Main Source Of Variation Research Articles

Comparison of Anomaly Detectors: Context Matters.

Deep generative models are challenging the classical methods in the field of anomaly detection nowadays. Every newly published method provides evidence of outperforming its predecessors, sometimes with contradictory results. The objective of this article is twofold: to compare anomaly detection methods of various paradigms with a focus on deep generative models and identification of sources of variability that can yield different results. The methods were compared on popular tabular and image datasets. We identified that the main sources of variability are the experimental conditions: 1) the type of dataset (tabular or image) and the nature of anomalies (statistical or semantic) and 2) strategy of selection of hyperparameters, especially the number of available anomalies in the validation set. Methods perform differently in different contexts, i.e., under a different combination of experimental conditions together with computational time. This explains the variability of the previous results and highlights the importance of careful specification of the context in the publication of a new method. All our code and results are available for download.

MP03-17 PATIENT, PHYSICIAN, HOSPITAL FACTORS ASSOCIATED WITH READMISSION AFTER RADICAL CYSTECTOMY

You have accessJournal of UrologyCME1 May 2022MP03-17 PATIENT, PHYSICIAN, HOSPITAL FACTORS ASSOCIATED WITH READMISSION AFTER RADICAL CYSTECTOMY Kassem Faraj, Lanyu Mi, and Mark Tyson Kassem FarajKassem Faraj More articles by this author , Lanyu MiLanyu Mi More articles by this author , and Mark TysonMark Tyson More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002515.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Radical Cystectomy (RC) is associated with significant morbidity and a result, high complication rates. Patient, or clinical factors, are the most commonly identified variables associated with hospital readmission. Other factors that may be important include hospital and physician characteristics, which have been suggested as contributing factors for readmission in other major surgeries. This study investigates the contribution of patient, physician and hospital factors on hospital readmission after RC. METHODS: This was a retrospective review of the Surveillance Epidemiology and End Results (SEER)-Medicare database focusing on bladder cancer patients who underwent RC between 2007-2016. These claims were identified using ICD-9/ICD-10 or HCPCS codes from MEDPAR or NCH claims, from which the annual hospital and physician volumes were calculated and classified as low, medium and high. Univariate and multivariable analyses were done for 90-day readmission as the outcome using a multilevel model to explore the association between readmission and characteristics of patient, hospital, and physician. Models with random intercepts were constructed to consider the variation from hospital and physician. The intraclass correlation coefficient (ICC) for hospital, physician and patient levels were also calculated. RESULTS: In 3530 patients, 1291 (36.6%) were readmitted within 90 days of the index surgery. On univariate analysis, continent urinary diversion (OR 1.533), greater NCI comorbidity score (OR 1.460 for NCI 2-4 vs <2; OR 1.754 for NCI >4 vs <2), robotic approach (OR 1.210), AJCC stage (stage III vs stage I, OR 0.777), hospital length of stay (OR 1.037), Northeast vs Western hospital location (OR 4.19), and any postoperative complication (OR 6.790) were significantly (p <0.05) associated with 90-day hospital readmission. On multilevel multivariable analysis, factors significantly (p <0.05) associated with 90-day readmission included continent urinary diversion (OR 1.462), robotic approach (OR 1.299), and any postoperative complication (OR 6.865). Neither hospital volume, physician volume, hospital region, teaching hospital status, nor NCI center designation were associated with hospital readmission. The main source of variation was determined to be at the patient (92.5%), followed by the physician (5.1%), and then hospital (2.4%) levels. CONCLUSIONS: Ninety-day hospital readmission at the time of RC is high, with around one-third of patients requiring a readmission. Patient-specific factors are the most important in impacting the odds of readmission, while hospital and physician factors contribute minimally to this outcome. Consequently, efforts to reduce readmission rates should focus on the patient level. Source of Funding: This study was supported by Grant Number P30 CA015083 from the National Cancer Institute (NCI) from the Robert D. and Patricia E. Kern Center for Health Care Delivery Science (M.D.T.), the Christian Haub Family Career Development Award for Cancer Research Honoring Dr Richard Emslander (M.D.T.), and the Eric and Gail Blodgett Foundation (M.D.T.). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NIH © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e27 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kassem Faraj More articles by this author Lanyu Mi More articles by this author Mark Tyson More articles by this author Expand All Advertisement PDF DownloadLoading ...

