Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem.

A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

A new family of quantile regression models applied to nutritional data.

This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line.

Modeling tropical tuna shifts: An inflated power logit regression approach.

We introduce a new class of zero-or-one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest that our modeling approach is adequate and capable of handling the outliers in the data. It exhibited superior performance compared to rival models in both diagnostic analysis and regarding the inference robustness. We offer a user-friendly method for fitting IPL regression models in practical applications.

The InBIO Barcoding Initiative Database: DNA barcodes of Orthoptera from Portugal.

Background: The InBIO Barcoding Initiative (IBI) Orthoptera dataset contains records of 420 specimens covering all the eleven Orthoptera families occurring in Portugal. Specimens were collected in continental Portugal from 2005 to 2021 and were morphologically identified to species level by taxonomists. A total of 119 species were identified corresponding to about 77% of all the orthopteran species known from continental Portugal. New information: DNA barcodes of 54 taxa were made public for the first time at the Barcode of Life Data System (BOLD). Furthermore, the submitted sequences were found to cluster in 129 BINs (Barcode Index Numbers), 35 of which were new additions to the Barcode of Life Data System (BOLD). All specimens have their DNA barcodes publicly accessible through BOLD online database. Stenobothruslineatus is recorded for the first time for continental Portugal. This dataset greatly increases the knowledge on the DNA barcodes and distribution of Orthoptera from Portugal. All DNA extractions and most specimens are deposited in the IBI collection at CIBIO, Research Center in Biodiversity and Genetic Resources.

Which contributes more to the relict flora distribution pattern in East Asia, geographical processes or climate change? New evidence from the phylogeography of Rehderodendron kwangtungense.

BACKGROUND: Relict species are important for enhancing the understanding of modern biogeographic distribution patterns. Although both geological and climatic changes since the Cenozoic have affected the relict flora in East Asia, the contributions of geographical processes remain unclear. In this study, we employed restriction-site associated DNA sequencing (RAD-seq) and shallow genome sequencing data, in conjunction with ecological niche modeling (ENM), to investigate the spatial genetic patterns and population differentiation history of the relict species Rehderodendron kwangtungense Chun. RESULTS: A total of 138 individuals from 16 populations were collected, largely covering the natural distribution of R. kwangtungense. The genetic diversity within the R. kwangtungense populations was extremely low (HO = 0.048 ± 0.019; HE = 0.033 ± 0.011). Mantel tests revealed isolation-by-distance pattern (R2 = 0.38, P < 0.001), and AMOVA analysis showed that the genetic variation of R. kwangtungense occurs mainly between populations (86.88%, K = 7). Between 23 and 21 Ma, R. kwangtungense underwent a period of rapid differentiation that coincided with the rise of the Himalayas and the establishment of the East Asian monsoon. According to ENM and population demographic history, the suitable area and effective population size of R. kwangtungense decreased sharply during the glacial period and expanded after the last glacial maximum (LGM). CONCLUSION: Our study shows that the distribution pattern of southern China mountain relict flora may have developed during the panplain stage between the middle Oligocene and the early Miocene. Then, the flora later fragmented under the force of orogenesis, including intermittent uplift during the Cenozoic Himalayan orogeny and the formation of abundant rainfall associated with the East Asian monsoon. The findings emphasized the predominant role of geographical processes in shaping relict plant distribution patterns.

Is competitive ability the key adaptation to benign environments? Revisiting experiments on closely related species of tidal plants.

A central goal in biology is to understand which traits underlie adaptation to different environments. Yet, few studies have examined the relative contribution of competitive ability towards adaptive divergence among species occupying distinct environments. Here, we test the relative importance of competitive ability as an adaptation to relatively benign versus challenging environments, using previously published studies of closely related species pairs of primarily tidal plants subjected to reciprocal removal with transplant experiments in nature. Subordinate species typically occupy more challenging environments and showed consistent evidence for adaptation to challenging conditions, with no significant competitive effect on non-local, dominant species. In contrast, dominant species typically occupy relatively benign environments and performed significantly better than non-local, subordinate species that faced competition from the dominant species. Surprisingly, when the two species were not allowed to compete, the subordinate species performed as well as the dominant species in the benign environments where the subordinate species do not occur. These results suggest that competitive ability is the most important adaptation distinguishing the species that occupy relatively benign environments. The limited scope and number of suitable experimental studies encourage future work to test if these results are generalizable across taxa and environments.

