Regression Modeling Approach Research Articles

Rapid Zooplankton Assessment: Evaluating a Tool for Ecosystem‐Based Fisheries Management in the Large Marine Ecosystems of Alaska

ABSTRACTEcosystem‐based fisheries management (EBFM) remains an aspirational goal for management throughout the world. One of the primary limitations of EBFM is the incorporation of basic lower trophic level information, particularly for zooplankton, despite the importance of zooplankton to fish. The generation of zooplankton abundance estimates requires significant time and expertise to generate. The rapid zooplankton assessment (RZA) is introduced as a tool whereby nontaxonomic experts may produce rapid zooplankton counts shipboard that can be applied to management in near real time. Zooplankton are rapidly counted shipboard and placed into three broad groups of zooplankton relevant to higher trophic levels: large copepods (> 2 mm), small copepods (< 2 mm), and euphausiids. A Bayesian, hierarchical linear regression modeling approach was used to validate the relationship between RZA abundances and laboratory‐processed abundances to ensure the rapid method is a reliable indicator. Additional factors likely to impact the accuracy of the RZA abundance predictions were added to the initial regression model: RZA sorter, survey, season, and large marine ecosystem (Bering Sea, Chukchi/Beaufort Sea, and Gulf of Alaska). We tested models that included the random effect of sorter nested within survey, which improved fits for both large copepods (Bayes R2 = 0.80) and euphausiids (Bayes R2 = 0.84). These factors also improved the fit for small copepods when the fixed effect of season was also included (Bayes R2 = 0.65). Additional RZA data were used to predict laboratory‐processed abundances for each zooplankton category and the results were consistent with model training data: large copepods (Bayes R2 = 0.80), small copepods (Bayes R2 = 0.64), and euphausiids (Bayes R2 = 0.88). The Bayesian models were therefore able to predict laboratory‐processed abundances with an associated error when accounting for these fixed and random effects. To demonstrate the utility of zooplankton data in management, zooplankton time series from the Bering Sea shelf were shown to vary in relation to warm and cold conditions. This variability impacted commercially important fish, notably Walleye Pollock (Gadus chalcogrammus), and these time series were used by managers using a risk table approach. The RZA method provides a rapid zooplankton population estimation in near real time that can be applied to the management process quickly, thus helping to fill a gap in EBFM.

The Relationship Between Chief Financial Officer Gender and Investment Efficiency

Objective: This study aims to examine the relationship between the gender of the chief financial officer (CFO) and the efficiency of corporate investment, especially over-investment. Methods: The independent variable is the gender of the CFO and the dependent variable is over-investment. The research method is correlation type and multivariate regression using panel data with a logistic regression model approach. The data of manufacturing companies in the Tehran Stock Exchange has been collected from 2017 to 2021. Results: The findings of the research show that the presence of a female financial manager is significantly related to the reduction of the company's over-investment level, so gender plays a role in the economic decision-making and investment of companies. Conclusion: Female chief financial managers are more risk-averse compared to male chief financial managers and consider the interests of shareholders in the company’s economic decisions. Therefore, companies should pay special attention to gender diversity at the level of company managers in making decisions and economic investments.

Profitabilitas, Likuiditas dan Pertumbuhan Perusahaan pada Kebijakan Hutang Perusahaan Manufaktur yang Terdaftar di Bursa Efek Indonesia Periode Tahun 2018 – 2022

This research aims to determine and analyze the influence of profitability, liquidity and company growth on the debt policy of manufacturing companies listed on the Indonesia Stock Exchange for the 2018-2022 period. This type of research is quantitative research using secondary data obtained from the Indonesia Stock Exchange website. The total population is 226 companies and the sample is 25 companies with a total of 125 observations. Sampling used purposive sampling technique. The data analysis method uses Ordinary Least Square, with a panel data regression model approach and statistical t-test.The research results show that: Profitability/ROA has a negative and insignificant effect on the debt policy of manufacturing companies listed on the Indonesia Stock Exchange (BEI) for the 2018-2022 period. Liquidity/CR has a positive and significant effect on the debt policy of manufacturing companies listed on the Indonesia Stock Exchange (BEI) for the 2018-2022 period. Company growth / GROWTH has a positive and significant effect on the debt policy of manufacturing companies listed on the Indonesia Stock Exchange (BEI) for the 2018-2022 period.

