing roles in plant development and senescence and in abiotic stress responses. A total of 127 potato clones were found to be late maturing and tolerant to drought, while nine were early to moderate-late maturing and tolerant to drought. Taken together, the data show that the studied germplasm panel and the identified candidate genes are prime genetic resources for breeders and biologists in conventional breeding and targeted gene editing as climate adaptation tools.

Statistical Analysis of Morphological Characteristics of Inconel 718 Formed by High Deposition Rate and High Laser Power Laser Cladding.

Laser cladding is one of the emerging additive manufacturing technologies and has been adopted in various industrial fields. In this study, the morphological characteristics of a single clad of Inconel 718 manufactured by coaxial laser cladding with high laser power from 4200 W to 5400 W, powder feeding rate from 25 g/min to 50 g/min, and cladding speed from 20 mm/s to 50 mm/s are studied. The cross-section of the melt pool is analyzed and classified by type into three types: shallow dilution, flat dilution, and fluctuating dilution. Nine parameters are designed to describe the morphological characteristics of the clad, and the corresponding linear regression models are developed to establish a quantitative relationship between the combined process parameters and morphological characteristics. The results indicate that the total area of the cross-section A, the clad area above the substrate Ac, the area of the molten substrate Am, the total height of the cross-section H, the height of the clad above the substrate hc, the penetration depth hm, the clad width W, the dilution ratio D, and the wetting angle θ are determined by complex coupling of energy input and mass accumulation, and they are proportional to PF0.4/V, P0.5F/V, P/F0.2/V0.4, P2F0.6/V, PF0.7/V, P2/F/V0.3, P/V0.8, P/FV0.2, and PF7/V0.8, respectively. The large linear regression coefficients and the analysis residuals indicate the high reliability of the statistical linear regression models. This work aims to provide a comprehensive understanding of the influence of the main processing parameters on the morphological characteristics of the clad, which is of great value in providing a reference and laying a basis for the practical application of laser cladding technology at a high deposition rate.

Impact of topographic factors on animal field pathings: Analysis and prediction of deer movement patterns

Understanding and tracking the complexities of animal movement patterns is of paramount importance in wildlife management, conservation efforts, and the sustainable use of natural resources. An infinite number of factors influence the movement path of animals within their respective habitats, including: the structure of the habitat, the availability of resources, the presence of natural predators, social memory, the topographic attributes of the environment, etc.Numerous studies have attempted to delineate the spatial boundaries of animal habitats by elucidating the complexities of their movement dynamics. These investigations have highlighted the profound impact of factors such as environmental topography and the presence of natural impediments and other anthropogenic structures on animal mobility, but very few have analyzed topographic factors at a fine three-dimensional spatial scale.This research focuses on a novel methodology for identifying animal trajectories at a fine scale and evaluating the influence of topographic factors on these trajectories, specifically of deer herds in southern Spain. To understand movement patterns, transects recorded in the field due to continued use by deer are analyzed. Topographical information was obtained in two steps: first with a graphical analysis of orthophotos for the incorporation of the sufficient data set. Secondly, the veracity of this data was verified using Global Positioning System (GPS) tracking technology. The integration of data from multiple sources with Geographic Information Systems (GIS) allowed the analysis to be automated. Next a statistical linear regression model, based on both the ascent and descent lengths and the total length of the path traveled, was designed to infer the trajectories between two designated points within the study area. Using topographical variables obtained in the study environment, such as the slope, the elevation difference (cumulative vertical distance), and the 3D length of the transect paths, the influence of these variables on the movement decisions of animals within their habitat is established in order to facilitate their subsequent prediction.Analytical tests of the trajectories have shown that the movement behavior of cervids is predictable. The results demonstrate the usefulness of the methodology presented which, by providing and collect valuable topographic information on movement and transit areas, can guide sustainable management practices for deer populations and their habitats.

