Sources of PM2.5 exposure misclassification in three Finnish register-based study populations and the impact on attenuation of health effect estimates.

Air pollution is a leading environmental health risk factor. The risk estimates, primarily based on air pollution epidemiology, are sensitive to exposure misclassification, which can result in underestimation. To address some of these challenges, our aim is to investigate how the length of the period over which the exposure is averaged, trends in long-term PM2.5 concentrations, and the seasonal variability are associated with each other. Furthermore, we assess the impact of residential relocation on exposure levels and quantify random exposure misclassification due to modelling and its impact on the attenuation of effects with respect to averaging time. We used nested air quality modelling across Finland, gridded population, and address histories from three study populations: the MATEX pregnancy and preschool children cohorts, as well as the FINPARK study's individuals diagnosed with Parkinson's disease and their controls, to estimate PM2.5 exposures. The prediction error was estimated by comparing modelled concentrations to observations and by using previous estimates for random monitoring instrument error. Due to the decreasing trend in PM2.5 concentrations, exposure levels rose progressively with longer averaging times, increasing by up to 28 % over a 16-year period. The shorter the exposure period, the more pronounced the seasonal effects: pregnant mothers' trimester-specific exposures were 13-22 % higher for trimesters ending in spring and 10-16 % lower for those ending in autumn compared to the average for the entire pregnancy. Residential relocation had a relatively minor impact on the exposure levels of the preschool children and adult FINPARK study population, but this effect was possibly partly masked by the decreasing trend. The results indicated that using predicted concentrations led to random exposure misclassification and potentially attenuated health effects. This effect became more notable when increasing the length of the exposure period from 3 months to 5 years, doubling the underestimation ratio from 1.5 to 3.1.

Unveiling nuances in data analysis to illuminate marine pilot strain.

Maritime studies, encompassing a range of disciplines, increasingly rely on advanced data analytics, particularly in the context of navigation. As technology advances, the statistical averaging of large datasets has become a critical component of these analyses. However, recent studies have highlighted discrepancies between statistical predictions and observable realities, especially in high-stress environments like port approach procedures conducted by marine pilots. This study analyzed physiological responses recorded during simulation exercises involving experienced marine pilots. The focus was not on the specific outcomes of the simulations but on the potential faults arising from conventional statistical signal processing, particularly mean-centered approaches. A large dataset of signals was generated, including one signal with a dominant characteristic intentionally designed to introduce imbalance, mimicking the uneven distribution of real-world data. Initial analysis suggested that the average physiological response of the pilots followed an S-shaped curve, indicative of a psycho-physiological reaction to stress. However, further post hoc analysis revealed that this pattern was primarily influenced by a single participant's data. This finding raises concerns about the generalizability of the S-curve as a typical stress response in maritime pilots. The results underscore the limitations of relying solely on conventional statistical methods, such as mean-centered approaches, in interpreting complex datasets. The study calls into question the validity of standardizing data interpretations based on dominant characteristic curves, particularly in environments as intricate as maritime navigation. The research highlights the need for a re-evaluation of these methods to ensure more reliable and nuanced conclusions in maritime studies. This study contributes to the ongoing discourse on data interpretation in maritime research, emphasizing the critical need to re-assess conventional statistical signal processing techniques. By recognizing the potential pitfalls in data generalization, the study advocates for more robust analytical approaches to better capture the complexities of real-world maritime challenges.

Evaluation of a Volume-Averaged Species Transport Model with Micro-Macro Coupling for Breakthrough Curve Prediction.

