Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Adventitious Error and Its Implications for Testing Relations Between Variables and for Composite Measurement Outcomes.

Wu and Browne (Psychometrika 80(3):571-600, 2015. https://doi.org/10.1007/s11336-015-9451-3 ; henceforth W &B) introduced the notion of adventitious error to explicitly take into account approximate goodness of fit of covariance structure models (CSMs). Adventitious error supposes that observed covariance matrices are not directly sampled from a theoretical population covariance matrix but from an operational population covariance matrix. This operational matrix is randomly distorted from the theoretical matrix due to differences in study implementations. W &B showed how adventitious error is linked to the root mean square error of approximation (RMSEA) and how the standard errors (SEs) of parameter estimates are augmented. Our contribution is to consider adventitious error as a general phenomenon and to illustrate its consequences. Using simulations, we illustrate that its impact on SEs can be generalized to pairwise relations between variables beyond the CSM context. Using derivations, we conjecture that heterogeneity of effect sizes across studies and overestimation of statistical power can both be interpreted as stemming from adventitious error. We also show that adventitious error, if it occurs, has an impact on the uncertainty of composite measurement outcomes such as factor scores and summed scores. The results of a simulation study show that the impact on measurement uncertainty is rather small although larger for factor scores than for summed scores. Adventitious error is an assumption about the data generating mechanism; the notion offers a statistical framework for understanding a broad range of phenomena, including approximate fit, varying research findings, heterogeneity of effects, and overestimates of power.

Slow recovery rates and spatial aggregation of Triatoma infestans populations in an area with high pyrethroid resistance in the Argentine Chaco.

BACKGROUND: The emergence of pyrethroid resistance has threatened the elimination of Triatoma infestans from the Gran Chaco ecoregion. We investigated the status and spatial distribution of house infestation with T. infestans and its main determinants in Castelli, a municipality of the Argentine Chaco with record levels of triatomine pyrethroid resistance, persistent infestation over 2005-2014, and limited or no control actions over 2015-2020. METHODS: We conducted a 2-year longitudinal survey to assess triatomine infestation by timed manual searches in a well-defined rural section of Castelli including 14 villages and 234 inhabited houses in 2018 (baseline) and 2020, collected housing and sociodemographic data by on-site inspection and a tailored questionnaire, and synthetized these data into three indices generated by multiple correspondence analysis. RESULTS: The overall prevalence of house infestation in 2018 (33.8%) and 2020 (31.6%) virtually matched the historical estimates for the period 2005-2014 (33.7%) under recurrent pyrethroid sprays. While mean peridomestic infestation remained the same (26.4-26.7%) between 2018 and 2020, domestic infestation slightly decreased from 12.2 to 8.3%. Key triatomine habitats were storerooms, domiciles, kitchens, and structures occupied by chickens. Local spatial analysis showed significant aggregation of infestation and bug abundance in five villages, four of which had very high pyrethroid resistance approximately over 2010-2013, suggesting persistent infestations over space-time. House bug abundance within the hotspots consistently exceeded the estimates recorded in other villages. Multiple regression analysis revealed that the presence and relative abundance of T. infestans in domiciles were strongly and negatively associated with indices for household preventive practices (pesticide use) and housing quality. Questionnaire-derived information showed extensive use of pyrethroids associated with livestock raising and concomitant spillover treatment of dogs and (peri) domestic premises. CONCLUSIONS: Triatoma infestans populations in an area with high pyrethroid resistance showed slow recovery and propagation rates despite limited or marginal control actions over a 5-year period. Consistent with these patterns, independent experiments confirmed the lower fitness of pyrethroid-resistant triatomines in Castelli compared with susceptible conspecifics. Targeting hotspots and pyrethroid-resistant foci with appropriate house modification measures and judicious application of alternative insecticides with adequate toxicity profiles are needed to suppress resistant triatomine populations and prevent their eventual regional spread.

Drivers of wood-inhabiting fungal diversity in European and Oriental beech forests.