Entropy-metric estimation of the small data models with stochastic parameters

The formalization of dependencies between datasets, taking into account specific hypotheses about data properties, is a constantly relevant task, which is especially acute when it comes to small data. The aim of the study is to formalize the procedure for calculating optimal estimates of probability density functions of parameters of linear and nonlinear dynamic and static small data models, created taking into account specific hypotheses regarding the properties of the studied object. The research methodology includes probability theory and mathematical statistics, information theory, evaluation theory, and stochastic mathematical programming methods. The mathematical apparatus presented in the article is based on the principle of maximization of information entropy on sets determined as a result of a small number of censored measurements of “input" and “output" entities in the presence of noise. These data structures became the basis for the formalization of linear and nonlinear dynamic and static models of small data with stochastic parameters, which include both controlled and noise-oriented input and output measurement entities. For all variants of the above-mentioned small data models, the tasks of determining the optimal estimates of the probability density functions of the parameters were carried out. Formulated optimization problems are reduced to the forms canonical for the stochastic linear programming problem with probabilistic constraints.

Advanced financial market forecasting: integrating Monte Carlo simulations with ensemble Machine Learning models

&lt;abstract&gt;&lt;p&gt;This paper presents a novel integration of Machine Learning (ML) models with Monte Carlo simulations to enhance financial forecasting and risk assessments in dynamic market environments. Traditional financial forecasting methods, which primarily rely on linear statistical and econometric models, face limitations in addressing the complexities of modern financial datasets. To overcome these challenges, we explore the evolution of financial forecasting, transitioning from time-series analyses to sophisticated ML techniques such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks. Our methodology combines an ensemble of these ML models, each providing unique insights into market dynamics, with the probabilistic scenario analysis of Monte Carlo simulations. This integration aims to improve the predictive accuracy and risk evaluation in financial markets. We apply this integrated approach to a quantitative analysis of the SPY Exchange-Traded Fund (ETF) and selected major stocks, focusing on various risk-reward ratios including Sharpe, Sortino, and Treynor. The results demonstrate the potential of our approach in providing a comprehensive view of risks and rewards, highlighting the advantages of combining traditional risk assessment methods with advanced predictive models. This research contributes to the field of applied mathematical finance by offering a more nuanced, adaptive tool for financial market analyses and decision-making.&lt;/p&gt;&lt;/abstract&gt;

Quality and redox state of bovine sperm cryopreserved with resveratrol use of resveratrol in bovine semen

AbstractThe use of antioxidants for semen preservation prevents oxidative damage caused by reactive oxygen species (ROS). One of the most promising natural antioxidants is resveratrol, a phytoalexin derived from plants, grapes, berries, peanuts and red wine. To evaluate the effect of resveratrol on the quality and redox status of cryopreserved bovine semen. Five bulls were subjected to electroejaculation to obtain 15 ejaculates. Each ejaculate was extended with a tris‐egg yolk‐glycerol‐based medium and divided into six aliquots supplemented with 0 (control), 10, 20, 30, 40 and 50 μM of resveratrol. Semen was frozen with liquid nitrogen vapours. Post‐thawing, motility and kinetics were evaluated using a computer‐assisted sperm analysis (CASA) system, membrane integrity using the hypoosmotic test (HOST), morphology by staining with eosin–nigrosin, sperm vitality by fluorescence microscopy with the SYBR14/IP probes. Total antioxidant capacity (TAC) was evaluated using the ABTS•+ assay and ROS was evaluated using spectrofluorimetry with the H2DCFDA probe. For the statistical analysis linear models were adjusted and means were compared using the Tukey test. All concentrations of resveratrol reduced post‐thawed motility and kinetics of sperm. Supplementation with 40 and 50 μM of resveratrol reduced sperm kinetics, and between 30 and 50 μM of resveratrol alterations in the sperm membrane and morphology were observed. However, using resveratrol at 50 μM increased TAC and at 20 μM, it reduced ROS production of cryopreserved bovine semen. Resveratrol appears to have a dose‐dependent effect in which higher doses produce greater sperm alterations, however, it can increase semen TAC during freezing. It is concluded that resveratrol can increase antioxidant capacity and reduce ROS production in cryopreserved bovine semen. However, its use between 10 and 50 μM reduces post‐thawing semen quality.