Disentangling the variability of the superficial white matter organization using regional-tractogram-based population stratification

Each variation of the cortical folding pattern implies a particular rearrangement of the geometry of the fibers of the underlying white matter. While this rearrangement only impacts the ends of the long pathways, it may affect most of the trajectory of the short bundles. Therefore, mapping the short fibers of the human brain using diffusion-based tractography requires a dedicated strategy to overcome the variability of the folding patterns. In this paper, we propose a fiber-based stratification strategy splitting the population into homogeneous groups for disentangling the superficial white matter bundle organization. This strategy introduces a new refined fiber distance which includes angular considerations for inferring fine-grained atlases of the short bundles surrounding a specific sulcus and a subtractogram distance that quantifies the similitude between fiber sets of two different subjects. The stratification splits the population into groups with similar regional fiber organization using manifold learning. We first successfully test the hypothesis that the main source of variability of the regional fiber organization is the variability of the regional folding pattern. Then, in each group, we proceed with the automatic identification of the most stable bundles, at a higher granularity level than what can be achieved with the non-stratified whole population, enabling the disentanglement of the very variable configuration of the short fibers. Finally, the method searches for bundle correspondence across groups to build a population level atlas. As a proof of concept, the atlas refinement achieved by this strategy is illustrated for the fibers that surround the central sulcus and the superior temporal sulcus using the HCP dataset.

Demography of smallholder agricultural women and youth enterprises and their association with the cultivation of the tomato (Solanum Lycopersicum) vegetable crop

The study was conducted at the Madimbo corridor and upper Mutale valley smallholder irrigation schemes. The purpose of the study was to characterize smallholder agricultural women and youth entrepreneurs (SHAW-YE). A semi-structured household questionnaire together with facilitation was used to survey a sample, purposively focusing on Smallholder Agriculture Women and Youth Enterprises (SHAW-YE). The sample was comprised of 294 respondents with sub-samples of respondents selected through gender category, age category, and gender by age category. The study revealed that the SHAW-YE are characterized by small land areas under cultivation. There were some associations between age, education, and income of SHAW-YE with their capacity to produce tomatoes. Participation of women was 48.4% compared to 20% of men in the winter production of tomatoes. Production participation by SHAW-YE farmers for summer revealed that women were participating at 9.7% compared with 13.3% for men. The participation of SHAW-YE around the ages of 36-59-year was at 52.9% while that for ages &gt;60-year was at 51.5%. The main source of variation may be the level of experience that the said age group of SHAW-YE may have in the production of tomatoes compared to 29.6% of 18–35-year participants. About 61.7% of SHAW-YE were at the level of Adult Basic Education &amp; Training (ABET). About 45.8% of the SHAW-YE earned less than R5000.00 compared with 50.7% earning more than R5000.00. Market channels and access should be promoted for SHAW-YE to enable the throughput of tomatoes to not only informal but also fresh produce and retail markets.

Assessing the effects of stress on feeding behaviors in laboratory mice.