The hyperdiverse wood-inhabiting fungi play a crucial role in the global carbon cycle, but often are threatened by deadwood removal, particularly in temperate forests dominated by European beech (Fagus sylvatica) and Oriental beech (Fagus orientalis). To study the impact of abiotic drivers, deadwood factors, forest management and biogeographical patterns in forests of both beech species on fungal composition and diversity, we collected 215 deadwood-drilling samples in 18 forests from France to Armenia and identified fungi by meta-barcoding. In our analyses, we distinguished the patterns driven by rare, common, and dominant species using Hill numbers. Despite a broad overlap in species, the fungal composition with focus on rare species was determined by Fagus species, deadwood type, deadwood diameter, precipitation, temperature, and management status in decreasing order. Shifting the focus on common and dominant species, only Fagus species, both climate variables and deadwood type remained. The richness of species within the deadwood objects increased significantly only with decay stage. Gamma diversity in European beech forests was higher than in Oriental beech forests. We revealed the highest gamma diversity for old-growth forests of European beech when focusing on dominant species. Our results implicate that deadwood retention efforts, focusing on dominant fungi species, critical for the decay process, should be distributed across precipitation and temperature gradients and both Fagus species. Strategies focusing on rare species should additionally focus on different diameters and on the conservation of old-growth forests.

Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions.

Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.

OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. MATERIALS AND METHODS: A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. RESULTS: For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. CONCLUSION: Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.

Age-specific heterogeneity of genetic susceptibility to cardiovascular disease might have opposite outcomes depending on the presence of prediabetes.

Examining age-specific heterogeneity of susceptibility to cardiovascular disease is also essential in individuals without prediabetes to determine its relative size and direction compared to those with prediabetes. Of particular interest, age-specific heterogeneity in genetic susceptibility may exhibit opposite directions depending on the presence or absence of prediabetes.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research.

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Spatial heterogeneity analysis for the transmission of syphilis disease in China via a data-validated reaction-diffusion model.

Based on the distinctive spatial diffusion characteristics observed in syphilis transmission patterns, this paper introduces a novel reaction-diffusion model for syphilis disease dynamics, incorporating general incidence functions within a heterogeneous environment. We derive the basic reproduction number essential for threshold dynamics and investigate the uniform persistence of the model. We validate the model and estimate its parameters by employing the multi-objective Markov Chain Monte Carlo (MCMC) method, using real syphilis data from the years 2004 to 2018 in China. Furthermore, we explore the impact of spatial heterogeneity and intervention measures on syphilis transmission. Our findings reveal several key insights: (1) In addition to the original high-incidence areas of syphilis, Xinjiang, Guizhou, Hunan and Northeast China have also emerged as high-incidence regions for syphilis in China. (2) The latent syphilis cases represent the highest proportion of newly reported cases, highlighting the critical importance of considering their role in transmission dynamics to avoid underestimation of syphilis outbreaks. (3) Neglecting spatial heterogeneity results in an underestimation of disease prevalence and the number of syphilis-infected individuals, undermining effective disease prevention and control strategies. (4) The initial conditions have minimal impact on the long-term spatial distribution of syphilis-infected individuals in scenarios of varying diffusion rates. This study underscores the significance of spatial dynamics and intervention measures in assessing and managing syphilis transmission, which offers insights for public health policymakers.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies.

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Assessing spatial heterogeneity of nutrient loads in a large shallow lake using a lattice Boltzmann water quality model.

Nutrient loads in lakes are spatially heterogeneous, but current spatial analysis method are mainly zonal, making them subjective and uncertain. This study proposes a high-resolution model for assessing spatial differences in nutrient loads based on the lattice Boltzmann method. The model was applied to Dongping Lake in China. Firstly, the contribution rates of four influencing factors, including water transfer, inflow, wind, and internal load, were calculated at different locations in the lake. Then, their proportionate contributions during different intervals to the whole lake area were calculated. Finally, the cumulative load could be calculated for any location within the lake. The validation showed that the model simulated hydrodynamics and water quality well, with relative errors between the simulated and measured water quality data smaller than 0.45. Wind increased the nutrient loads in most parts of the lake. The loads tended to accumulate in the east central area where high-frequency circulation patterns were present. Overall, the proposed water quality model based on the lattice Boltzmann method was able to simulate seven indexes. Therefore, this model represents a useful tool for thoroughly assessing nutrient load distributions in large shallow lakes and could help refine lake restoration management.

Pan-cancer analysis of heterogeneity of tumor mutational burden and genomic mutation under treatment pressure.

