Tag: statistics

Int J Inj Contr Saf Promot. 2023 Oct 9:1-15. doi: 10.1080/17457300.2023.2266841. Online ahead of print.

ABSTRACT

Road accidents remain a serious problem and directly affect drivers. Therefore, the perspectives of drivers are important in improving road safety. The objectives of this study are to empirically examine damage due to road accidents using the willingness-to-pay (WTP) approach and to analyze the factors that influence WTP at the driver and district levels. This study obtained data on WTP derived from car drivers across Thailand, which covers 96 districts. The value of statistical life was 824,344 USD per fatality (2,296 million USD annually). The results of Multilevel Structural Equation Modeling revealed a statistically important insight. At the driver level, the Health Belief Model and sociodemographic exert influence on the intention to pay. The demographic factor that has the greatest influence on perceived risk and leads to a high intention to pay is the working age group (γ = 0.826). However, when considering the HBM, perceived susceptibility (γ = 0.901) emerges as the most valuable factor influencing drivers’ concerns about road accidents. On the other hand, district-level factors have a negative influence on the intention to pay for road safety measures. Among these factors, the law enforcement (γ = -0.555) practices implemented by local authorities have the most significant impact on drivers’ perspectives and intentions regarding WTP. This finding can be used as a guideline for budget allocation and policy recommendation for policymakers in improving road safety according to the area contexts.

PMID:37812734 | DOI:10.1080/17457300.2023.2266841

ACS Nano. 2023 Oct 9. doi: 10.1021/acsnano.3c04037. Online ahead of print.

ABSTRACT

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of “Nano-Tumor Database”, which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded “Nano-Tumor Database” and several statistical models that may help nanomedicine design in the future.

PMID:37812732 | DOI:10.1021/acsnano.3c04037

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2307584120. doi: 10.1073/pnas.2307584120. Epub 2023 Oct 9.

ABSTRACT

As social animals, people are highly sensitive to the attention of others. Seeing someone else gaze at an object automatically draws one’s own attention to that object. Monitoring the attention of others aids in reconstructing their emotions, beliefs, and intentions and may play a crucial role in social alignment. Recently, however, it has been suggested that the human brain constructs a predictive model of other people’s attention that is far more involved than a moment-by-moment monitoring of gaze direction. The hypothesized model learns the statistical patterns in other people’s attention and extrapolates how attention is likely to move. Here, we tested the hypothesis of a predictive model of attention. Subjects saw movies of attention displayed as a bright spot shifting around a scene. Subjects were able to correctly distinguish natural attention sequences (based on eye tracking of prior participants) from altered sequences (e.g., played backward or in a scrambled order). Even when the attention spot moved around a blank background, subjects could distinguish natural from scrambled sequences, suggesting a sensitivity to the spatial-temporal statistics of attention. Subjects also showed an ability to recognize the attention patterns of different individuals. These results suggest that people possess a sophisticated model of the normal statistics of attention and can identify deviations from the model. Monitoring attention is therefore more than simply registering where someone else’s eyes are pointing. It involves predictive modeling, which may contribute to our remarkable social ability to predict the mind states and behavior of others.

PMID:37812722 | DOI:10.1073/pnas.2307584120

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2308496120. doi: 10.1073/pnas.2308496120. Epub 2023 Oct 9.

ABSTRACT

Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.

PMID:37812720 | DOI:10.1073/pnas.2308496120

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2307552120. doi: 10.1073/pnas.2307552120. Epub 2023 Oct 9.

ABSTRACT

There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress-strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity.

PMID:37812709 | DOI:10.1073/pnas.2307552120

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2306317120. doi: 10.1073/pnas.2306317120. Epub 2023 Oct 9.

ABSTRACT

Space weather, including solar storms, can impact Earth by disturbing the geomagnetic field. Despite the known dependence of birds and other animals on geomagnetic cues for successful seasonal migrations, the potential effects of space weather on organisms that use Earth’s magnetic field for navigation have received little study. We tested whether space weather geomagnetic disturbances are associated with disruptions to bird migration at a macroecological scale. We leveraged long-term radar data to characterize the nightly migration dynamics of the nocturnally migrating North American avifauna over 22 y. We then used concurrent magnetometer data to develop a local magnetic disturbance index associated with each radar station (ΔBmax), facilitating spatiotemporally explicit analyses of the relationship between migration and geomagnetic disturbance. After controlling for effects of atmospheric weather and spatiotemporal patterns, we found a 9 to 17% decrease in migration intensity in both spring and fall during severe space weather events. During fall migration, we also found evidence for decreases in effort flying against the wind, which may represent a depression of active navigation such that birds drift more with the wind during geomagnetic disturbances. Effort flying against the wind in the fall was most reduced under both overcast conditions and high geomagnetic disturbance, suggesting that a combination of obscured celestial cues and magnetic disturbance may disrupt navigation. Collectively, our results provide evidence for community-wide avifaunal responses to geomagnetic disturbances driven by space weather during nocturnal migration.

PMID:37812699 | DOI:10.1073/pnas.2306317120

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2307354120. doi: 10.1073/pnas.2307354120. Epub 2023 Oct 9.