Belief inducibility and informativeness.

We consider a group of receivers who share a common prior on a finite state space and who observe private correlated messages that are contingent on the true state of the world. Our focus lies on the beliefs of receivers induced via the signal chosen by the sender and we provide a comprehensive analysis of the inducible distributions of posterior beliefs. Classifying signals as minimal, individually minimal, and language-independent, we show that any inducible distribution can be induced by a language-independent signal. We investigate the role of the different classes of signals for the amount of higher order information that is revealed to receivers. The least informative signals that induce a fixed distribution over posterior belief profiles lie in the relative interior of the set of all language-independent signals inducing that distribution.

Relativistic Roots of κ-Entropy.

The axiomatic structure of the κ-statistcal theory is proven. In addition to the first three standard Khinchin-Shannon axioms of continuity, maximality, and expansibility, two further axioms are identified, namely the self-duality axiom and the scaling axiom. It is shown that both the κ-entropy and its special limiting case, the classical Boltzmann-Gibbs-Shannon entropy, follow unambiguously from the above new set of five axioms. It has been emphasized that the statistical theory that can be built from κ-entropy has a validity that goes beyond physics and can be used to treat physical, natural, or artificial complex systems. The physical origin of the self-duality and scaling axioms has been investigated and traced back to the first principles of relativistic physics, i.e., the Galileo relativity principle and the Einstein principle of the constancy of the speed of light. It has been shown that the κ-formalism, which emerges from the κ-entropy, can treat both simple (few-body) and complex (statistical) systems in a unified way. Relativistic statistical mechanics based on κ-entropy is shown that preserves the main features of classical statistical mechanics (kinetic theory, molecular chaos hypothesis, maximum entropy principle, thermodynamic stability, H-theorem, and Lesche stability). The answers that the κ-statistical theory gives to the more-than-a-century-old open problems of relativistic physics, such as how thermodynamic quantities like temperature and entropy vary with the speed of the reference frame, have been emphasized.

A heavy-tailed model for analyzing miRNA-seq raw read counts.

This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model using discrete stable distributions is proposed as a novel approach to better capture the heterogeneity and extreme values commonly observed in miRNA-seq data. Additionally, the parameters of the discrete stable distribution are proposed as an alternative target for differential expression analysis. An R package for computing and estimating the discrete stable distribution is provided. The proposed model is applied to miRNA-seq raw counts from the Norwegian Women and Cancer Study (NOWAC) and the Cancer Genome Atlas (TCGA) databases. The goodness-of-fit is compared with the popular Poisson and negative binomial distributions, and the discrete stable distributions are found to give a better fit for both datasets. In conclusion, the use of discrete stable distributions is shown to potentially lead to more accurate modeling of the underlying biological processes.

Estimating the proportions and latencies of reaction time outliers: A pooling method and case study of lexical decision tasks.

A methodological problem in most reaction time (RT) studies is that some measured RTs may be outliers-that is, they may be very fast or very slow for reasons unconnected to the task-related processing of interest. Numerous ad hoc methods have been suggested to discriminate between such outliers and the valid RTs of interest, but it is extremely difficult to determine how well these methods work in practice because virtually nothing is known about the actual characteristics of outliers in real RT datasets. This article proposes a new method of pooling cumulative distribution function values for examining empirical RT distributions to assess both the proportions of outliers and their latencies relative to those of the valid RTs. As the method is developed, its strengths and weaknesses are examined using simulations based on previously suggested ad hoc models for RT outliers with particular assumed proportions and distributions of valid RTs and outliers. The method is then applied to several large RT datasets from lexical decision tasks, and the results provide the first empirically based description of outlier RTs. For these datasets, fewer than 1% of the RTs seem to be outliers, and the median outlier latency appears to be approximately 4-6 standard deviations of RT above the mean of the valid RT distribution.

Exploring ultrasonic communication in mice treated with Cannabis sativa oil: Audio data processing and correlation study with different behaviours.