In porous water filters, the transport and entrapment of contaminants can be modeled as a classic mass transport problem, which employs the conventional convection-dispersion equation to predict the transport of species existing in trace amounts. Using the volume-averaging method (VAM), the upscaling has revealed two possible macroscopic equations for predicting contaminant concentrations in the filters. The first equation is the classical convection-dispersion equation, which incorporates a total dispersion tensor. The second equation involves an additional transport coefficient, identified as the adsorption-induced vector. In this study, the aforementioned equations were solved in 1D for column tests using 3D unit cells. The simulated breakthrough curves (BTCs), using the proposed micro-macro-coupling-based VAM model, are compared with the direct numerical simulation (DNS) results based on BCC-type unit cells arranged one-after-another in a daisy chain manner, as well as with three previously reported experimental works, in which the functionalized zeolite and zero-valent iron fillings were used as an adsorbent to remove phosphorous and arsenic from water, respectively. The disagreement of VAM BTC predictions with DNS and experimental results reveals the need for an alternative closure formulation in VAM. Detailed investigations reveal time constraint violations in all the three cases, suggesting this as the main cause of VAM's failure.

Effects of urbanization on cloud-to-ground lightning strike frequency: a global perspective.

Urbanization tends to increase local lightning frequency (i.e. the 'lightning enhancement' effect). Despite many urban areas showing lightning enhancement, the prevalence of these effects is unknown and the drivers underlying these patterns are poorly quantified. We conducted a global assessment of cloud-to-ground lightning flashes (lightning strikes) across 349 cities to evaluate how the likelihood and magnitude of lightning enhancement vary with geography, climate, air pollution, topography and urban development. The likelihood of exhibiting lightning enhancement increased with higher temperature and precipitation in urban areas relative to their natural surroundings (i.e. urban heat islands and elevated urban precipitation), higher regional lightning strike frequency, greater distance to water bodies and lower elevations. Lightning enhancement was stronger in cities with conspicuous heat islands and elevated urban precipitation effects, higher lightning strike frequency, larger urban areas and lower latitudes. The particularly strong effects of elevated urban temperature and precipitation indicate that these are dominant mechanisms by which cities cause local lightning enhancement.

Discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data" by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim.

This discussion provides commentary on the paper by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim entitled "LEAP: the latent exchangeability prior for borrowing information from historical data". The authors propose a novel method to bridge the incorporation of supplemental information into a study while also identifying potentially exchangeable subgroups to better facilitate information sharing. In this discussion, we highlight the potential relationship with other Bayesian model averaging approaches, such as multisource exchangeability modeling, and provide a brief numeric case study to illustrate how the concepts behind latent exchangeability prior may also improve the performance of existing methods. The results provided by Alt et al. are exciting, and we believe that the method represents a meaningful approach to more efficient information sharing.

Time course and neural locus of the Flashed Face Distortion Effect.

Viewing a rapid sequence of face images shown in the periphery can lead to large caricature-like distortions in the perceived images, a phenomenon known as the Flashed Face Distortion Effect (FFDE). The mechanisms underlying FFDE are poorly understood. Here we examined the timing and sites of the adaptation processes giving rise to the FFDE. To investigate the effects of presentation rate, we maintained consistent trial lengths while assessing how variations in the temporal frequencies of face presentation influenced the magnitude of face distortion and the averaging of facial expressions. Over a wide range of temporal frequencies (1.2-60 Hz) tested, we observed a decrease in FFDE strength as the presentation rate increased. To probe the neural sites of FFDE, we varied whether successive faces were presented to the same or different eyes using a dichoptic display. Distortion effects were comparable for monocular, binocular, and interocular conditions, yet much larger than a control condition where faces were presented with a temporal interval between successive images, suggesting a cortical locus for FFDE.

The paradox of argument strength: how weak arguments undermine the persuasive effects of strong arguments.

This paper analyzes effects of the mutual presentation of weak and strong arguments. Departing from the prevalent "the-more-the-better" heuristic, our research scrutinizes whether the inclusion of weak arguments enhances or diminishes the persuasive impact of strong arguments. Leveraging insights from judgment formation literature, we conducted four experimental studies on political and health-related topics to unravel whether the presenting weak arguments strengthens the persuasive effect of a strong argument (adding) or actually weakens this persuasive effect (averaging). The results show that providing supporting arguments of moderate strength along with a strong argument increases persuasion, representing an additive pattern. However, presenting weak supporting arguments along with a strong argument reduces the persuasive effect of the strong argument, representing an averaging pattern. Exposure to weak arguments diminishes the strength of strong ones, suggesting the omission of weak arguments. These findings underscore the vital role of strategically selecting arguments to optimize persuasion across disciplines.