Stress often affects eating behaviors, increasing caloric intake in some individuals and decreasing it in others. The determinants of feeding responses to stress are unknown, in part because this issue is rarely studied in rodents. We focused our efforts on the novelty-suppressed feeding (NSF) assay, which uses latency to eat as readout of anxiety-like behavior, but rarely assesses feeding per se. We explored how key variables in experimental paradigms - estrous and diurnal cyclicity, age and duration of social isolation, prandial state, diet palatability, and elevated body weight - influence stress-induced anxiety-like behavior and food intake in male and female C57BL/6J mice. Latency to eat in the novel environment is increased in both sexes across most of the conditions tested, while effects on caloric intake are variable. In the common NSF assay (i.e., lean mice in the light cycle), sex-specific effects of the length of social isolation, and not estrous cyclicity, are the main source of variability. Under conditions that are more physiologically relevant for humans (i.e., overweight mice in the active phase), the novel stress now elicits robust hyperphagia in both sexes . This novel model of stress eating can be used to identify underlying neuroendocrine and neuronal substrates. Moreover, these studies can serve as a framework to integrate cross-disciplinary studies of anxiety and feeding related behaviors in rodents.

Laser incidence angle influence on energy density variations, surface roughness, and porosity of additively manufactured parts

The use of additive manufacturing (AM) technologies for fabricating structural and functional parts is rapidly rising due to their transformative design flexibility and customizability. However, maintaining the uniformity and consistency of the fabricated part across the build plate is a critical challenge. One of the main sources of variability across the build plate is laser spot elongation in laser beam powder bed fusion (LB-PBF) AM which typically occurs when the beam moves from the center of the plate to the edge at different incidence angles. This laser spot elongation may then result in the loss of laser power density which can potentially cause elongated melt pools, poor dimensional accuracy, and formation of lack of fusion and more gas entrapped pores in the final part. Here, we report both geometrical calculations and experimental evidence to show the behavior of the laser in the different areas of the build plate. A pulsed laser is first used to show and calculate the geometrical footprint of the beam elongations at different incidence angles. Then, the surface roughness, melt pool size, and porosity variations are investigated for varying laser interaction cross-section across the build plate by manufacturing AlSi10Mg (an aluminum alloy) parts and correlating them to theoretical calculations. It is found that for given optimized process parameters at the center of the build plate, as the beam goes farther away from the center, it becomes elongated. As the power density falls, melt pool width becomes wider, melt pool depth becomes shallower (depending on the elongation orientation with respect to the scan direction), and the number of lack of fusion defects increases. This work provides fundamental insight into possible sources of variations across the build plate in an LB-PBF process that may help design new strategies to compensate for such variations to enhance uniformity and reliability of AM parts.

A tutorial on the analysis of multifactorial designs from one or more data sources using AComDim

AbstractTo analyze data acquired from experiments based on multifactorial designs, approaches able to decompose and model the main sources of variation in the measurements are particularly useful. Although methods combining analysis of variance (ANOVA) with principal components analysis (PCA) or simultaneous components analysis (SCA), namely, ANOVA‐PCA (APCA) and ANOVA‐SCA (ASCA), have proven to be efficient to tackle such data structures, they require the construction and interpretation of a model for each experimental factor and interaction. Other methods like parallel factor analysis‐SCA (PARAFASCA), ANOVA multiblock orthogonal partial least squares (AMOPLS) and ANOVA‐Common Dimensions (AComDim) offer the possibility to have an overall picture of all the sources of variation by means of a single model. Moreover, acquiring data from one or multiple data sources through the same experimental design is becoming more common, and approaches able to cope with such complex data structures are needed. In this tutorial, an explanation of the theory and software implementation of AComDim as well as its usage for the analysis of data acquired from experimental designs is provided. Relying on the multiblock nature of AComDim, an extension to cases where data from multiple sources are acquired on the same samples is proposed.

Warfarin Monitoring in Safety-Net Health Systems: Analysis by Race/Ethnicity and Language Preference.