Studying ultrasonic vocalizations (USVs) plays a crucial role in understanding animal communication, particularly in the field of ethology and neuropharmacology. Communication is associated with social behaviour; so, USVs study is a valid assay in behavioural readout and monitoring in this context. This paper delved into an investigation of ultrasonic communication in mice treated with Cannabis sativa oil (CS mice), which has been demonstrated having a prosocial effect on behaviour of mice, versus control mice (vehicle-treated, VH mice). To conduct this study, we created a dataset by recording audio-video files and annotating the duration of time that test mice spent engaging in social activities, along with categorizing the types of emitted USVs. The analysis encompassed the frequency of individual sounds as well as more complex sequences of consecutive syllables (patterns). The primary goal was to examine the extent and nature of diversity in ultrasonic communication patterns emitted by these two groups of mice. As a result, we observed statistically significant differences for each considered pattern length between the two groups of mice. Additionally, the study extended its research by considering specific behaviours, aiming to ascertain whether dissimilarities in ultrasonic communication between CS and VH mice are more pronounced or subtle within distinct behavioural contexts. Our findings suggest that while there is variation in USV communication between the two groups of mice, the degree of this diversity may vary depending on the specific behaviour being observed.

Do efficacy results obtained from randomized controlled trials translate to effectiveness data from observational studies for relapsing-remitting multiple sclerosis?

BACKGROUND: Randomized controlled trials are considered the gold standard in regulatory decision making, as observational studies are known to have important methodological limitations. However, real-world evidence may be helpful in specific situations. This review investigates how the effect estimates obtained from randomized controlled trials compare to those obtained from observational studies, using drug therapy for relapsing-remitting multiple sclerosis as an example. STUDY DESIGN AND SETTING: A systematic review of randomized controlled trials and observational studies was conducted. The primary outcome was the annualized relapse rate. Using (network) meta-analysis together with posterior predictive distributions, the drug-specific rate ratios from the network of randomized controlled trials were compared with those from the network of observational studies. RESULTS: Effect estimates from 26 observational studies showed greater magnitudes and were less precise compared to estimates obtained from 21 randomized controlled trials. Twenty of the 28 treatment comparisons between designs had similar rate ratios. Seven inconsistencies in observed rate ratios could be attributed to two specific disease-modifying therapies. CONCLUSION: In this case study, estimates from observational studies predominantly agreed with estimates from randomized controlled trials given their posterior predictive distributions. Multiple observational studies together may therefore supplement additional pivotal randomized controlled trials in relapsing-remitting multiple sclerosis, for instance facilitating the extrapolation of trial results to the broader patient population.

EXPRESS: Modeling the Impact of Single vs Dual Presentation on Visual Discrimination Across Resolutions.

Visual categorisation relies on our ability to extract useful diagnostic information from complex stimuli. To do this, we can utilise both the 'high-level' and 'low-level' information in a stimulus, however the extent to which changes in these properties impact the decision-making process is less clear. We manipulated participants' access to high-level category features via gradated reductions to image resolution while exploring the impact of access to additional category features through a dual stimulus presentation when compared to single stimulus presentation. Results showed that while increasing image resolution consistently resulted in better choice performance, no benefit was found for dual presentation over single presentation, despite responses for dual presentation being slower compared to single presentation. Applying the diffusion decision model revealed increases in drift rate as a function of resolution, but no change in drift rate for single versus dual presentation. The increase in response time for dual presentation was instead accounted for by an increase in response caution for dual presentations. These findings suggest that while increasing access to high-level features (via increased resolution) can improve participants' categorisation performance, increasing access to both high- and low-level features (via an additional stimulus) does not.

Designing and implementing an experimental survey on knowledge and perceptions about alcohol warning labels.

OBJECTIVES: This paper describes the design and implementation of an online survey experiment to investigate the effects of alcohol warning labels on alcohol-related knowledge, risk perceptions and intentions. METHOD: The survey collected self-reported data from 14 European countries through two waves of data collection with different recruitment strategies: dissemination via social media and public health agencies was followed by paid-for Facebook ads. The latter strategy was adopted to achieve broader population representation. Post-stratification weighting was used to match the sample to population demographics. RESULTS: The survey received over 34,000 visits and resulted in a sample size of 19,601 participants with complete data on key sociodemographic characteristics. The responses in the first wave were over-representing females and higher educated people, thus the dissemination was complemented by the paid-for Facebook ads targeting more diverse populations but had higher attrition rate. CONCLUSION: Experiments can be integrated into general population surveys. Pan-European results can be achieved with limited resources and a combination of sampling methods to compensate for different biases, and statistical adjustments.

