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BACKGROUND: Although uncertainties exist regarding implementation, artificial intelligence-driven generative language models (GLMs) have enormous potential in medicine. Deployment of GLMs could improve patient comprehension of clinical texts and improve low health literacy. OBJECTIVE: The goal of this study is to evaluate the potential of ChatGPT-3.5 and GPT-4 to tailor the complexity of medical information to patient-specific input education level, which is crucial if it is to serve as a tool in addressing low health literacy. METHODS: Input templates related to 2 prevalent chronic diseases-type II diabetes and hypertension-were designed. Each clinical vignette was adjusted for hypothetical patient education levels to evaluate output personalization. To assess the success of a GLM (GPT-3.5 and GPT-4) in tailoring output writing, the readability of pre- and posttransformation outputs were quantified using the Flesch reading ease score (FKRE) and the Flesch-Kincaid grade level (FKGL). RESULTS: Responses (n=80) were generated using GPT-3.5 and GPT-4 across 2 clinical vignettes. For GPT-3.5, FKRE means were 57.75 (SD 4.75), 51.28 (SD 5.14), 32.28 (SD 4.52), and 28.31 (SD 5.22) for 6th grade, 8th grade, high school, and bachelors, respectively; FKGL mean scores were 9.08 (SD 0.90), 10.27 (SD 1.06), 13.4 (SD 0.80), and 13.74 (SD 1.18). GPT-3.5 only aligned with the prespecified education levels at the bachelors degree. Conversely, GPT-4s FKRE mean scores were 74.54 (SD 2.6), 71.25 (SD 4.96), 47.61 (SD 6.13), and 13.71 (SD 5.77), with FKGL mean scores of 6.3 (SD 0.73), 6.7 (SD 1.11), 11.09 (SD 1.26), and 17.03 (SD 1.11) for the same respective education levels. GPT-4 met the target readability for all groups except the 6th-grade FKRE average. Both GLMs produced outputs with statistically significant differences (P

Here, we tested the respective contributions of primate premotor and prefrontal cortex to support vocal behavior. We applied a model-based generalized linear model (GLM) analysis that better accounts for the inherent variance in natural, continuous behaviors to characterize the activity of neurons throughout the frontal cortex as freely moving marmosets engaged in conversational exchanges. While analyses revealed functional clusters of neural activity related to the different processes involved in the vocal behavior, these clusters did not map to subfields of prefrontal or premotor cortex, as has been observed in more conventional task-based paradigms. Our results suggest a distributed functional organization for the myriad neural mechanisms underlying natural social interactions and have implications for our concepts of the role that frontal cortex plays in governing ethological behaviors in primates. • Neurons in marmoset monkey PFC and PMC recorded during natural conversations • GLM and PSTH were applied to quantify neural activity in continuous behavior • Model-based approach robustly outperformed more traditional analyses • Neurons in behavior-related functional clusters were distributed throughout PFC/PMC Li and colleagues applied model-based and traditional analyses to characterize single-neuron responses in the frontal cortex while marmosets engaged in their natural conversational exchanges. Results showed that the population supported nearly all facets of this ethological behavior through an anatomically distributed—but functionally modular—pattern of neural activity. [ABSTRACT FROM AUTHOR]

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies. [ABSTRACT FROM AUTHOR]

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens—"thresholded regression"—exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights. [ABSTRACT FROM AUTHOR]

Predicting suspended sediment loads in areas without detailed measurements, or with only short-term records, is crucial for the sustainable management of water resources. This study aimed to establish the relationships between specific suspended sediment loads and the characteristics of 23 sub-basins within the Haraz-Neka River basin, in Iran, to create regional models to estimate the sediment loads. To achieve this, several analytical methods were used, including cluster analysis, principal component analysis, principal component and classification analysis, and general linear modelling. Among these, the principal component analysis regression model was the most effective for estimating suspended sediment loads in the clusters. The principal component and classification analysis revealed that the best predictor was the first principal component, which strongly correlated with the minimum and mean elevation of the sub-basins. The general linear model regression showed the best overall performance for estimating regional suspended sediment loads in the study area. [ABSTRACT FROM AUTHOR]