Racial/ethnic disparities in anticoagulation management are well established. Differences in warfarin monitoring can contribute to these disparities and should be measured. We assessed for differences in international normalized ratio (INR) monitoring by race/ethnicity and language preference across safety-net care systems serving predominantly low-income, ethnically diverse populations. Cross-sectional analysis of process and safety data shared from the Safety Promotion Action Research and Knowledge Network (SPARK-Net) initiative, a consortium of five California safety-net hospital systems. Eligible patients were at least 18 years old, received warfarin for at least 56 days during the measurement period from July 2015 to June 2017, and had INR testing in an ambulatory care setting at a participating healthcare system. We conducted a scaled Poisson regression for adjusted rate ratio of having at least one INR checked per 56-day time period for which a patient had a warfarin prescription. Adjusting for age, sex, healthcare system, and insurance status/type, we assessed for racial/ethnic and language disparities in INR monitoring. Of 8129 patients, 3615 (44%) were female; 1470 (18%), Black/African American; 3354 (41%), Hispanic/Latinx; 1210 (15%), Asian; 1643 (20%), White; and 452 (6%), other. Three thousand five hundred forty-nine (45%) were non-English preferring. We did not observe statistically significant disparities in the rate of appropriate INR monitoring by race/ethnicity or language; the primary source of variation was by healthcare network. Older age, female gender, and uninsured patients had a slightly higher rate of appropriate INR monitoring, but differences were not clinically significant. We did not find a race/ethnicity nor language disparity in INR monitoring; safety-net site was the main source of variation.

Genotype × environment interaction and stability analyses of grain yield in rainfed winter bread wheat

Abstract The genotype × environment (GE) interaction analysis is fundamental in crop breeding programs to guide selection and for recommendation of high performing and stable genotypes for breeding objectives. This study aimed at quantifying the GE interaction effects and determines grain yield stability among winter bread wheat genotypes under rainfed conditions of Iran. Twenty-four winter wheat genotypes were evaluated under nine test locations using a randomized complete blocks design with four replications during three cropping seasons (2019–21). The additive main effects and multiplicative interaction (AMMI) model and several parametric and nonparametric stability statistics were applied for analysis of grain yield data collected from the experiments. AMMI analysis of variance for grain yield revealed significant effects (p &lt; 0.01) for genotype, environment, and GE interaction. The environment was the main source of variation and accounted for 83.5% of the total yield variation, followed by GE (6.5%) and genotype (1.0%) effects. The AMMI biplot analysis indicated the genotypes G3, G23, G22, G10, and G19 as high yielding with stability performance across environments. Genotypes G14, G13, G20, and G9 showed large positive interaction with the environments featuring the highest rainfall during growing season, while genotypes G7, G6, and G21 had a large positive interaction with environments with low rainfall. Spearman’s rank correlation analysis revealed that the AMMI stability value, Shukla’s stability variance (σ2i), Wricke’s ecovalence (W2i), coefficient of determination (R2i), variance in regression deviations (S2di), and nonparametric statistic of S2(i) were not correlated with mean yield in tested genotypes, showing they are related to static/biological concept of stability. In contrast, the genotypic superiority index (Pi) and regression coefficient (bi) were significantly correlated (p &lt; 0.01) with mean yield and corresponded to dynamic/agronomic concept of stability. These findings suggest that selection of genotypes should be considered based on selection objectives of using the various stability parameters described here. In conclusion, the selected genotypes in this study should be recommended as new cultivars or parental lines for grain yield and stability improvement under rainfed conditions of Iran or similar agro-ecologies.