BACKGROUND: High tumor mutational burden (TMB) is one of the widely researched predictive biomarkers of immune checkpoint inhibitors and has been shown to be closely related with response to immunotherapy in multiple cancer types. However, for patients who have failed conventional therapy and are about to undergo immunotherapy, there is no consensus recommendation on the timing of tumor sampling for TMB analysis, and the effects of different therapies on TMB have not been clarified. This retrospective observational study aimed to investigate the heterogeneity of TMB and genomic mutation under the treatment pressure. PATIENTS AND METHODS: We retrospectively collected the available genomic and therapeutic information from 8051 samples across 15 tumor types (>50 samples/tumor) found in 30 published studies and investigated the distribution and heterogeneity of TMB under treatment across diverse cohorts. RESULTS: This integrated analysis has shown anticancer treatments increased TMB. Significant effects of treatment on TMB were more frequently observed in tumor types with lower treatment-naïve TMB, including breast, prostate, and pediatric cancers. For different cancer therapies, chemotherapy was prone to be correlated with an increased TMB in most cancer types. Meanwhile, the fraction of the TMB-high category of breast, prostate, and bladder cancers and glioma increased significantly after chemotherapy. Several actionable genes including ERS1 and NF1 in breast cancer, as well as some prognostic markers including TERT in bladder cancer and IDH1 in glioma, were significantly changed in post-chemotherapy tumors compared to treatment-naïve tumors. CONCLUSION: Our study reveals the heterogeneity of TMB under treatment across diverse cancer types and provides evidences that chemotherapy was associated with increases in TMB as well as the fraction of TMB-high category, suggesting that resampling tumor tissues for calculating post-chemotherapy TMB could be a better option for predicting the response to immunotherapy, especially for tumors with initially low TMB.

Directly selecting cell-type marker genes for single-cell clustering analyses.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

Computed Tomography-Based Intratumor Heterogeneity Predicts Response to Immunotherapy Plus Chemotherapy in Esophageal Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: This study explored the intratumor heterogeneity (ITH) of esophageal squamous cell carcinoma (ESCC) using computed tomography (CT) and investigated the value of CT-based ITH in predicting the response to immune checkpoint inhibitor (ICI) plus chemotherapy in patients with ESCC. MATERIALS AND METHODS: This retrospective study included 416 patients with ESCC who received ICI plus chemotherapy at two independent hospitals between January 2019 and July 2022. Multiparametric CT features were extracted from ESCC lesions and screened using hierarchical clustering and dimensionality reduction algorithms. Logistic regression and machine learning models based on selected features were developed to predict treatment response and validated in separate datasets. ITH was quantified using the score calculated by the best-performing model and visualized through feature clustering and feature contribution heatmaps. A gene set enrichment analysis (GSEA) was performed to identify the biological pathways underlying the CT-based ITH. RESULTS: The extreme gradient boosting model based on CT-derived ITH had higher discriminative power, with areas under the receiver operating characteristic curve of 0.864 (95% confidence interval [CI]: 0.774-0.954) and 0.796 (95% CI: 0.698-0.893) in the internal and external validation sets. The CT-based ITH pattern differed significantly between responding and non-responding patients. The GSEA indicated that CT-based ITH was associated with immunity-, keratinization-, and epidermal cell differentiation-related pathways. CONCLUSION: CT-based ITH is an effective biomarker for identifying patients with ESCC who could benefit from ICI plus chemotherapy. Immunity-, keratinization-, and epidermal cell differentiation-related pathways may influence the patient's response to ICI plus chemotherapy.

Unveiling the Influence of Brightness Heterogeneity in Fluorescence Correlation Spectroscopy of Perovskite Nanocrystals.

Nanoparticles (NPs), including perovskite nanocrystals (PNCs) with single photon purity, present challenges in fluorescence correlation spectroscopy (FCS) studies due to their distinct photoluminescence (PL) behaviors. In particular, the zero-time correlation amplitude [g2(0)] and the associated diffusion timescale (τD) of their FCS curves show substantial dependency on pump intensity (IP). Optical saturation inadequately explains the origin of this FCS phenomenon in NPs, thus setting them apart from conventional dye molecules, which do not manifest such behavior. This observation is apparently attributed to either photo-brightening or optical trapping, both lead to increased NP occupancy (N) in the excitation volume, consequently reducing the g2(0) amplitude [since g2(0) α 1/N] at high IP. However, an advanced FCS study utilizing alternating laser excitation at two different intensities dismisses such possibilities. Further investigation into single-particle blinking behaviors as a function of IP reveals that the intensity dependence of g2(0) primarily arises from the brightness heterogeneity prevalent in almost all types of NPs. This report delves into the complexities of the photophysical properties of NPs and their adverse impacts on FCS studies.