Linear Regression of Sampling Distributions of the Mean.

We show that the simple and multiple linear regression coefficients and the coefficient of determination R2 computed from sampling distributions of the mean (with or without replacement) are equal to the regression coefficients and coefficient of determination computed with individual data. Moreover, the standard error of estimate is reduced by the square root of the group size for sampling distributions of the mean. The result has applications when formulating a distance measure between two genes in a hierarchical clustering algorithm. We show that the Pearson R coefficient can measure how differential expression in one gene correlates with differential expression in a second gene.

Free Electron-Plasmon Coupling Strength and Near-Field Retrieval through Electron Energy-Dependent Cathodoluminescence Spectroscopy.

Tightly confined optical near fields in plasmonic nanostructures play a pivotal role in important applications ranging from optical sensing to light harvesting. Energetic electrons are ideally suited to probing optical near fields by collecting the resulting cathodoluminescence (CL) light emission. Intriguingly, the CL intensity is determined by the near-field profile along the electron propagation direction, but the retrieval of such field from measurements has remained elusive. Furthermore, the conditions for optimum electron near-field coupling in plasmonic systems are critically dependent on such field and remain experimentally unexplored. In this work, we use electron energy-dependent CL spectroscopy to study the tightly confined dipolar mode in plasmonic gold nanoparticles. By systematically studying gold nanoparticles with diameters in the range of 20-100 nm and electron energies from 4 to 30 keV, we determine how the coupling between swift electrons and the optical near fields depends on the energy of the incoming electron. The strongest coupling is achieved when the electron speed equals the mode phase velocity, meeting the so-called phase-matching condition. In aloof experiments, the measured data are well reproduced by electromagnetic simulations, which explain that larger particles and faster electrons favor a stronger electron near-field coupling. For penetrating electron trajectories, scattering at the particle produces severe corrections of the trajectory that defy existing theories based on the assumption of nonrecoil condition. Therefore, we develop a first-order recoil correction model that allows us to account for inelastic electron scattering, rendering better agreement with measured data. Finally, we consider the albedo of the particles and find that, to approach unity coupling, a highly confined electric field and very slow electrons are needed, both representing experimental challenges. Our findings explain how to reach unity-order coupling between free electrons and confined excitations, helping us understand fundamental aspects of light-matter interaction at the nanoscale.

Environmental Markers of Plastics and Microplastics.

The slow reaction rates to chemical and photochemical degradation are well-known properties of plastics. However, large plastic surfaces exposed to environmental conditions release particles and compounds that affect ecosystems and human health. The aim of this work was to identify compounds associated with the degradation of polyethylene (PE), polystyrene (PS), and polyvinyl chloride (PVC) microplastics (markers) on silica and sand and evaluate their use to screen microplastics on natural sand. Products were identified by using targeted and untargeted LC-HRMS analysis. All polymers underwent chemical oxidation on silica. PE released dicarboxylic acids (HO2C-(CH2)n-CO2H (n = 4-30), while PS released cis/trans-chalcone, trans-dypnone, 3-phenylpropiophenone, and dibenzoylmethane. PVC released dicarboxylic acids and aromatic compounds. Upon irradiation, PE was stable while PS released the same compounds as under chemical oxidation but at lower yields. Under the above condition, PVC generated HO2C-[CH2-CHCl]n-CH2-CO2H and HO2C-[CH2-CHCl]n-CO2H (n = 2-19) dicarboxylic acids. The same products were detected on sand but at a lower concentration than on silica due to better retention within the pores. Detection of markers of PE and PS on natural sand allowed us to screen microplastics by following a targeted analysis. Markers of PVC were not detected before or after thermal/photo-oxidation due to the low release of compounds and limitations associated with surface exposure/penetration of radiation.

Spatio-seasonal patterns and sources of major ions in the Longjiang River catchment, Southern China.