Usage of Statistical Techniques to Monitor the Performance of Wind Turbines

Calculating the yearly energy output, which keeps the balance between both the generation and consumption of electricity, is made easier with the use of wind energy production estimates for grid interfaces. Effective wind speed forecasting is crucial for achieving this goal. In this research, linear statistical models of prediction Generalized Autoregressive Score (GAS), GAS model with exogenous variable x (GASX), and Autoregressive Integrated Moving Average (ARIMA) are the models used to estimate wind speed accurately. Additionally, the modeling of non-linear time-series data has been done using the Non-Linear GASX (NLGASX) statistical predictive modeling method. Additionally, the REctified Linear Unit (RELU), Softmax, Hyperbolic Tangent (TANH), and Sigmoid modeling approaches are used to optimize the suggested NLGASX model. In comparison to existing models, the suggested optimized NLGASX approach performs significantly better. In order to anticipate wind power, the wind power curve modeling additionally takes wind speed as an input. The estimated wind power has been used to determine the yearly energy

Statistical Inference Methods for Clinical Medical Data with Missing and Truncated Data

Abstract In clinical medicine, due to some accidents will inevitably produce the situation of missing data, this study for its with missing and truncated data, the use of mathematical statistics methods for inference supplement. After classifying the types of incomplete data, the article utilizes the great likelihood and empirical likelihood to form a linear statistical model to infer such data. It verifies it through simulation experiments and example analysis. In the simulation experiment, for the case of the same missing probability, as the number of samples increases from 150 to 300, the bias, variance, and mean square error of this paper’s algorithm in parameter β 1 are reduced to 0.0122, 0.1435, and 0.1441, respectively. In the actual statistical inference analysis of cardiac disease and heart transplantation, the standard error of this paper’s method reduces by 0.0576 compared with that of CAA, and the inference The results are by the reality. In clinical medicine, this study proposes a practical statistical extrapolation method and a realization path for objective interpretation when incomplete data is present.

Effects of Exchange Rate on Performance of Equity Funds in Kenya

In recent years, Kenya has witnessed significant growth in both the quantity and scale of diversified equity mutual funds. Despite this expansion, their performance has not surpassed that of their benchmark, specifically the market return on a risk-adjusted basis. This assessment is based on various performance measures, including the Sharpe ratio. Poor performance of mutual funds in Kenya discourages individual and corporate investors, in addition to hindering the realization of Vision 2030. Investors believe that information on past performance can be used to predict future performance. The extent to which macro-economic variables affect the performance of mutual funds is unclear. Some scholars show a positive correlation, while others show a negative correlation between the variables. Other factors that had an impact on the overall economic climate, such as the COVID-19 pandemic, the war in Ukraine, and the uncertainty in Kenya's political environment as a result of its general elections, made the situation even more unpredictable. The purpose of this study was to examine the impact of the exchange rate on the performance of equity mutual funds in Kenya. The study adopted a descriptive and correlational research design. The target population was 23 equity funds licensed by the Capital Market Authority, of which eleven were sampled through a census. The study relied on secondary data from the Central Bank of Kenya, the Capital Markets Authority, and the Kenya National Bureau of Statistics between 2018 and 2022. Panel data analysis was used. Descriptive statistics, encompassing measures such as mean and standard deviation, were employed in the analysis. Additionally, inferential statistics, specifically panel regression, were conducted. A static linear panel model was constructed to elucidate the relationship between independent variables and the dependent variable, which in this context is financial performance. The data was organized and presented in tables. Notably, the results indicated a noteworthy negative relationship between the exchange rate and the financial performance of equity funds. The findings of this research are of great importance to policymakers, managers of equity funds, and the scholarly community at large. It will be of significance to researchers and future scholars who might need to refer to it and build on it for further research. The study recommended that equity fund managers should invest in both domestic and foreign portfolios to diversify the risk associated with exchange rates.