Concentration of polycyclic aromatic hydrocarbons (PAHs) in bread and health risk assessment across the globe: A systematic review and meta-analysis.

Although bread is the principal food in most countries, polycyclic aromatic hydrocarbons (PAHs) may be present and pose a potential risk to consumers. The aim of this review is to provide a comprehensive report on the concentration and health risks associated with PAHs in bread around the world. Various databases, such as Scopus, PubMed, Science Direct, and Google Scholar, were searched from their beginnings until December 2023 for this systematic review, which included 34 potentially relevant articles with data relating to 1057 bread samples. Utilizing a multilevel regression modeling approach, the study evaluated various factors such as fuel type, bread type, and geographical location. Following the initial evaluation, in 26.47% and 20.28% of all studies, the levels of Bap and PAH4 were higher than the permissible limit values, respectively. Based on the isomer ratios, 55.88% of the studies associated the presence of PAHs in bread samples with pyrogenic/coal combustion sources. According to the carcinogenic risk results, bread consumers in all studies have been exposed to moderate or high levels of carcinogenicity. The most significant risk levels are associated with the consumption of bread in Egypt, Kuwait, Iran, and India. Moreover, meta-regression analysis demonstrated significantly higher toxicity equivalent quotient and cancer risk mean values in bread baked using fossil fuels compared to other sources (p<.05). The high concentrations of PAHs, especially Benzo[a]pyrene, in bread pose a serious public health risk. Stringent regulations and monitoring are crucial to reduce contamination. Further research is necessary to develop safe processing methods to remove PAHs in bread.

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa.

Multi-spectral imaging by unoccupied aerial vehicles provides a non-destructive, high throughput approach to measuring biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multi-spectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11-0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multi-spectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.

Correlation of the Walk Score and Environmental Perceptions with Perceived Neighborhood Walkability: The Quantile Regression Model Approach

The walk score, which is widely used as an index of walkability, does not include pedestrian’s perception, so there is a limit to explaining the level of perceived walkability in a neighborhood. The purpose of this study is to examine how an objectively measured walk score and subjectively measured environmental perceptions correlate with perceived neighborhood walkability. This study conducted a survey on 371 participants aged 18 or older living in Daegu, South Korea to examine perceived neighborhood walkability and perception of the built environment. We measured the walk score based on participants’ location using a geographic information system. We used the quantile regression model, whereby we investigated the effects of explanatory variables (e.g., the walk score, perceptions of the built environment) by classifying perceived neighborhood walkability by quantile into Q10, Q25, Q50, Q75, and Q90. The walk score had a positive association with people with low perceived neighborhood walkability (Q10), but a negative association with people with high perceived neighborhood walkability (Q90). Regarding views of the built environment, in most quantiles, people perceived the environment as walkable if there were abundant green spaces and diverse alternative routes. Conversely, odors, smoke, hills, and stairs impeded walkability. This indicates that along with an objective walkability index, perceptions of the built environment play an important role in determining perceived neighborhood walkability. This implies that our results can help identify appropriate policies to promote walkability for citizens.

Structural regression modelling of peptide based drug delivery vectors for targeted anti-cancer therapy.

Drug resistance in cancer poses a serious challenge in finding an effective remedy for cancer patients, because of the multitude of contributing factors influencing this complex phenomenon. One way to counter this problem is using a more targeted and dose-limiting approach for drug delivery, rather than relying on conventional therapies that exhibit multiple pernicious side-effects. Stability and specificity have traditionally been the core issues of peptide-based delivery vectors. In this study, we employed a structural regression modelling approach in the design, synthesis and characterization of a series of peptides that belong to approximately same topological cluster, yet with different electrostatic signatures encoded as a result of their differential positioning of amino acids in a given sequence. The peptides tagged with the fluorophore 5(6)-carboxyfluorescein, showed higher uptake in cancer cells with some of them colocalizing in the lysosomes. The peptides tagged with the anti-cancer drug methotrexate have displayed enhanced cytotoxicity and inducing apoptosis in triple-negative breast cancer cells. They also showed comparable uptake in side-population cells of lung cancer with stem-cell like properties. The most-optimized peptide showed accumulation in the tumor resulting in significant reduction of tumor size, compared to the untreated mice in in-vivo studies. Our results point to the following directives; (i) peptides can be design engineered for targeted delivery (ii) stereochemical engineering of peptide main chain can resist proteolytic enzymes and (iii) cellular penetration of peptides into cancer cells can be modulated by varying their electrostatic signatures.