River water quality is closely related to the major ion sources and hydrological conditions. However, there is a limited cognition about the geochemical sources and the seasonal variations of major ions. Thus, in this study, a total of 90 water samples were collected from the Longjiang River and its three tributaries in the dry and wet seasons. The samples were analyzed, including major ion concentrations and physicochemical parameters. Statistical analysis, such as correlation analysis and principal component analysis (PCA), was employed to investigate the spatial and seasonal variations in major ion composition and their respective sources. Our study revealed that the predominant major ions in the studied samples are Ca2+, Mg2+, HCO - 3, and SO2 - 4. Most of ions exhibited notable spatial disparities attributable to variations in geological settings and human activities. Regions characterized by igneous rock outcrops tend to exhibit higher levels of K+ and Na+, while areas with higher population densities in the middle and downstream segments show elevated concentrations of Cl-, NO - 3, SO2 - 4, Na+, and K+. The observed peak SO2 - 4 levels may be attributed to active mining operations. Most parameters displayed higher values in flood season than those in dry season due to dilution effects. Stoichiometric analysis indicated that carbonate weathering inputs contribute to over 85% of the mean total cation concentrations in the water, followed by contributions from silicates, atmospheric deposition, and anthropogenic inputs. On the whole, although the water quality remains non-polluted and is suitable for drinking and irrigation purposes, the enrichment of SO2 - 4 and NO - 3 may contribute to water eutrophication. Caution is warranted during the dry season due to reduced water flow resulting from dam interceptions and limited dilution capacity, potentially leading to elevated pollutant concentrations. Taken together, our results provided a scientific basis for water quality managements of monsoon rivers.

Cellular Distribution and Intracellular Localization of Different Sizes of Fluorescent Thiol-Organosilica Particles in Mouse Lungs.

We investigated the distribution of intratracheally administered thiol-organosilica (thiol-OS) particles in mouse lungs. Toward this end, single doses of thiol-OS particles containing fluorescein (140 nm in diameter) (F140) and rhodamine B (Rh) (Rh160, Rh280, Rh420, Rh640, and Rh1630 with diameters of 160, 280, 420, 640, and 1630 nm, respectively) were administered. After 24 h, fluorescence imaging revealed homogeneous fluorescence with a patchier pattern on the lung surface and no difference among the six particle sizes. Simultaneous dual administration of Rh and F140 particles did not reveal any size-dependent differences in the lung surface fluorescence. Fluorescence microscopy of the lung sections revealed a similar tissue distribution in the fluorescent areas of Rhs and F140. Some fluorescent areas showed one type of particle fluorescence or only one fluorescence. Cellular distribution of particles was observed in bronchoalveolar lavage cells and lung sections under a high magnification, and correlative light and electron microscopy revealed large cells with fluorescence corresponding to both particle types and small cells with fluorescence of individual particle types, indicating a cell-subset-dependent particle size effect. Rh280, Rh420, and Rh640 exhibited significant size effects and were taken up by alveolar macrophages. Extracellular particles were observed, indicating that saturation exceeded the particle dose threshold in the alveoli. F140 taken up by small and large macrophages colocalized with CD68, CD11c, and CD11b and correlated with CD11c. The size effect, intracellular localization, and extracellular distribution of particles provide insights into lung and systemic drug delivery.

Data-driven determination of zooplankton bioregions and robustness analysis.

Identifying biogeographic regions through cluster analysis of species distribution data is a common method for partitioning ecosystems. Selecting the appropriate cluster analysis method requires a comparison of multiple algorithms. In this study, we demonstrate a data-driven process to select a method for bioregionalization based on community data and test its robustness to data variability following these steps: •We aggregated and curated zooplankton community observations from expeditions in the Northeast Pacific.•We determined the best bioregionalization approach by comparing nine cluster analysis methods using ten goodness of clustering indices.•We evaluated the robustness of the bioregionalization to different sources of sampling and taxonomic variability by comparing the bioregionalization of the overall dataset with bioregionalizations of subsets of the data. The K-means clustering of the log-chord transformed abundance was selected as the optimal method for bioregionalization of the zooplankton dataset. This clustering resulted in the emergence of four bioregions along the cross-shelf gradient: the Offshore, Deep Shelf, Nearshore, and Deep Fjord bioregions. The robustness analyses demonstrated that the bioregionalization was consistent despite variability in the spatial and temporal frequency of sampling, sampling methodology, and taxonomic coverage.