Pattern of heterogeneity in normal brain ageing: screening for mild cognitive impairment and its risk of progression with a radiomics model.

PURPOSE: This study aimed to develop a normal brain ageing model based on magnetic resonance imaging and radiomics, therefore identifying radscore, an imaging indicator representing white matter heterogeneity and exploring the significance of radscore in detecting people's cognitive changes. METHODS: Three hundred sixty cognitively normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 105 CN subjects from the Parkinson's Progression Markers Initiative database were used to develop the model. In ADNI, 230 mild cognitive impairment (MCI) subjects were matched with 230 CN old-aged subjects to evaluate their heterogeneity difference. One hundred four MCI subjects with 48 months of follow-up were divided into low and high heterogeneity groups. Kaplan-Meier survival curve analysis was used to observe the importance of heterogeneity results for predicting MCI progression. RESULTS: The area under the receiver operating characteristic curve of the model in the training, internal test and external test sets was 0.7503, 0.7512 and 0.7514, respectively. There was a significantly positive correlation between age and radscore of CN subjects (r = 0.501; P < .001). The radscore of MCI subjects was significantly higher than that of matched CN subjects (P < .001). The median radscore ratios of MCI to CN from four age groups (66-70y, 71-75y, 76-80y and 81-85y) were 1.611, 1.760, 1.340 and 1.266, respectively. The probability to progression of low and high heterogeneity groups had a significant difference (P = .002). CONCLUSION: When radscore is significantly higher than that of normal ageing, it is necessary to alert the possibility of cognitive impairment and deterioration.

Defending against environmental threats: Unveiling household adaptation strategies and population heterogeneity.

Humanity faces a variety of risks from pollution and environmental degradation. Societal advancement has equipped the public with numerous self-protection measures to mitigate these threats. However, the ways in which individuals deploy and balance self-defence mechanisms within this complex risk landscape and the resulting consequences remain largely unexplored. Drawing on a detailed survey of households' self-defence practices, this study rigorously analyses the heterogeneity and driving factors behind household-level self-defence strategies. Through exploratory latent class modelling, we identified four distinct defence patterns: inaction, water-sensitive, air-sensitive, and multifaceted. These patterns reveal varied defence capabilities among the population. By integrating frameworks from economics and social psychology, significant disparities were found in the driving factors behind these patterns. Practices aimed at combating air pollution are primarily driven by the actual severity of pollution and perceived coping capabilities, whereas measures to enhance water quality are influenced more by perceived threats. This disparity arises from variations in information availability and health awareness. The study also highlights a misalignment between the distribution of defence capabilities and the levels of pollution. Given that income restricts self-defence options, this mismatch indicates that economically disadvantaged groups are disproportionately affected by severe health inequalities.

Wavelet transform of autoradiographs: Analysis of MOX fuel heterogeneity.

Radiation imaging is extensively applied in nuclear industry for various purposes including fuel characterization. Accurate quantitative evaluation of these radiographic images is difficult by the existing manual process and therefore image analytical methods have been attempted. The method of wavelet transform analysis has been applied on Gamma autoradiography (GAR) images of experimental (U, Pu)O2 mixed oxide (MOX) fuel pins with the objective to investigate the effectiveness of the method for fuel homogeneity characterization. The method was found effective to carry out quantified estimations of size and relative plutonium concentration of the heterogeneous portions in the fuel. The results were validated with theoretically simulated results as practical standards and calibrations are not possible in these samples. The results of wavelet transformation analysis were found to be more accurate with reference to the theoretically simulated values in comparison with conventional pixel analysis applied on the original GAR images.

Beyond the surface: Exploring the temporally stable factors influencing injury severities in large-truck crashes using mixed logit models.

An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses.