Density Model Research Articles

Supplemental structured surveys and pre-existing detection models improve fine-scale density and population estimation with opportunistic community science data

Density and population estimates aid in conservation and stakeholder communication. While free and broadly available community science data can effectively inform species distribution models, they often lack the information necessary to estimate imperfect detection and area sampled, thus limiting their use in fine-scale density modeling. We used structured distance-sampling surveys to model detection probability and calculate survey-specific detection offsets in community science models. We estimated density and population for 16 songbird species under three frameworks: (1) a fixed framework that assumes perfect detection within a specified survey radius, (2) an independent framework that calculates offsets from an independent source, and (3) a calibration framework that calculates offsets from supplemental surveys. Within the calibration framework, we examined the effects of calibration dataset size and data pooling. Estimates of density and population size were consistently biased low in the fixed framework. The independent and calibration frameworks produced reliable estimates for some species, but biased estimates for others, indicating discrepancies in detection probability between structured and community science surveys. The calibration framework produced reliable population estimates with as few as 10 calibration surveys with positive detections. Data pooling dramatically decreased bias. This study provides conservationists and managers with a cost-effective method of estimating density and population.

The electron density at the midpoint of the plasmapause

The high density plasmasphere in the magnetosphere is often separated from a lower density region outside of the plasmasphere, called the plasmatrough, by a sharp gradient in electron density called the plasmapause. Here we use plasmapause events identified from electron density data from the ISEE, CRRES, Polar, and IMAGE missions, and the nonlinear genetic algorithm TuringBot, to find models for the electron density at the midpoint of the plasmapause, ne,pp. A good model for ne,pp should include dependence on L, which is the strongest dependence. But models can be improved by including weaker dependencies on the magnetic local time, MLT, the solar EUV index F10.7, and geomagnetic activity as indicated by averages of Kp and AE. The most complicated model that we present predicts ne,pp within a factor of 1.64, and is within the range of observed plasmapause densities for about 96% of our events. These models can be useful for separating plasma populations into plasmasphere-like and plasmatrough-like populations. We also make available our database of electron density measurements categorized into various populations.

Prediction model for low bone mass mineral density in type 2 diabetes: an observational cross-sectional study.

Considering the prevalence of type 2 diabetes (T2D), osteoporosis should be considered a serious complication. However, an effective tool for the assessment of low bone mass mineral density (BMD) in T2D patients is not currently available. Therefore, the aim of our study was to establish a simple-to-use risk assessment tool by exploring risk factors for low BMD in T2D patients. This study included 436 patients with a low BMD and 381 patients with a normal BMD. Multiple logistic regression analysis was performed to evaluate risk factors for low BMD in T2D patients. A nomogram was then developed from these results. A receiver operating characteristic (ROC) curve, calibration plot, and goodness-of-fit test were used to validate the nomogram. The clinical utility of the nomogram was also assessed. Multivariate logistic regression indicated that age, sex, education, body mass index (BMI), fasting C-peptide, high-density cholesterol (HDL), alkaline phosphatase (ALP), estimated glomerular filtration rate (eGFR), and type I collagen carboxy terminal peptide (S-CTX) were independent predictors for low BMD in T2D patients. The nomogram was developed from these variables using both the unadjusted area under the curve (AUC) and the bootstrap-corrected AUC (0.828). Calibration plots and the goodness-of-fit test demonstrated that the nomogram was well calibrated. The nomogram-illustrated model can be used by clinicians to easily predict the risk of low BMD in T2D patients. Our study also revealed that common factors are independent predictors of low BMD risk. Our results provide a new strategy for the prediction, investigation, and facilitation of low BMD in T2D patients.