Ciyi Hua,1 Feifei Li,1 Youyang Shi,2 Yuan Xu,1 Mengdie Zhu,1 Yang Wang,1 Xiqiu Zhou,2 Sheng Liu1,3 1Institute of Chinese Traditional Surgery, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Department of Breast Surgery, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 3Graduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of ChinaCorrespondence: Sheng Liu, Institute of Chinese Traditional Surgery, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, People’s Republic of China, Tel +86 18917763005, Fax +86-64385700, Email shengliu@shutcm.edu.cnBackground: Patients with clinically cured granulomatous lobular mastitis (GLM) still face a high probability of recurrence and new occurrence.Purpose: To evaluate the long-term efficacy of traditional Chinese medicine (TCM) in treating GLM and to hypothesize potential risk factors for recurrence or new occurrence.Patients and Methods: A retrospective analysis was conducted on GLM patients treated with TCM at Longhua Hospital affiliated with Shanghai University of Traditional Chinese Medicine from January 2016 to July 2021. We analyzed general data, two-year recurrence and new occurrence rates, and 12 risk factors associated with recurrence or new occurrence.Results: This cross-sectional study included 261 GLM patients with an average age at onset of 31.95 years (primarily aged 31– 40). The two-year recurrence rate for GLM was 1.53%, and the new occurrence rate was 4.21%. Univariate and multivariate conditional logistic regression analyses revealed that a history of inverted nipples was associated with the risk of both recurrence and new occurrence of GLM (HR = 8.672, 5.375, P < 0.05), and menstrual irregularity was related to a higher risk of recurrence (HR = 13.172, P < 0.001).Conclusion: A history of inverted nipples is identified as a potential risk factor associated with the long-term recurrence and new occurrence of GLM, while menstrual irregularity is associated with recurrence. Despite this, patients with GLM undergoing TCM demonstrate low rates of long-term recurrence and new occurrence after achieving clinical cure, underscoring the effectiveness of TCM. This study lays the groundwork for a long-term effectiveness strategy to guide future GLM treatments.Keywords: granulomatous lobular mastitis, GLM, recurrence, new occurrence, risk factor

Marine protected areas contribute to mitigating the effects of human activities on marine ecosystems. The waters of Southampton Island, Nunavut, Canada are under consideration to become a marine protected area, but baseline information of marine mammal presence and habitat use is lacking. This study represents the first passive acoustic monitoring of marine mammals in this area, with data collected in 2018 and 2019. Bioacoustics analyses and generalized linear models were used to investigate species seasonal and temporal vocalization trends; infer behavior and habitat use from vocal cues; and explore vocal activity correlation with sea ice. Over a diel period, bearded seals (Erignathus barbatus (Erxleben, 1777)) and walruses (Odobenus rosmarus (Linnaeus, 1758)) were found to increase their vocal activity at night, whereas belugas (Delphinapterus leucas (Pallas, 1776)) mainly vocalized during daytime. Seasonally, bearded seals were recorded only during their breeding season at sea ice break-up, while walruses vocalized consistently throughout the open-water period outside their breeding season. Beluga vocal trends suggest their use of the area as a migratory corridor during sea ice break-up and freeze-up. Finally, bowhead whales (Balaena mysticetus Linnaeus, 1758) were recorded later than expected, concurrently with the onset of their mating vocal displays during the fall migration at sea ice freeze-up.

This study assesses the reliability and limitations of the Geostationary Lightning Mapper (GLM) in detecting continuing currents by comparing observations from ground‐based high‐speed cameras with GLM‐16 data. Our findings show that the GLM's one‐group detection efficiency (DE_1) is 53%, while the more stringent five‐consecutive‐group detection efficiency (DE_5) is 10%. Optical signals detected by the GLM predominantly occur during the early stages of continuing currents. Additionally, there is a notable disparity in detection efficiencies between positive and negative continuing currents, with positive continuing currents being detected more frequently. The application of the logistic regression model developed by Fairman and Bitzer (2022) further illustrates the limitations in continuing current identification. The study underscores the challenges of relying solely on satellite data to monitor and analyze continuing currents, emphasizing the need for advancements in detection technologies and methodologies to reliably detect continuing current at a large spatial scale. Plain Language Summary: This study investigates continuing current in cloud‐to‐ground lightning—a prolonged electrical discharge known for causing significant damage, such as igniting large‐scale wildfires. For the first time, we used recordings from ground‐based high‐speed cameras to assess how effectively the Geostationary Lightning Mapper (GLM), an optical instrument on the GOES satellite, detects these currents. Our findings reveal that the GLM often captures only the early stages of continuing currents and struggles to detect them consistently, particularly those in weaker, negative types of lightning. These results underscore the limitations of current satellite‐based optical instruments in fully monitoring continuing currents, emphasizing the need for improved methods to better detect and understand these electrical discharges and mitigate their impacts. Key Points: The Geostationary Lightning Mapper is limited in detecting continuing current, with a one‐group DE of 53% and a five‐group DE of just 10%Optical signals captured by the Geostationary Lightning Mapper are predominantly during the early stage of continuing currentsA significant disparity is observed in GLM detection efficiencies between positive and negative continuing currents [ABSTRACT FROM AUTHOR]