Advancing Nuclear Energy Forecasting: Exploring Regression Modeling Techniques for Improved Accuracy

The urgent requirement for sustainable and dependable energy sources has stimulated an increased fascination with precisely forecasting nuclear energy generation. This work utilizes sophisticated regression modeling approaches, namely XGBoost, to predict nuclear energy generation by leveraging economic indices such as Gross Domestic Product (GDP). Each model’s prediction accuracy has been evaluated by examining historical data on nuclear energy output and GDP from various locations. Here, measures such as mean squared error (MSE) and coefficient of determination (R2) to analyze their effectiveness have been used. The results of this study demonstrate that the XGBoost model outperforms standard regression approaches, showing greater R2 values and lower MSE scores. Furthermore, the consequences of these discoveries for the development of energy policy offer possible directions for future study in energy forecasting. This study provides useful insights for energy planners and policymakers, enabling a more profound comprehension of the complex relationship between economic indicators and nuclear energy generation.

Project characteristic-based performance prediction model for school constructions: hierarchical regression approach

Project underperformance remains a significant concern in the construction industry. The majority of prior studies have focused on identifying the subjective factors that affect project performance. However, there are currently no in-depth studies evaluating the influence of project characteristics and managerial/organizational obstacles on construction project performance for design-bid-build projects using empirical data. Therefore, this study aims to present a predictive model that illustrates the correlation between project characteristics, managerial/organizational complexity and difficulties, and construction project performance. Project data relating to the construction of 101 public schools within a developing country was collected. Subsequently, the project characteristics were identified, and two performance indicators (schedule performance index and cost performance index) were calculated for each project. A survey was conducted with construction professionals who took part in all of the control and management processes of these school projects to evaluate their managerial and organizational difficulties. Hierarchical regression model approach and correlation analyses were employed to develop the predictor model. The results indicate that factors such as location, school type, and project duration significantly predict both the schedule performance and managerial and organizational difficulties in school construction. An indirect correlation, rather than a direct association, was found between schedule performance and managerial and organizational difficulties. The proposed model will be a helpful guide for construction professionals, engineering managers and government decision-makers seeking to improve the performance of "design-bid-build" school construction projects. We suggest integrating the qualification-based selection (QBS) system into traditional procurement methods for public investments.

Analysis of Economic Growth in East Nusa Tenggara Province Using Spatial Regression Model

This research aims to examine the significant factors influencing economic growth in the East Nusa Tenggara Province. The estimation is carried out through a spatial regression model approach. The research variables were selected based on the economic growth model proposed by Mankiw-Romer-Weil, namely Gross Regional Domestic Product at constant prices (Y), Labor ( X1), Electricity Consumption (X2 ), Regional Original Income (X3 ), Capital Expenditure ( X4), and Schooling Duration (X5 ). The research data consists of secondary data from the year 2022 in 22 regencies/cities obtained from the Central Statistics Agency (BPS) of East Nusa Tenggara Province. Modeling with the Ordinary Least Squares (OLS) regression method resulted in three significant independent variables at α=5%: local revenue, capital expenditure, and average years of schooling. Based on diagnostic tests, there was spatial dependence in lag and error, leading to the use of Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) for spatial regression. From these spatial models, the significant independent variables influencing economic growth in the 22 regencies/cities in East Nusa Tenggara were local revenue, capital expenditure, and average years of schooling. The queen contiguity weighting was used. Based on R2 and AIC criteria, the best spatial regression model was the Spatial Error Model (SEM) with the highest R2 of 0.837610 and the smallest AIC value of 393.03. For further research, it is recommended to consider local factors that may influence economic growth, such as the sustainability of the agricultural or tourism sector, which may vary in each region.