Microbial Fuel Cells Performance Evaluation through the Application of Fuzzy Logic

Objective: This work aimed to employ fuzzy logic combined with the design of experiments technique to statistically evaluate how the microbial fuel cells operating parameters influence their performance. Theoretical Framework: Microbial fuel cells (MFC) are a technology of interest in the current scenario as they allow simultaneously promoting the biotreatment of waste and the bio generation of electrical energy. Methodology: Through a bibliographical search based on publications on Google Scholar platform over the last 10 years, it was noticed that anode area, external electrical resistance and reactor volume are the most reported input parameters in MFC research and current density and power density are the output parameters most frequently portrayed in these studies, which is why these variables were selected for statistical investigation. Results and Discussion: The results showed that, for both outputs studied, reactor volume and anode area showed a positive effect, while the external electrical resistance showed a negative effect. It was also possible to develop mathematical models that indicated the relationship between the input and output variables studied, with statistical significance for power density model (R2 = 86%). Originality: Applying computational simulations and subsequent design of experiments to obtain results in accordance with those obtained experimentally in the laboratory.

127 Utilizing predictive models for bone mineral content and bone mineral density as a tool to assess dietary phosphorous

Abstract Dietary phosphorous is a necessary nutrient for bone development in pigs at all stages of its growing. Deficiencies in phosphorous decrease bone mineral content (BMC) and bone mineral density (BMD) and may be a causative of lameness. However, the normal range expected in whole body (WB) and individual bones for BMC and BMD in commercial pigs has been poorly defined. Therefore, our objectives were to 1) model normal WB bone density at multiple timepoints in wean to finish pigs, and 2) determine the extent to which individual bone (femur and 3rd metatarsal) BMC and BMD could predict WB BMC and BMD. Mixed sex pigs [Camborough (1050) x 337, PIC] were utilized and included freshly weaned ~21 d (n = 8), ~44 d of age (n = 24), ~84 d of age (n = 24) and ~154 d of age (n = 24). Pigs were individually penned. Excluding weaning age, at each age group pigs were randomly assigned to diets adequate, marginally deficient, or deficient in available phosphorus (avP) and fed for 3, 4, and 6 wk (age group 44, 84, 154, respectively). Within each age group, diets were formulated to meet nutrient and energy requirements except for avP and pigs fed ad libitum. On-test and off-test body weight (BW) and feed intake and gain to feed ratio were assessed for each age group. At the end of each feeding period, all pigs were euthanized and WB, femur (F) and 3rd metatarsal (M) BMC and BMD determined by dual X-ray absorptiometry (DXA). Data were analyzed using linear and generalized additive models that predicted response variables (BMC or BMD) as a function of BW or age. Only pigs fed adequate avP diets were used in the model fit. Pigs fed adequate avP diets increased BMD with age (0.22, 0.42, 0.84 and 1.10 g/cm2, days of age 21, 44, 84 and 154 respectively, P < 0.001). Within day of age 84, marginally and deficient avP diets reduced BMC and BMD by 21 to 41% (P = 0.002) and 12 to 25% (P = 0.006), respectively. Within day of age 154, marginally and deficient avP diets reduced BMC and BMD by 9% (P = 0.05) and 6 to 8% (P = 0.06), respectively. Body weight record and individual bone (F and M) BMC and BMD from pigs fed deficient avP diets were used to predict WB BMC and BMD and the mean square error of prediction was selected as a criterion to evaluate the ability of the models to detect these inadequately fed pigs. Using the selected models, pigs fed nutritionally deficient diets were detected due to 2- to 7-fold increase in MSE of predicted WBMD or WBMC compared with pigs fed adequate diets. These models can be used to assess bone development and strength in growing pigs and to infer the adequacy of avP level supplied in their diets.