Augmentation of ACL Autograft Reconstruction With an Amnion Collagen Matrix Wrap and Bone Marrow Aspirate Concentrate: A Pilot Randomized Controlled Trial With 2-Year Follow-up.

It is theorized that the lack of a synovial lining after anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) contributes to slow ligamentization and possible graft failure. Whether graft maturation and incorporation can be improved with the use of a scaffold requires investigation. To evaluate the safety and efficacy of wrapping an ACL autograft with an amnion collagen matrix and injecting bone marrow aspirate concentrate (BMAC), quantify the cellular content of the BMAC samples, and assess 2-year postoperative patient-reported outcomes. Randomized controlled trial; Level of evidence, 2. A total of 40 patients aged 18 to 35 years who were scheduled to undergo ACLR were enrolled in a prospective single-blinded randomized controlled trial with 2 arms based on graft type: bone-patellar tendon-bone (BTB; n = 20) or hamstring (HS; n = 20). Participants in each arm were randomized into a control group who underwent standard ACLR or an intervention group who had their grafts wrapped with an amnion collagen matrix during graft preparation, after which BMAC was injected under the wrap layers after implantation. Postoperative magnetic resonance imaging (MRI) mapping/processing yielded mean T2* relaxation time and graft volume values at 3, 6, 9, and 12 months. Participants completed the Single Assessment Numeric Evaluation Score, Knee injury and Osteoarthritis Outcome Score, and pain visual analog scale. Statistical linear mixed-effects models were used to quantify the effects over time and the differences between the control and intervention groups. Adverse events were also recorded. No significant differences were found at any time point between the intervention and control groups for BTB T2* (95% CI, -1.89 to 0.63; P = .31), BTB graft volume (95% CI, -606 to 876.1; P = .71), HS T2* (95% CI, -2.17 to 0.39; P = .162), or HS graft volume (95% CI, -11,141.1 to 351.5; P = .28). No significant differences were observed between the intervention and control groups of either graft type on any patient-reported outcome measure. No adverse events were reported after a 2-year follow-up. In this pilot study, wrapping a graft with an amnion collagen matrix and injecting BMAC appeared safe. MRI T2* values and graft volume of the augmented ACL graft were not significantly different from that of controls, suggesting that the intervention did not result in improved graft maturation. NCT03294759 (ClinicalTrials.gov identifier).

Medium-term patient's satisfaction after primary total knee arthroplasty: enhancing prediction for improved care

BackgroundPatient-reported satisfaction after total knee arthroplasty (TKA) is low compared to other orthopedic procedures. Although several factors have been reported to influence TKA outcomes, it is still challenging to identify patients who will experience dissatisfaction five years after surgery, thereby improving their management. Indeed, both perioperative information and follow-up questionnaires seem to lack statistical predictive power. HypothesisThis study aims to demonstrate that machine learning can improve the prediction of patient satisfaction, especially when classical statistics fail to identify complex patterns that lead to dissatisfaction. Patients and methodsPatients who underwent primary TKA were included in a Registry that collected baseline data and clinical outcomes at different follow-ups. The patients were divided into satisfied and dissatisfied groups based on a satisfaction questionnaire administered five years after surgery. Satisfaction was predicted using linear statistical models compared to machine learning algorithms. ResultsA total of 147 subjects were analyzed. Regarding statistics, significant differences between satisfaction levels started emerging only six months after the intervention, and the classification was close to random guessing. However, machine learning algorithms could improve the prediction by 72% soon after the intervention, and an improvement of 178% was possible when including follow-ups up to one year. DiscussionThis study demonstrates the feasibility of a registry-based approach for monitoring and predicting satisfaction using ML algorithms. Level of evidenceIII.