Developing a nomogram model for 3-month prognosis in patients who had an acute ischaemic stroke after intravenous thrombolysis: a multifactor logistic regression model approach

ObjectivesThis study is to establish a nomination graph model for individualised early prediction of the 3-month prognosis of patients who had an acute ischaemic stroke (AIS) receiving intravenous thrombolysis with...

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7ppm, 18.94 and 60.76µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

P-660 Finding the optimal starting dose of gonadotropins for an ovarian stimulation treatment through Machine Learning

Abstract Study question Can machine learning algorithms provide an individualized starting dose of gonadotropins to maximize the number of oocytes retrieved in patients undergoing ovarian stimulation ? Summary answer Methods of Policy Learning based on Causal Inference and Machine Learning optimize starting doses of gonadotropins with a substantial gain of oocytes under appropriate treatment. What is known already Despite attempts toward standardization, there has been no consensus about establishing the optimal starting dose of gonadotropins to be used in ovarian stimulation. In the real world, medical practice differs considerably across countries and even among fertility clinics nationally. The machine learning community has proposed a variety of models for doing this, but with limited comparisons, causal inference can provide the statistical tools to enable the best model to be chosen and allow its explicability. Study design, size, duration In a retrospective single-center study, we reviewed the data of 11,436 cycles of ovarian stimulation for IVF between 2012 and 2023. In order to estimate the causal effect of the starting dose of gonadotropins on the number of oocytes retrieved, we selected a relevant subset of confounding covariates – including age, Antral Follicle Count (AFC), Anti-Müllerian Hormone (AMH) and oestradiol (E2) rates. Participants/materials, setting, methods We used five different Machine Learning models : Linear Regression, Regression Forest, Super Learner (SL), Multi-Arm Causal Forest (MACF) and a model based on the Targeted Maximum Likelihood Estimation (TMLE). We employed these models for Policy Learning through an Outcome Regression Modeling approach. We compared the gain of oocytes obtained through the optimal policy of our different models and analyzed the variables which played a crucial role in determining the optimal dose. Main results and the role of chance We use two approaches to compare our models, the first one gives the expected average gain of oocytes under the optimal policy : in this case, the Linear Regression is the best with an average gain of 0.90 oocytes, followed by the MACF (0.80), the SL (0.58), the TMLE (0.44) and the Regression Forest (0.18). Considering the lack of heterogeneity of our database we use a second approach, an Augmented Inverse Propensity Score based value, to compare with more robustness the performance of the optimal policy of each model. In this case the MACF (an explainable model specifically designed for causal inference) is the best with a gain of 0.50 oocytes, followed by the SL (0.42), the Linear Regression (0.37), the TMLE (0.23) and the Regression Forest (0.18). In general, our models recommend lower doses, for instance 20% of the patients are recommended by the MACF to reduce their initial doses of gonadotropins by on average 100 IU. The most significant covariates in the models are the AFC, the BMI, and the rate of AMH, with p-values &amp;lt; 0.001 in the Linear Regression that has a R-squared of 0.418. Limitations, reasons for caution Full IVF history was not included in patients who had previous IVF attempts, but only data from the last previous attempt was considered. Also, the study is monocentric and more heterogeneity will be present considering other centers. Wider implications of the findings This study needs to be completed by data from different centers in France and around the world to bring diversity to the patients’ data and to the treatment prescriptions in order to improve external validity. Algorithm recommendations and their adherence need to be tested prospectively. Trial registration number not applicable

Relationship of companies’ characteristics to foreign ownership: Studies on emerging market stock exchange in seven countries from ASEAN and Middle East

This study explores the relationship between company’s characteristics and foreign ownership within the emerging market stock exchanges from 7 countries in the ASEAN and Middle East. The objective is to provide an understanding of determinants that influence foreign investors to invest in companies in emerging markets. This study uses panel data of publicly traded companies from Philippines, Thailand, Indonesia, Vietnam, UAE, Saudi Arabia, and Qatar within Q3 2020 – Q4 2022 period with the econometric robust regression model approach. Employing a quantitative research approach, this study utilizes a robust regression analysis. Various company characteristics, such as size, profitability, liquidity, leverage, dividend yield, and book-to-market value, are examined as potential determinants of foreign ownership. Results show that size has strong significant positive relationship to foreign ownership, profitability has positive not significant relationship to foreign ownership, liquidity has positive not significant to foreign ownership, leverage has negative weak significant to foreign ownership, dividend yield has positive significant to foreign ownership, and book-to-market has positive moderate significant to foreign ownership. The findings contribute to a deeper understanding of foreign ownership dynamics within the emerging market stock exchanges in the ASEAN region and Middle East, providing a solid foundation for informed decision-making and strategic planning.

Stacking regression model approach to mixed convection flow of ternary- nanofluid over slanted surface with magnetic field, waste discharge concentration, and joule heating effects

The current work is carried out to investigate the mixed convection flow behavior of ternary-based nanofluid over an inclined surface in the presence of a magnetic field, waste discharge concentration, and joule heating effects. The governing equations with proper assumptions were converted into ordinary differential equations (ODEs) form by implementing suitable similarity constraints and solved with Runge Kutta Fehlberg 4th 5th order and shooting scheme. The stacking regression model approach is implemented along with the Runge Kutta Fehlberg 4th 5th order technique. The obtained outcomes are presented with graphs, and model correctness is assessed using the combination of Gaussian Process Regression (GPR), Extra Tree Regression (ETR), and Random Forest (RF). The consistency and stability of the model are demonstrated by the closely linked testing and training data. The study outcomes show that the rise in the magnetic constraint will improve the velocity profile while improved values of local pollutant external source and external pollutant source variation parameters will enhance the concentration profile. Enhancement in Eckart number and solid volume fraction values will improve the rate of thermal distribution. Ternary nanofluid shows significant improvement than nanofluid. The surface drag force is increased about 8 %-9 % in assisting flow case and 8 %-17 % for opposing flow case for different values of magnetic parameter, the rate of thermal distribution improved from 75.54 % to 11.55 % in assisting flow case and 16.87 % to 25.70 % in opposing flow case for different values of Eckert number. The study's results might contribute to the advancement of more effective heat exchangers and cooling systems for electronics and engines. Exploration and extraction of oil, as well as the creation of efficient water purification systems.

On Least Squares Support Vector Regression for Predicting Mechanical Properties of Steel Rebars

Aiming at ensuring the quality of the product and reducing the cost of steel manufacturing, an increasing number of studies have been developing nonlinear regression models for the prediction of the mechanical properties of steel rebars using machine learning techniques. Bearing this in mind, we revisit this problem by developing a design methodology that amalgamates two powerful concepts in parsimonious model building: (i) sparsity, in the sense that few support vectors are required for building the predictive model, and (ii) locality, in the sense that simpler models can be fitted to smaller data partitions. In this regard, two regression models based on the Least Squares Support Vector Regression (LSSVR) model are developed. The first one is an improved sparse version of the one introduced in a previous work. The second one is a novel local LSSVR-based regression model. The task of interest is the prediction of four output variables (the mechanical properties YS, UTS, UTS/YS, and PE) based on information about its chemical composition (12 variables) and the parameters of the heat treatment rolling (6 variables). The proposed LSSVR-based regression models are evaluated using real-world data collected from steel rebar manufacturing and compared with the global LSSVR model. The local sparse LSSVR approach was able to consistently outperform the standard single regression model approach in the task of interest, achieving improvements in the average R2 from previous studies: 5.04% for UTS, 5.19% for YS, 1.96% for UTS/YS, and 3.41% for PE. Furthermore, the sparsification of the dataset and the local modeling approach significantly reduce the number of SV operations on average, utilizing 34.0% of the total SVs available for UTS estimation, 44.0% for YS, 31.3% for UTS/YS, and 32.8% for PE.

Assessment of spatial variation in lung cancer incidence and air pollutants: spatial regression modeling approach

ABSTRACT A notable finding is that Kerala’s capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas.

Soil-based assessment of site productivity for southern pine plantations in the coastal plain of the southeastern US: (I) loblolly pine

Site productivity and forest management strategies hinge on intrinsic site resources such as soil water and nutrient availability. In this study, regression tree model and linear regression model approaches were employed to quantify the relationship between site index and soil parameters for loblolly pine plantations in the Coastal Plain. Both the optimal regression tree model and the final linear regression model identified modified CRIFF (Cooperative Research in Forest Fertilization) soil groups and surface soil extractable phosphorus (Mehlich 1 P) as significant variables for predicting site index. The final linear regression model, with a R2 of 0.899 and an RMSE of 1.12 m, exhibited superior performance compared to the optimal regression tree model. Additionally, mean annual increments in stand volume and green weight for each soil group were simulated and compared with data from a long-term loblolly pine culture/density experimental study in the same region. These resulting predictive models offer applicability across diverse sites and are particularly well-suited for areas where forest stands are not currently established.

Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning-A Two-Year Follow-Up Cohort Study.

The early diagnosis and treatment of rheumatoid arthritis (RA) are essential to prevent joint damage and enhance patient outcomes. Diagnosing RA in its early stages is challenging due to the nonspecific and variable clinical signs and symptoms. Our study aimed to identify the most predictive features of hand ultrasound (US) for RA development and assess the performance of machine learning models in diagnosing preclinical RA. We conducted a prospective cohort study with 326 adults who had experienced hand joint pain for less than 12 months and no clinical arthritis. We assessed the participants clinically and via hand US at baseline and followed them for 24 months. Clinical progression to RA was defined according to the ACR/EULAR criteria. Regression modeling and machine learning approaches were used to analyze the predictive US features. Of the 326 participants (45.10 ± 11.37 years/83% female), 123 (37.7%) developed clinical RA during follow-up. At baseline, 84.6% of the progressors had US synovitis, whereas 16.3% of the non-progressors did (p < 0.0001). Only 5.7% of the progressors had positive PD. Multivariate analysis revealed that the radiocarpal synovial thickness (OR = 39.8), PIP/MCP synovitis (OR = 68 and 39), and wrist effusion (OR = 12.56) on US significantly increased the odds of developing RA. ML confirmed these US features, along with the RF and anti-CCP levels, as the most important predictors of RA. Hand US can identify preclinical synovitis and determine the RA risk. The radiocarpal synovial thickness, PIP/MCP synovitis, wrist effusion, and RF and anti-CCP levels are associated with RA development.

Maternal genetic effects throughout the life of the dam in Pirenaica beef cattle. a random regression model approach

In beef cattle, dams play a crucial role in shaping the pre- and postnatal environment for the growth of their offspring. Acknowledging the substantial impact of maternal influence on the early development of calves, researchers utilize maternal animal models. These models take into account both maternal genetic and permanent environmental effects, operating under the assumption that these influences remain constant throughout the productive life of the cow. Nevertheless, it cannot be ruled out that these genetic and environmental effects may evolve throughout the lifespan of the cows. Therefore, this study aims to describe the changes in genetic and environmental maternal effects over the productive lifespan of cows. To accomplish this goal, we utilized random regression models, incorporating the age of the dam effect, maternal genetic effects, and environmental permanent effects using Legendre orthogonal polynomials. Additionally, the analytical model incorporated a covariate to adjust for the calf’s age at recording, a two-level sex effect, a random herd-year-season effect, and an additive direct genetic effect associated linked to the calf. The dataset comprised information from dams aged between 2 and 16 years, resulting in a final database that comprised weight records of 58 332 calves from 21 673 dams. The average weight at 90 days was 135.0 ± 39.3 kg, and the mean age of the dam at calving was 7.03 ± 3.41 years. We evaluated models incorporating 2, 3, 4, 5, and 6 orthogonal polynomials alongside the standard maternal animal model. Afterward, we selected the model with five orthogonal polynomials based on the Akaike Information Criteria. The Restricted Maximum Likelihood estimates within this model indicated a direct heritability of around 0.50, and a maternal heritability ranging between 0.15 and 0.25, exhibiting a consistent increase between 4, 5 to 13 years. The genetic correlation estimates between direct and maternal genetic effects remained stable at approximately −0.55 across the lifespan of the cows. Furthermore, maternal genetic correlations between different ages of the dam decreased to around 0.7 for more distant age points. The maternal permanent correlations were notably lower, occasionally even reaching negative values, suggesting variability in environmental influence on maternal effects over the productive lifespan of the cow. Finally, the model enables the prediction of breeding values for the maternal genetic effects of the cow across its lifespan, providing opportunities for innovative selection strategies on the maternal side.