ABSTRACT

Entrainment of dry air into clouds strongly influences cloud optical and precipitation properties and the response of clouds to aerosol perturbations. The response of cloud droplet size distributions to entrainment-mixing is examined in the Pi convection-cloud chamber that creates a turbulent, steady-state cloud. The experiments are conducted by injecting dry air with temperature (T_e) and flow rate (Q_e) through a flange in the top boundary, into the otherwise well-mixed cloud, to mimic the entrainment-mixing process. Due to the large-scale circulation, the downwind region is directly affected by entrained dry air, whereas the upwind region is representative of the background conditions. Droplet concentration (Cn) and liquid water content (L) decrease in the downwind region, but the difference in the mean diameter of droplets (D_m) is small. The shape of cloud droplet size distributions relative to the injection point is unchanged, to within statistical uncertainty, resulting in a signature of inhomogeneous mixing, as expected for droplet evaporation times small compared to mixing time scales. As T_e and Q_e of entrained air increase, however, Cn, L, and D_m of the whole cloud system decrease, resulting in a signature of homogeneous mixing. The apparent contradiction is understood as the cloud microphysical responses to entrainment and mixing differing on local and global scales: locally inhomogeneous and globally homogeneous. This implies that global versus local sampling of clouds can lead to seemingly contradictory results for mixing, which informs the long-standing debate about the microphysical response to entrainment and the parameterization of this process for coarse-resolution models.

PMID:37812695 | DOI:10.1073/pnas.2307354120

Angew Chem Int Ed Engl. 2023 Oct 9:e202313864. doi: 10.1002/anie.202313864. Online ahead of print.

ABSTRACT

Docking of alkali metal ions to water-soluble macrocyclic receptors generally reduces the affinity of guest molecules due to competitive binding. The idea that solvation water molecules could display a larger steric hindrance towards guest binding than cations has not been considered to date. We show that the docking of large cations to cucurbit[5]uril (CB5) unexpectedly increases (by a factor of 5-8) the binding of hydrophobic guests, methane and ethane. This is due to the removal of water molecules from the carbonyl portals of CB5 during cation binding, which frees up space for hydrophobe encapsulation. In contrast, smaller cations like sodium protrude deeply into the cavity of CB5 and cause the expected decrease in binding, such that the rational selection of alkali cations allows for a variation of up to a factor of 20 in binding of methane and ethane. The statistical analysis of crystallographic data shows that the cavity volume of CB5 can be enlarged by placing large alkali ions (Rb+ and Cs+) centro-symmetrically at the portals. The results reveal a hitherto elusive steric hindrance of solvation water molecules near receptor binding sites, which is pertinent for the design of supramolecular catalysts and the understanding of biological receptors.

PMID:37812692 | DOI:10.1002/anie.202313864

PLoS Comput Biol. 2023 Oct 9;19(10):e1011520. doi: 10.1371/journal.pcbi.1011520. Online ahead of print.

ABSTRACT

Vector or host competence can be defined as the ability of an individual to become infected and subsequently transmit a pathogen. Assays to measure competence play a key part in the assessment of the factors affecting mosquito-borne virus transmission and of potential pathogen-blocking control tools for these viruses. For mosquitoes, competence for arboviruses can be measured experimentally and results are usually analysed using standard statistical approaches. Here we develop a mechanistic approach to studying within-mosquito virus dynamics that occur during vector competence experiments. We begin by developing a deterministic model of virus replication in the mosquito midgut and subsequent escape and replication in the hemocoel. We then extend this to a stochastic model to capture the between-individual variation observed in vector competence experiments. We show that the dose-response of the probability of mosquito midgut infection and variation in the dissemination rate can be explained by stochastic processes generated from a small founding population of virions, caused by a relatively low rate of virion infection of susceptible cells. We also show that comparing treatments or species in competence experiments by fitting mechanistic models could provide further insight into potential differences. Generally, our work adds to the growing body of literature emphasizing the importance of intrinsic stochasticity in biological systems.

PMID:37812643 | DOI:10.1371/journal.pcbi.1011520

PLoS Pathog. 2023 Oct 9;19(10):e1011722. doi: 10.1371/journal.ppat.1011722. Online ahead of print.

ABSTRACT

Sequential dengue virus (DENV) infections often generate neutralizing antibodies against all four DENV serotypes and sometimes, Zika virus. Characterizing cross-flavivirus broadly neutralizing antibody (bnAb) responses can inform countermeasures that avoid enhancement of infection associated with non-neutralizing antibodies. Here, we used single cell transcriptomics to mine the bnAb repertoire following repeated DENV infections. We identified several new bnAbs with comparable or superior breadth and potency to known bnAbs, and with distinct recognition determinants. Unlike all known flavivirus bnAbs, which are IgG1, one newly identified cross-flavivirus bnAb (F25.S02) was derived from IgA1. Both IgG1 and IgA1 versions of F25.S02 and known bnAbs displayed neutralizing activity, but only IgG1 enhanced infection in monocytes expressing IgG and IgA Fc receptors. Moreover, IgG-mediated enhancement of infection was inhibited by IgA1 versions of bnAbs. We demonstrate a role for IgA in flavivirus infection and immunity with implications for vaccine and therapeutic strategies.

PMID:37812640 | DOI:10.1371/journal.ppat.1011722