Minimizing robust density power-based divergences for general parametric density models

Minimizing robust density power-based divergences for general parametric density models

Differential responses in water-use strategies of evergreen (Ligustrum lucidum) and deciduous (Robinia pseudoacacia) trees to tunnel excavation in a subtropical karst trough valley

Tunnel(s) excavation could decrease groundwater levels and soil water storage and consequently change trees growth conditions in karst areas. However, the adaptation mechanisms of different tree species to altered hydrological conditions caused by tunnel(s) construction in karst areas remain unknown. Here, we investigated the water-use strategies of two different tree species (the evergreen Ligustrum lucidum and deciduous Robinia pseudoacacia) in response to tunnel excavation in a typical subtropical karst trough valley by using the sap flow density, stable isotopes (δ13C, δ2H, and δ18O) and MixSIAR models. Tunnel excavation resulted in a 3.7–6.3 % reduction in soil moisture content (SMC) in Zhongliang Mountain Karst Trough (ZMKT) Valley. Both tree species demonstrated reduced transpiration in tunnel-affected valley (TAV) compared to tunnel-unaffected valley (TUAV) (transpiration in TAV was only 77.6 % of TUAV), with evergreen trees exhibiting a smaller decline in transpiration compared to deciduous trees. Furthermore, the proportions of karst groundwater utilized by evergreen and deciduous tree species in TAV were 8.3 % and 5.0 % higher than in TUAV, respectively, with evergreen tree species exhibiting greater absorption of karst groundwater than deciduous tree species. Meanwhile, the average leaf δ13C values and WUEi (intrinsic water-use efficiency) of both tree species in TAV were 0.9 ‰ and 15.7 μmol/mol higher than those in TUAV, respectively, with evergreen trees exhibiting a smaller increase in WUEi compared to deciduous trees. These results indicate that tunneling-induced water stress events may prompt trees to adopt more conservative water-use strategies. Evergreen tree species rely on more karst groundwater absorption to sustain relatively higher transpiration rates, and deciduous trees adapt to severe water stress by increasing their WUEi or shedding leaves to minimize transpiration. Our finding contributes to the evaluation of eco-hydrological processes in karst areas affected by tunnel(s) excavation.

Evolution of Subsonic Shock Waves Associated with Reconnection Jets in Earth’s Magnetotail

Motivated by the signatures of nonlinear electrostatic waves observed by the Magnetospheric Multiscale spacecraft mission in reconnection jet regions of Earth's magnetotail, we have explored the dynamical features of ion-acoustic shock waves in the magnetotail. In this investigation, we have examined the dynamics and characteristics of ion-acoustic subsonic shock waves in non-Maxwellian space plasma comprising of two counterstreaming ion beams with suprathermal electrons, assumed to follow a kappa (κ) distribution. A reductive perturbation technique has been adopted to establish an evolution equation for small amplitude electrostatic shock structures. Importantly, subsonic waves only exist when the beam velocity exceeds a certain threshold, beyond which supersonic and subsonic waves may coexist. The combined effects of the beam velocity and the non-Maxwellian electron statistics have been analyzed to examine the characteristics of subsonic shock waves. Both symmetric and asymmetric (in relative beam density) models have been considered, leading to distinct possibilities in the evolution of subsonic shock waves. The findings of the investigation will help unfold the relatively unexplored dynamical characteristics of subsonic shock waves that may form and propagate in the magnetosphere.

Forecast cosmological constraints from the number counts of Gravitational Waves events

We present a forecast for the upcoming Einstein Telescope (ET) interferometer with two new methods to infer cosmological parameters. We consider the emission of Gravitational Waves (GWs) from compact binary coalescences, whose electromagnetic counterpart is missing, namely Dark Sirens events. Most of the methods used to infer cosmological information from GW observations rely on the availability of a redshift measurement, usually obtained with the help of external data, such as galaxy catalogues used to identify the most likely galaxy to host the emission of the observed GWs. Instead, our approach is based only on the GW survey itself and exploits the information on the distance of the GW rather than on its redshift. Since a large dataset spanning the whole distance interval is expected to fully represent the distribution, we applied our methods to the expected ET's far-reaching measuring capabilities. We simulate a dataset of observations with ET using the package darksirens, assuming an underlying ΛCDM cosmology, and including the possibility to choose between three possible Star Formation Rate density (SFR) models, also accounting for possible population III stars (PopIII). We test two independent statistical methods: one based on a likelihood approach on the theoretical expectation of observed events, and another applying the cut-and-count method, a simpler method to compare the observed number of events with the predicted counts. Both methods are consistent in their final results, and also show the potential to distinguish an incorrect SFR model from the data, but not the presence of a possible PopIII. Concerning the cosmological parameters, we find instead that ET observations by themselves would suffer from strong degeneracies, but have the potential to significantly contribute to parameter estimation if used in synergy with other surveys.

Molecular dynamics investigations on the thermophysical property of fatty acid ethyl esters

To develop innovative fuel additives or substitutes and design spray-assisted internal combustion engines, it is important to have a comprehensive understanding of the thermophysical characteristics of biodiesels. Rooted in statistical mechanics, the molecular simulation method can predict the thermophysical properties over a wide range of temperatures and pressures, which overcomes the challenges of experimental measurements and facility restrictions. This work focused on the evaluation of three classical force fields (FFs) in terms of their ability to predict the thermophysical properties of ethyl-caprylate (EtC8:0), ethyl-caprate (EtC10:0), ethyl-laurate (EtC12:0), ethyl-myristate (EtC14:0), ethyl-palmitate (EtC16:0), and ethyl-stearate (EtC18:0) within a temperature range of (303.15 to 403.15) K and pressures up to 100 MPa. The Optimized Potential for Liquid Simulations–All atom and its modification (OPLS and L–OPLS) and Transferable Potential for Phase Equilibria–United Atom (TraPPE–UA) FFs were evaluated. These findings demonstrated that the L–OPLS FF, when applied in the isobaric-isothermal (NPT) and canonical ensembles (NVT), effectively replicated the experimental data and semi-empirical models for density, viscosity, diffusivity coefficient, and interfacial behavior at elevated temperatures and pressures. Moreover, the utilization of the radial distribution function, radius of gyration, and end-to-end distance distribution in the analysis of liquid conformations allowed for a comprehensive investigation of the microscopic structure. This work provides the theoretical methodologies and empirical evidence for the advancement of biodiesel and other petrochemicals.

Optimal analysis of the proportion between modified lime mud and active mineral additive in mortar: Insights from particle size distribution and packing density modeling

The use of lime mud in building materials is becoming more popular. However, its application has been limited by optimizing the proportion of lime mud and other active admixtures. In this study, the lime mud was firstly modified by a mechanochemical method to refine the grain size. Secondly, various particle size distribution functions were used to described the particle size distribution of cementitious materials. Further, based on the modifier Anderson and Andreasen (MAA) function, the optimal mixing proportion of cementitious materials was designed, and its feasibility was confirmed by the orthogonal test and range analysis. According to the hydration analysis through X-Ray Diffraction analysis (XRD), Thermogravimetry-Derivative Thermogravimetry (TG-DTG), Scanning Electron Microscope (SEM), and Mercury Intrusion Porosimetry (MIP), a significant increase in the harmless pore content was observed in the mortar with the optimal blend of MLM, MK, and SF.

Tomographic imaging of South Rewa basin, Central India: Implications of Gondwana rifting and Late Cretaceous volcanism

We have imaged thick (3.0–5.0 km) sub-trappean Gondwana sediments deposited in a typical rift-environment of the south Rewa basin in Central India masked by Deccan basalts. The basalts intrude through the basin forming several dykes, sills, and other intrusions along with large-scale undulations of the basement showing complex geology and tectonic settings due to Late Cretaceous volcanism (~65 Ma). To delineate complex subsurface geological structures and understand the tectonic settings, we use long-offset seismic reflection data along the N-S trending 127 km Kuvari-Shahdol profile of the basin. The conventional stack and pre-stack time migration techniques fail to image the complex subsurface structures dominated by steeply dipping faults. The steeply dipping faults with small-scale structures are influenced by strong lateral and vertical velocity variations that cause problems in imaging the basin. Hence, a robust tomographic inversion with pre-stack depth migration technique is adopted to image fine-scale subtle subsurface geological structures with smooth velocity variations. The tomographic velocity model and pre-stack depth migration seismic image could decipher basaltic trap thickness, numerous dyke intrusions, alternate horsts and grabens with deposition of thick hydrocarbon-bearing sub-trappean Gondwana rocks along with the basement configuration. The deep basinal faults with highly distorted basement structures represent the intense tectonic activity of this Gondwana rift-basin. The deep-seated hydrocarbon reservoir is discovered with an excellent trapping mechanism forming both pre-rift and syn-rift settings of the rift-basin. The seismic image is further constrained and corroborated by the inversion of residual Bouguer gravity anomaly data to obtain corresponding density model derived from tomographic velocity model. The large basement faults, horsts and grabens, several intrusives like dyke, sill, laccolith, and thick sub-trappean Gondwana formations with Deccan trap cover indicate complex geology and tectonic settings of south Rewa basin forming the Late Cretaceous Volcanic Province of India.

Three-dimensional density model of the mantle beneath the Ukrainian shield

Three-dimensional density model of the mantle beneath the Ukrainian shield

Simulation Method for the Impact of Atmospheric Wind Speed on Optical Signals in Satellite–Ground Laser Communication Links

To analyze the intensity of atmospheric turbulence in a satellite–ground laser communication link, it is important to consider the effect of increased atmospheric turbulence caused by wind speed. Atmospheric turbulence causes a change in the refractive index, which negatively impacts the quality and focusing ability of the laser beam by altering its phase front. To simulate the changes in amplitude and phase characteristics of laser beam propagation in atmospheric turbulence caused by wind speed, a transverse translation phase screen is used. To better understand and address the influence of atmospheric wind speed on the phase of optical signals in satellite–ground laser communication links, this paper proposes a Monte Carlo simulation method. This method utilizes the spatial and temporal variations in the refractive index in the atmosphere and integrates the principles of optical signal propagation in the atmosphere to simulate changes in the phase of optical signals under different wind speed conditions. By analyzing the variations in the received optical signal’s power, the Monte Carlo method is employed to simulate phase screens and logarithmic amplitude screens. Additionally, it models the probability density of the statistical behavior of received optical signal’s fluctuations, as well as the time autocorrelation coefficient of optical signals. This paper, under the coupling condition in satellite–ground laser communication links, conducted a Monte Carlo simulation experiment to analyze the characteristics of the optical signal’s fluctuations in the link and discovered that atmospheric wind speed affects the shape of the power spectral density model of the received optical signal. Increasing wind speed leads to a decrease in the time autocorrelation coefficient of the received optical signal and affects the coupling efficiency. The paper then used a cubic spline interpolation fitting method to verify the models of the power spectral density and the autocorrelation time coefficient of the optical signal. This provides a theoretical foundation and practical guidance for the optimization of satellite–ground laser communication systems.

Investigation of Acute Effects of Using Different Density Foam Roller Models on Hamstring Muscle Stiffness and Flexibility in Professional Soccer Players

It was aimed to examine the acute effects of the use of foam roller models of different densities on hamstring muscle stiffness and flexibility in professional soccer players. Hamstring muscle's stiffness were evaluated using the Myoton Pro device. The flexybility of the hamstring muscle were evaluated using with the unilateral sit-reach test on 21 male professional soccer players who played in Gaziantep Soccer Club at aged 18-40. Athletes were randomly divided into groups of seven to use soft, medium and hard foam rollers. The athletes applied the method consisting of two sets of one minute on the dominant hamstring muscle. Measurements were made twice, before the application (BA) and after the application (AA). The muscle stiffness measurement of the soccer players was determined BA mean of 16.26 N/m and AA mean of 16.17 N/m.. Flexibility measurement was determined BA mean of 28,36 cm and AA mean of 31,05 cm. It was found that the mean of pre-test and post-test did not differ statistically for muscle stiffness (p>0,05) according to measurement times, but statistically differed for flexibility (p0,05). It shows that foam roller application is important for increasing muscle flexibility, but there is no difference between foam roller models applied at different intensities in terms of removing muscle stiffness and increasing flexibility.

X-ray counterpart detection and γ-ray analysis of the supernova remnant G279.0+01.1 with eROSITA and Fermi-LAT

A thorough inspection of known Galactic supernova remnants (SNRs) along the Galactic plane with SRG/eROSITA yielded the detection of the X-ray counterpart of the SNR G279.0+01.1. The SNR is located just 1.5° above the Galactic plane. Its X-ray emission emerges as an incomplete, partial shell of ~3° angular extension. It is strongly correlated to the fragmented shell-type morphology of its radio continuum emission. The X-ray spatial morphology of the SNR seems to be linked to the presence of dust clouds in the surroundings. The majority of its X-ray emission is soft (exhibiting strong O, Ne, and Mg lines), and it occurs in a narrow range of energies between 0.3 and 1.5 keV. Above 2.0 keV the remnant remains undetected. The remnant’s X-ray spectrum is purely of a thermal nature. Constraining the X-ray absorption column to values which are consistent with optical extinction data from the remnant’s location favors nonequilibrium over equilibrium models. A nonequilibrium two-temperature plasma model of kT ~ 0.3 keV and kT ~ 0.6 keV, as well as an absorption column density of NH ~ 0.3 cm−2 describe the spectrum of the entire remnant well. Significant temperature variations across the remnant have been detected. Employing 14.5 yr of Fermi-LAT data, we carried out a comprehensive study of the extended giga-electronvolt source 4FGL J1000.0-5312e. By refining and properly modeling the giga-electronvolt excess originating from the location of the remnant, we conclude that the emission is likely related to the remnant itself rather than being colocated by chance. The remnant’s properties as determined by the X-ray spectra are consistent with the ~2.5 kpc distance estimates from the literature, which implies a source diameter of ~140 pc and an old age of >7 × 105 yr. However, if the source is associated with any of the pulsars previously considered to be associated with the SNR, then the updated nearby pulsar distance estimates from the YMW16 electron density model rather place the SNR at a distance of ~0.4 kpc. This would correspond to a ~20 pc linear size and a younger age of 104− < 7 × 105 yr, which would be more in line with the nonequilibrium state of the plasma.

Predicting Bulk Density for Agglomerated Raspberry Ketone via Integrating Morphological and Size Metrics Using Artificial Neural Networks

The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of raspberry ketone with different degrees of agglomeration and morphological metrics (particle shape descriptors and roughness descriptors) and size metrics (size descriptors) and developed an artificial neural network (ANN) prediction model for the tapped bulk density of raspberry ketone. Samples prepared under different conditions were sieved and remixed, the tapped bulk density of the particles was then measured, and the descriptor features of the particles were obtained by combining them with image processing. The dimensions of the variables were decreased by principal component analysis and variance processing. To overcome the hyperparameter estimation of the heuristic-based artificial neural networks, the network model architectures were optimized by a neural architecture search strategy combining two-objective optimization. The results demonstrated that the tapped bulk density of raspberry ketone products is not only related to the descriptors of particle size and shape but also has a non-negligible relationship with particle roughness descriptors. The performance of the optimal ANN model demonstrated that the model can well predict the tapped bulk density of raspberry ketone with different degrees of agglomeration. The ANN model obtained by extracting morphology and size metrics through online image analysis can be used to measure the tapped bulk density in real-time and has the potential to be used for developing model-based online process monitoring.