Robust organ size in Arabidopsis is primarily governed by cell growth rather than cell division patterns.

Organ sizes and shapes are highly reproducible, or robust, within a species and individuals. Arabidopsis thaliana sepals, which are the leaf-like organs that enclose flower buds, have consistent size and shape, indicating robust development. Cell growth is locally heterogeneous due to intrinsic and extrinsic noise. To achieve robust organ shape, fluctuations in cell growth must average to an even growth rate which requires that fluctuations are uncorrelated or anti-correlated in time and space. Here, we live image and quantify the development of sepals with increased or decreased number of cell divisions (lgo mutant and LGO overexpression, respectively), a mutant with altered cell growth variability (ftsh4), and double mutants combining these. Changes in the number of cell divisions do not change the overall growth pattern. By contrast, in ftsh4 mutants, cell growth accumulates in patches of over- and under-growth due to correlations that impair averaging, resulting in increased organ shape variability. Thus, we demonstrate in vivo the number of cell divisions does not affect averaging of cell growth, preserving robust organ morphogenesis, while correlated growth fluctuations impair averaging.

Intracellular Ebola virus nucleocapsid assembly revealed by in situ cryo-electron tomography.

Filoviruses, including the Ebola and Marburg viruses, cause hemorrhagic fevers with up to 90% lethality. The viral nucleocapsid is assembled by polymerization of the nucleoprotein (NP) along the viral genome, together with the viral proteins VP24 and VP35. We employed cryo-electron tomography of cells transfected with viral proteins and infected with model Ebola virus to illuminate assembly intermediates, as well as a 9 Å map of the complete intracellular assembly. This structure reveals a previously unresolved third and outer layer of NP complexed with VP35. The intrinsically disordered region, together with the C-terminal domain of this outer layer of NP, provides the constant width between intracellular nucleocapsid bundles and likely functions as a flexible tether to the viral matrix protein in the virion. A comparison of intracellular nucleocapsids with prior in-virion nucleocapsid structures reveals that the nucleocapsid further condenses vertically in the virion. The interfaces responsible for nucleocapsid assembly are highly conserved and offer targets for broadly effective antivirals.

Analysis of Hamacher power aggregation operators for circular complex p, q-quasirung orthopair fuzzy 2-tuple linguistic sets and their application in green industry development.

Green industry development focuses on balancing economic and financial growth with environmental stewardship and ensuring that companies and industries are contributing positively to both environmental sustainability and prosperity. This manuscript aims to develop the novel technique of circular complex p, q-quasirung orthopair fuzzy 2-tuple linguistic (CCp, q-QOF2-TL) set and their operational laws based on algebraic t-norms and Hamacher t-norms, where the algebraic t-norms and Einstein t-norms are the special cases of the Hamacher t-norms for parameter Ϝ Ⅎ s = l and Ϝ Ⅎ s = 2 . Further, we derive the Hamacher power aggregation operators based on any finite collection of CCp, q-QOF2-TL numbers (CCp, q-QOF2-TLNs), called CCp, q-QOF2-TL Hamacher power average (CCp, q-QOF2-TLHPA) operator, CCp, q-QOF2-TL Hamacher power weighted average (CCp, q-QOF2-TLHPWA) operator, CCp, q-QOF2-TL Hamacher power geometric (CCp, q-QOF2-TLHPG) operator, CCp, q-QOF2-TL Hamacher power weighted geometric (CCp, q-QOF2-TLHPWG) operator, and described their basic properties, called idempotency, monotonicity, and boundedness. Further, we demonstrate the technique of multi-attribute decision-making (MADM) problem based on the above operators to evaluate the major factor that will be playing in the development of the green industry. Finally, we compare the proposed ranking values with the obtained ranking values of existing techniques to show the supremacy and superiority of the initiated approaches.

Rhythmic variance influences the speed but not the accuracy of complex averaging decisions.

When a rhythm makes an event predictable, that event is perceived faster, and typically more accurately. However, the experiments showing this used simple tasks, and most manipulated temporal expectancy by using periodic or aperiodic precursors unrelated to stimulus and task. Three experiments tested the generality of these observations in a complex task in which rhythm was intrinsic to, rather than a precursor of, the information needed to respond: listeners averaged the laterality of a stream of noise bursts. We varied presentation rate, degree of periodicity, and average lateralisation. Decisions following a probe tone were fastest after periodic stimuli, and slowest after the most aperiodic stimuli. Without a probe tone, listeners responded sooner during periodic sequences, thus hearing less information. Periodicity did not benefit accuracy overall. This gain in speed but not accuracy for less information is not reported for simpler tasks. Neural entrainment supplemented by cognitive factors provide a tentative explanation. When the task is inherently complex and demands high attention over long durations, both expected-periodic and unexpected-aperiodic stimuli can increase response amplitude, enhancing stimulus representation, but periodicity increases confidence to respond early. Drift diffusion modelling supports this proposal: aperiodicity modulated the decision threshold, but not the drift rate or non-decision time. Together, these new data and the literature point towards task-dependent effects of temporal expectation on decision-making, showing interactions between rhythmic variance, task complexity, and sources of expectation about stimuli. We suggest the implications are worth exploring to extend understanding of rhythmicity on decision-making to everyday situations.

Recent technical advances in cellular cryo-electron tomography.

Understanding the in situ structure, organization, and interactions of macromolecules is essential for elucidating their functions and mechanisms of action. Cellular cryo-electron tomography (cryo-ET) is a cutting-edge technique that reveals in situ molecular-resolution architectures of macromolecules in their lifelike states. It also provides insights into the three-dimensional distribution of macromolecules and their spatial relationships with various subcellular structures. Thus, cellular cryo-ET bridges the gap between structural biology and cell biology. With rapid advancements, this technique achieved substantial improvements in throughput, automation, and resolution. This review presents the fundamental principles and methodologies of cellular cryo-ET, highlighting recent developments in sample preparation, data collection, and image processing. We also discuss emerging trends and potential future directions. As cellular cryo-ET continues to develop, it is set to play an increasingly vital role in structural cell biology.

Utilizing aggregation operators based on q-rung orthopair neutrosophic soft sets and their applications in multi-attributes decision making problems.

Neutrosophic sets provide greater versatility in dealing with a variety of uncertainties, including independent, partially independent, and entirely dependent scenarios, which q-ROF soft sets cannot handle. Indeterminacy, on the other hand, is ignored completely or partially by q-ROF soft sets. To address this issue, this study offers a unique novel concept as known as q-RONSS, which combines neutrosophic set with q-ROF soft set. This technique addresses vagueness using a set of truth, indeterminacy, and false membership degrees associated with the parametrization tool, with the condition that the sum of the qth power of the truth, indeterminacy, and false membership degrees be less than or equal to one. In addition, this study outlines operational laws for the suggested structure. The main purpose this article is to define some averaging and geometric operators based on the q-rung orthopair neutrosophic soft set. Furthermore, this article provides a step-by-step method and a mathematical model for the suggested techniques. To solve a MADM issue, this research article proposes a numerical example of people selection for a specific position in a real estate business based on a variety of criteria. Finally, to demonstrate the proposed model's superiority and authenticity, this article performs several analyses, including sensitivity analysis, to address the reliability and influence of various parameter "q" values on the alternatives and the ultimate ranking outcomes using the averaging and geometric operators. A comparison of the proposed operators to current operators demonstrates the validity of the proposed structure. Furthermore, a comparison of the proposed structure to current theories demonstrates its superiority by overcoming their limits and offering a more flexible and adaptable framework. Finally, this study reviews the findings and consequences of our research.

Atomic-Scale Characterization of Dilute Dopants in Topological Insulators via STEM-EDS Using Registration and Cell Averaging Techniques.

Magnetic dopants in three-dimensional topological insulators (TIs) offer a promising avenue for realizing the quantum anomalous Hall effect (QAHE) without the necessity for an external magnetic field. Understanding the relationship between site occupancy of magnetic dopant elements and their effect on macroscopic property is crucial for controlling the QAHE. By combining atomic-scale energy-dispersive X-ray spectroscopy (EDS) maps obtained by aberration-corrected scanning transmission electron microscopy (AC-STEM) and novel data processing methodologies, including semi-automatic lattice averaging and frame registration, we have determined the substitutional sites of Mn atoms within the 1.2% Mn-doped Sb2Te3 crystal. More importantly, the methodology developed in this study extends beyond Mn-doped Sb2Te3 to other quantum materials, traditional semiconductors, and even electron irradiation sensitive materials.

Toxicity of rare earth elements (REEs) to marine organisms: Using species sensitivity distributions to establish water quality guidelines for protecting marine life.

A lack of chronic rare earth element (REE) toxicity data for marine organisms has impeded the establishment of numerical REE water quality benchmarks (e.g., guidelines) to protect marine life and assess ecological risk. This study determined the chronic no (significant) effect concentrations (N(S)ECs) and median-effect concentrations (EC50s) of eight key REEs (yttrium (Y), lanthanum (La), cerium (Ce), praseodymium (Pr), neodymium (Nd), gadolinium (Gd), dysprosium (Dy) and lutetium (Lu)) for 30 coastal marine organisms (encompassing 22 phyla and five trophic levels from temperate and tropical habitats). Organisms with calcifying life stages were most vulnerable to REEs, which competitively inhibit calcium uptake. The most sensitive organism was a sea urchin, with N(S)ECs ranging from 0.64 µg/L for Y to 1.9 µg/L for La and Pr, and EC50s ranging from 4.3 µg/L for Y to 14.4 µg/L for Pr. Conversely, the least sensitive organism was a cyanobacterium, with N(S)ECs ranging from 121 µg/L for Y to 469 µg/L for Pr, and EC50s ranging from 889 µg/L for Y to 3000 µg/L for Pr. Median sensitivity varied 215-fold across all organisms. The two-fold difference in median toxicity (µmol/L EC50) among REEs (Y âˆ¼ Gd > Lu âˆ¼ Nd âˆ¼ Dy âˆ¼ Ce > La ∼ Pr) was attributed to offset differences in binding affinity (log K) to cell surface receptors and the percentage of free metal ion (REE3+) in the test waters. The toxicity (EC50) of the remaining REEs (samarium, europium, terbium, holmium, thulium and ytterbium) was predicted using a combination of physicochemical data and measured EC50s for the eight tested REEs, with good agreement between predicted and measured EC50s for selected organisms. Numerical REE water quality guidelines to protect marine life were established using species sensitivity distributions (e.g., for 95 % species protection, values ranged from 1.1 µg/L for Y to 3.0 µg/L for La, Pr or Lu).

Controlling wind turbine tower vibration under external force by applying control systems combination.

The global focus has recently shifted away from fuel-based power sources, and one of the most important projects for energy production is wind energy. To maintain low costs, the current research examines the problem of vibrations affecting wind turbine towers' performance (WTTs). In particular, the tower, resulting from excessive vibrations, can negatively affect a structure's power output and service life, as it can cause fatigue. Therefore, we conducted numerical tests on various types of controlled systems. Our tests revealed that combining a new technique cubic negative velocity control (CNVC) and linear negative acceleration control (LNAC) was the most effective and cost-efficient option for vibration damping. This solution was derived by using an approximation method for the averaging technique. The external force is an important component of a nonlinear dynamic system and can be characterized by two-degree-of-freedom (2-DOF) differential coupled equations. After implementing the control measures, we conducted a numerical analysis of the vibration values before and after the operation. Stability is studied numerically. The numerical and approximate solutions were confirmed through the frequency response equation and time history with fourth-order Runge-Kutta (RK-4). Finally, we investigated the effect of parameters and compared our results with those of previously published studies.

Selection of appropriate location based on signed distance based ranking approach with interval type-2 trapezoidal fuzzy preference relations.