Genetic and Environmental Effects on Grain Size Uniformity in Winter Wheat

Grain size is an important yield component and also influences flour yield, seed vigor and early plantlet development. It is usually described by the weight (or mass) of thousand grains or average of dimensions of a sample, without paying attention to the variability of individual grains. However, variation in individual grain size is economically important for milling industry, because many small grains are lost during the cleaning process prior to milling and a high proportion of small grains is indicative of a poor flour yield. Also, when grains are sown for a new crop, grain mass variability increases the heterogeneity of germination and leads to non-uniform seedling emergence and crop establishment, often leading to decreased yield. We analyzed using the software ImageJ (http://imagej.nih.gov/ij/) individual grain size distributions of samples 80 to 120 individual grains each, harvested from yield trials at NARDI Fundulea Romania with 13 cultivars grown on four management variants, during two years. Individual grain projected area, as a proxy of grain size, ranged from 8.18 to 29.06 mm2, and for most cultivars minimum values of individual grain projected area were less than half of maximum values. On average over all management variants and years, coefficients of variation (s%) varied from less than 14% in cultivars Bezostaya 1, Glosa and Pajura to more than 15.5% in cultivars Adelina and Litera. ANOVA for the coefficients of variation of grain projection areas showed that the main source of variation was the effect of cultivars, which was significant when tested both against the Error and the interaction Cultivars*Years. The effect of the four studied managements was significant when tested against the Error, but not when tested against the interaction with Years, suggesting that the managements did not have the same effect in the two years of study. The large differences between cultivars found in our study provide opportunities in breeding for higher grain uniformity.

Preliminary project design for insect production: part 5 – models and simulation of insect production – organism development

The issue dealt with here is the rearing of an organism from egg to larva to pupa as is commonly done with various mealworms and black soldier flies, while organism development is the principal subject of interest. In order to obtain a uniform product at harvest time, the population should be synchronised as much as possible and this means that variability in development in the population must be reduced to the greatest possible extent. Four main sources of variability in development were identified, two of them operational and two biological in nature. These are: (1) distribution of the initial ages of the eggs; (2) distribution of incubation temperatures of the organisms; (3) inherent variability in the development rate of individual organisms; and (4) distribution in temperature sensitivity of the organisms. The combined effects of the various sources of variability on production and productivity were examined by means of simulation. This kind of exercise is useful in a number of contexts: (a) during the very early stage of project planning, as part of preliminary project design, before any formal design work is started and well before any major financial commitments are made; (b) during trouble-shooting of a low-performing process in order to upgrade its product quality; (c) to develop specifications for egg age distribution, design specifications for incubation equipment, etc.; (d) to better understand what data are required about the organism of interest so as to facilitate process design; and (e) to set objectives for organism breeding efforts. Examples are presented of how variabilities in the various factors can combine to yield organism development distributions at harvest time and how understanding the interactions between factors can help in designing a better production process.

Association of eating motives with anthropometry, body composition, and dietary intake in healthy German adults

Effective policies to address poor food choices and dietary patterns need to consider the complex set of motives affecting eating behavior. This study examined how different eating motives are associated with anthropometry, body composition, and dietary intake. Our analysis is based on a cross-sectional sample with 429 healthy adults in three different age groups collected in Germany from 2016 to 2018. Dietary intake, Body Mass Index (BMI), waist circumference (WC), and fat-free mass (FFM) were measured by standardized methods. Eating motives were measured using The Eating Motivation Scale (TEMS). Regressing dietary intakes and anthropometric indicators on TEMS motives, we identify the main sources of variation in diet and nutritional status separately for men and women. Results indicated the Health motive to be positively associated with FFM (B±SE=1.72±0.44) and negatively with WC (B±SE=−3.23±0.81) for men. For women, the Need & Hunger motive was positively associated with FFM (B±SE=1.63±0.44) and negatively with WC (B±SE=−2.46±0.81). While Liking and Habits were the most frequently stated eating motives, we did not find them to be significantly related to the nutritional status. Other motives were associated with dietary intake but not anthropometry or body composition. The Price motive was positively and the Convenience motive was negatively associated with energy (B±SE=63.77±19.98;B±SE=−46.96±17.12) and carbohydrate intake (B±SE=7.15±2.65;B±SE=−5.98±2.27) for men. The results highlight the need for more differentiated analyses of eating motives, beyond comparing the relative importance of motives based on mean values, towards the association of motives with dietary intake and nutritional status.

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

Urban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve &gt; 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability.

Towards a model of language neurobiology in early development

Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.

Seasonal-To-Interannual Variability of Sea-Surface Temperatures in The Inter-Americas Seas: Pattern-Dependent Biases in The Regional Ocean Modeling System

The Inter-Americas Seas (IAS), involving the Gulf of Mexico, the Caribbean and a section of the eastern tropical Pacific Ocean bordering Central America, Colombia and Ecuador, exhibits very active ocean-land-atmosphere interactions that impact socio-economic activities within and beyond the region, and that are still not well understood or represented in state-of-the-art models. On seasonal-to-interannual timescales, the main source of variability of this geographical area is related to interactions between the Pacific and the Atlantic oceans, involving to anomalous sea-surface temperature (SST) patterns like El Niño-Southern Oscillation (ENSO), and regional features in the Caribbean linked to the bi-modal seasonality of the Caribbean Low-Level Jet. This study investigates seasonal-to-interannual IAS surface-temperature anomalies in observations, and their representation in am eddy-permitting, 1/9o-resolution simulation using the Regional Ocean Modeling System (ROMS), interannually-forced by the Climate Forecast System Reanalysis. Here, rather than analyzing model biases locally (i.e., gridbox-by-gridbox), a non-local SST pattern-based diagnostic was conducted via a principal component analysis. The approach allowed to identify magnitude, variance and spatial systematic errors in SST patterns related to the Western Hemisphere Warm Pool, ENSO, the Inter-American Seas Dipole, and several other variability modes. These biases are mainly related to errors in surface heat fluxes, misrepresentation of air-sea interactions impacting surface latent cooling in the Caribbean, and too strong sub-surface thermal stratification, mostly off the coast of Ecuador and northern Peru.

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules: A Multicenter Phantom Study.

BackgroundRadiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications.ObjectivesThe aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies.Materials and MethodsA chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research.ResultsA total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate <0.001). Finally, we obtained a subset of 19 stable, informative, and nonredundant RFs under all 3 sources of variability. These RFs exhibited clinical effectiveness and showed better prediction performance than unstable RFs in the validation dataset (P = 0.017, Delong test).ConclusionsThe stability of RFs was affected to different degrees by test-retest and differences in CT manufacturers and models and CT acquisition and reconstruction parameters, but the influences of these factors showed relatively consistent patterns. We also obtained a subset of 19 stable, informative, and nonredundant RFs that should be preferably used to enhance the generalizability of further radiomics research.

Wood Density and Ring Width in Quercus rotundifolia Trees in Southern Portugal

Quercus rotundifolia Lam., known as holm oak or evergreen oak, occurs naturally in the western Mediterranean region, mainly as part of the agroforestry or agrosilvopastoral systems in Portugal (“montado”) and Spain (“dehesa”), and is economically important for acorn production. Less attention has been given to Q. rotundifolia wood, and its density variability is not known, namely related to tree growth. The wood density of 20 Q. rotundifolia trees was measured along the radial direction by X-ray densitometry and the factors responsible for ring width and wood density variation within and between trees were investigated at two sites located within the main species region in southern Portugal. Ring width was significantly different between sites, with an average of 1.81 mm and 1.55 mm. Wood density was very high and averaged between 888 kg/m3 and 914 kg/m3 but not significantly different between sites. Ring width and wood density showed a positive and significant correlation at both sites. Cambial age was the main source of variation for ring width and wood density, while between-tree effects accounted for a considerable proportion of wood density variation. The results are an important contribution for the species valorisation aiming at high-value wood products, also adding knowledge on the species growth of interest for tree selection and sustainable management.

Electromethanogenesis at medium-low temperatures: Impact on performance and sources of variability

In this study we aimed to understand the impact of medium–low temperatures on the two main steps that usually comprise the electromethanogenesis (EM) process: electrothrophic hydrogenesis and hydrogenothrophic methanogenesis. Results revealed that pure CO2 could effectively be converted into a high-purity biogas (∼90:10 CH4/CO2) at 30 °C. However, when temperature was reduced to 15 °C, methane richness greatly decreased (∼40:60 CH4/CO2). This deterioration in performance was mostly attributed to a decline in methanogenic activity (represented mainly by Methanobacterium and Methanobrevibacter). In contrast, the hydrogenic activity (mostly Desulfomicrobium) did not suffer any significant decay. Results also seemed to indicate that methanogenesis, rather than hydrogenesis, is the main source of variability in EM. Increasing the temperature again to 30 °C restored previous performance, which highlights the resilience of EM to wide temperature fluctuations (from 30 to 15 and back 30 °C).