Magnetically driven relativistic jet in the high-redshift blazar OH 471

Understanding the mechanisms that launch and shape powerful relativistic jets from supermassive black holes (SMBHs) in high-redshift active galactic nuclei (AGNs) is crucial for probing the co-evolution of SMBHs and galaxies over cosmic time. We focus on the high-redshift ($z=3.396$) blazar OH 471 to explore the jet launching mechanism in the early Universe. Using multi-frequency radio monitoring observations and high-resolution Very Long Baseline Interferometry (VLBI) imaging over three decades, we studied the milliarcsecond structure and long-term variability of OH 471. Our spectral modeling of the radio flux densities revealed a synchrotron self-absorbed spectrum, indicating strong magnetic fields within the compact core. By applying the flux freezing approximation, we estimated the magnetic flux carried by the jet. We found that it reaches or exceeds theoretical predictions for jets powered by black hole spin energy via the Blandford-Znajek mechanism. This implies that OH 471 is in a magnetically arrested disk (MAD) state, where the magnetic flux accumulated near the horizon regulates the accretion flow, allowing for an efficient extraction of black hole rotational energy. Our study demonstrates the dominance of MAD accretion in powering the prominent radio flares and relativistic jets observed in the radio-loud AGN named OH 471. Statistical studies of larger samples of high-redshift AGNs will shed light on the role of MAD accretion in launching and accelerating the earliest relativistic jets.

Prediction of Density in Standing Trees of Various Wood Species in Natural Forests Using Near-infrared Spectroscopy

Density plays an important role as basic information for applying wood as construction materials. Recent years, the application of near-infrared spectroscopy as non-destructive testing (NDT) has been promising. Density prediction for standing trees in huge variation trees and species of natural forest needs to be investigated using NDT as of eco-green harvesting. The combination of density information and near-infrared spectroscopy is enabled to build a prediction model. This research applied increment cores sampling for density prediction analysis using near-infrared spectroscopy method. The research combined increment cores samples from multiple wood species to be analyzed in one chemometrics analysis of cross-validation partial least squares regression (CV-PLSR) to build a prediction model of density.  The research resulted coefficient of determination for cross-validation (R2CV) of 0.76 with number of latent variable (LV) 10 from the 1st derivative with 13 smoothing-point spectra and wavelength of 1200 – 1800 nm as the best prediction model. The result seemed sufficient enough with those number of LV for this small tube wood sampling of increment cores from multiple wood species. This research proved that building a prediction model for multiple wood species is possible to be done.

Objective QC for diffusion MRI data: Artefact detection using normative modelling

Abstract Diffusion MRI is a neuroimaging modality used to evaluate brain structure at a microscopic level and can be exploited to map white matter fibre bundles and microstructure in the brain. One common issue is the presence of artefacts, such as acquisition artefacts, physiological artefacts, distortions, or image processing-related artefacts. These may lead to problems with other downstream processes and can bias subsequent analyses. In this work, we use normative modelling to create a semi-automated pipeline for detecting diffusion imaging artefacts and errors by modelling 24 white matter imaging-derived phenotypes from the UK Biobank dataset. The considered features comprised four microstructural features (from models with different complexity such as fractional anisotropy and mean diffusivity from a diffusion tensor model and parameters from neurite orientation, dispersion, and density models), each within six pre-selected white matter tracts of various sizes and geometrical complexity (corpus callosum, bilateral corticospinal tract and uncinate fasciculus and fornix). Our method was compared to two traditional quality control approaches: a visual quality control protocol performed on 500 subjects and quantitative quality control using metrics derived from image pre-processing. The normative modelling framework proves to be comprehensive and efficient in detecting diffusion imaging artefacts arising from various sources (such as susceptibility induced distortions or motion), as well as outliers resulting from inaccurate processing (such as erroneous spatial registrations). This is an important contribution by virtue of this methods’ ability to identify the two problem sources (i) image artefacts and (ii) processing errors, which subsequently allows for a better understanding of our data and informs on inclusion/exclusion criteria of participants.