In real life situation, it is often difficult to judge the relative importance of different parameters being considered for evaluating some alternatives. In the context of fuzzy sets, it is a situation where it is difficult to define precise membership grades for attribute values. Here we require more generalized type of fuzzy sets which have a greater representational power than ordinary fuzzy sets. For this purpose we use "interval type-2 trapezoidal fuzzy preference relations (IT2TrFPRs)" in this article as a generalization of fuzzy preference relations and consider the environment discussed above, where there is no information on priority weights. A collective decision matrix will be constructed on the basis of hybrid averages using weighted averaging and signed distance based OWA operation. Then a least deviation model will be employed in order to determine the priority weight vectors. Finally, the alternatives will be ranked on the basis of weighted normalized signed distance of each alternative from the ideal solution. Moreover, a real life example of location selection is illustrated to elaborate the effectiveness of the proposed scheme.

Averaging Times for Pulse Oximeter Measurements - A Review of Manuscripts Published in the Top Five Sleep Medicine Journals.

Purpose: Clinical management decisions often rely on a patient's SpO2 level and desaturation rate. Limitations include that measurements depend on the averaging time (AVT) used, which is particularly relevant to sleep medicine, but has yet received little attention. Methods: Cross-sectional review of studies reporting pulse oximeter saturation (SpO2) measurements published in 5 leading sleep medicine journals. All papers published between 2017 and 2023 reporting SpO2 measurements were screened regarding the AVT used. Results: Of 193 papers identified, 151 were included; of these, only 9 studies mentioned the AVT, 4 of these were published in one journal. The AVT ranged from zero (beat-to-beat-mode) to 10s, with 3s being used most often (33.3%), followed by 2s (22.2%). Conclusion: The AVT is only rarely mentioned in sleep medicine papers, despite its influence on sleep study results. Reported AVTs were heterogenous. Further research is warranted to set up guidelines for using or reporting the AVT.

Abundance-diversity relationship as a unique signature of temporal scaling in the fossil record.

Species diversity increases with the temporal grain of samples according to the species-time relationship (STR), impacting palaeoecological analyses because the temporal grain (time averaging) of fossil assemblages varies by several orders of magnitude. We predict a positive relation between total abundance and sample size-independent diversity (ADR) in fossil assemblages because an increase in time averaging, determined by a decreasing sediment accumulation, should increase abundance and depress species dominance. We demonstrate that, in contrast to negative ADR of non-averaged living assemblages, the ADR of Holocene fossil assemblages is positive, unconditionally or when conditioned on the energy availability gradient. However, the positive fossil ADR disappears when conditioned on sediment accumulation, demonstrating that ADR is a signature of diversity scaling induced by variable time averaging. Conditioning ADR on sediment accumulation can identify and remove the scaling effect caused by time averaging, providing an avenue for unbiased biodiversity comparisons across space and time.

Mendelian randomization and Bayesian model averaging of autoimmune diseases and Long COVID.

Background: Following COVID-19, reports suggest Long COVID and autoimmune diseases (AIDs) in infected individuals. However, bidirectional causal effects between Long COVID and AIDs, which may help to prevent diseases, have not been fully investigated. Methods: Summary-level data from genome-wide association studies (GWAS) of Long COVID (N = 52615) and AIDs including inflammatory bowel disease (IBD) (N = 377277), Crohn's disease (CD) (N = 361508), ulcerative colitis (UC) (N = 376564), etc. were employed. Bidirectional causal effects were gauged between AIDs and Long COVID by exploiting Mendelian randomization (MR) and Bayesian model averaging (BMA). Results: The evidence of causal effects of IBD (OR = 1.06, 95% CI = 1.00-1.11, p = 3.13E-02), CD (OR = 1.10, 95% CI = 1.01-1.19, p = 2.21E-02) and UC (OR = 1.08, 95% CI = 1.03-1.13, p = 2.35E-03) on Long COVID was found. In MR-BMA, UC was estimated as the highest-ranked causal factor (MIP = 0.488, MACE = 0.035), followed by IBD and CD. Conclusion: This MR study found that IBD, CD and UC had causal effects on Long COVID, which suggests a necessity to screen high-risk populations.