EC50 images, a novel endpoint from PET target occupancy studies, reveal spatial variation in apparent drug affinity.

We recently introduced voxel-level images of drug occupancy from PET via our "Lassen plot filter." Occupancy images revealed clear dependence of 11C-flumazenil displacement on dose of GABAa inhibitor, CVL-865, but with different scales in different brain regions. We hypothesized that regions requiring higher drug concentrations to achieve desired occupancy would have higher EC50 values. We introduce an "EC50 image" from human data to evaluate this hypothesis. Five healthy subjects were scanned with the nonselective GABAa tracer, 11C-flumazenil, before and (twice) after administration of CVL-865. We created ten occupancy images and applied an Emax model locally to create one EC50 image. We also performed simulations to confirm our observations of regional variation in EC50 and to identify the main source of variability in EC50. As expected, the EC50 image revealed spatial variation in apparent drug affinity. High EC50 was found in areas of low occupancy for a given drug dose. Simulations demonstrated that sampling from an inadequate range of plasma drug concentrations could impair precision. Our results argue for (a) confidence in the ability of the EC50 images to identify regional differences and (b) a need to tailor the range of drug doses in an occupancy study to regularize the precision of the EC50 throughout the brain. The EC50 image could add value to early-phase drug development by identifying regional variation in affinity that might impact therapy or safety and by guiding dose selection for later-phase trials.

Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2*R47H.

Late-onset Alzheimer’s disease (AD; LOAD) is the most common human neurodegenerative disease, however, the availability and efficacy of disease-modifying interventions is severely lacking. Despite exceptional efforts to understand disease progression via legacy amyloidogenic transgene mouse models, focus on disease translation with innovative mouse strains that better model the complexity of human AD is required to accelerate the development of future treatment modalities. LOAD within the human population is a polygenic and environmentally influenced disease with many risk factors acting in concert to produce disease processes parallel to those often muted by the early and aggressive aggregate formation in popular mouse strains. In addition to extracellular deposits of amyloid plaques and inclusions of the microtubule-associated protein tau, AD is also defined by synaptic/neuronal loss, vascular deficits, and neuroinflammation. These underlying processes need to be better defined, how the disease progresses with age, and compared to human-relevant outcomes. To create more translatable mouse models, MODEL-AD (Model Organism Development and Evaluation for Late-onset AD) groups are identifying and integrating disease-relevant, humanized gene sequences from public databases beginning with APOEε4 and Trem2*R47H, two of the most powerful risk factors present in human LOAD populations. Mice expressing endogenous, humanized APOEε4 and Trem2*R47H gene sequences were extensively aged and assayed using a multi-disciplined phenotyping approach associated with and relative to human AD pathology. Robust analytical pipelines measured behavioral, transcriptomic, metabolic, and neuropathological phenotypes in cross-sectional cohorts for progression of disease hallmarks at all life stages. In vivo PET/MRI neuroimaging revealed regional alterations in glycolytic metabolism and vascular perfusion. Transcriptional profiling by RNA-Seq of brain hemispheres identified sex and age as the main sources of variation between genotypes including age-specific enrichment of AD-related processes. Similarly, age was the strongest determinant of behavioral change. In the absence of mouse amyloid plaque formation, many of the hallmarks of AD were not observed in this strain. However, as a sensitized baseline model with many additional alleles and environmental modifications already appended, the dataset from this initial MODEL-AD strain serves an important role in establishing the individual effects and interaction between two strong genetic risk factors for LOAD in a mouse host.