A strong and fast-moving electrical storm occurred in the Southwest Florida region overnight, from 01:00 UTC on 17 April to 07:00 UTC on 17 April 2023. Video recordings were conducted in the region at Latitude N 26.34° and Longitude W 81.79° for 5 h and 15 min, from 01:45 UTC to 07:00 UTC. The camera captured the flashes transforming from pinkish, violet, blue, and then emerald green in the sky twice: the first colored flash lasted 2.0 s, and the second one lasted 0.5 s. The characteristics of the flashes were analyzed using video images integrated with lightning flash data from the Geostationary Lightning Mapper (GLM). To gain deeper insights into the associated atmospheric conditions, the Advanced Baseline Imager (ABI) was also used to help understand the spectral anomalies. Both events had similarities: the same pattern of changing luminous colors in the optical images and the trajectory of the lightning discharges, showing clusters and horizontal distributions. Event 1 occurred mainly over the ocean and featured more intense storms, heavier rain, and denser, higher cloud-tops compared to Event 2, which occurred inland and involved dissipating storms. Moreover, the group energy detected in Event 1 was an order of magnitude higher than in Event 2. We attribute the wavelength of the recorded colored luminosity to varying atmospheric molecular concentrations, which ultimately contributed to the unique spectral line. In this study, we explore the correlation between colored flashes and specific atmospheric concentrations. [ABSTRACT FROM AUTHOR]

Background: Self‐regulation in early childhood develops within a social context. Variations in such development can be attributed to inter‐individual behavioral differences, which can be captured both as facets of temperament and across a normal:abnormal dimensional spectrum. With increasing emphasis on irritability as a robust early‐life transdiagnostic indicator of broad psychopathological risk, linkage to neural mechanisms is imperative. Currently, there is inconsistency in the identification of neural circuits that underlie irritability in children, especially in social contexts. This study aimed to address this gap by utilizing a functional magnetic resonance imaging (fMRI) paradigm to investigate pediatric anger/frustration using social stimuli. Methods: Seventy‐three children (M = 6 years, SD = 0.565) were recruited from a larger longitudinal study on irritability development. Caregivers completed questionnaires assessing irritable temperament and clinical symptoms of irritability. Children participated in a frustration task during fMRI scanning that was designed to induce frustration through loss of a desired prize to an animated character. Data were analyzed using both general linear modeling (GLM) and independent components analysis (ICA) and examined from the temperament and clinical perspectives. Results: ICA results uncovered an overarching network structure above and beyond what was revealed by traditional GLM analyses. Results showed that greater temperamental irritability was associated with significantly diminished spatial extent of activation and low‐frequency power in a network comprised of the posterior superior temporal sulcus (pSTS) and the precuneus (p <.05, FDR‐corrected). However, greater severity along the spectrum of clinical expression of irritability was associated with significantly increased extent and intensity of spatial activation as well as low‐ and high‐frequency neural signal power in the right caudate (p <.05, FDR‐corrected). Conclusions: Our findings point to specific neural circuitry underlying pediatric irritability in the context of frustration using social stimuli. Results suggest that a deliberate focus on the construction of network‐based neurodevelopmental profiles and social interaction along the normal:abnormal irritability spectrum is warranted to further identify comprehensive transdiagnostic substrates of the irritability. [ABSTRACT FROM AUTHOR]

The lunar phase is known to influence the spawning activity of some Lutjanus snappers, however, information on its influence on feeding activity is limited. In the present study, diet, feeding habit, and lunar periodicity of feeding activity on two reef‐associated sympatric snappers, blackspot snapper Lutjanus fulviflamma (n = 339) and blacktail snapper L. fulvus (n = 172), were investigated by stomach content analyses in the Yaeyama region (24°N, 124°E), southern Japan. Diets of the two snappers were similar and mainly consumed fishes, crabs, and shrimps. Relative volume of the stomach contents to body weight was compared by season, lunar day, and predator size using a generalized linear model. Larger individuals fed on larger fish prey and tended to show higher stomach fullness for both snappers. Periodic regression revealed that the lunar periodicity of the feeding activity is different between the two Lutjanus snappers. The feeding activity of L. fulviflamma increased around the spring tides of both new and full moons during a single lunar cycle. On the other hand, the feeding activity of L. fulvus increased only before the new moon but decreased before the full moon. Spawning of L. fulvus is known to occur only around the full moon, and enlarged gonads are considered to suppress the feeding activity of the species. Individuals with much stomach contents did not have large gonads for both snappers, and this supports the above supposition. Spawning and feeding activities are closely linked and simultaneous temporal analysis will enable a clearer understanding of the ecological role of these predators. [ABSTRACT FROM AUTHOR]

Motivation: High-throughput sequencing technology facilitates the quantitative analysis of microbial communities, improving the capacity to investigate the associations between the human microbiome and diseases. Our primary motivating application is to explore the association between gut microbes and obesity. The complex characteristics of microbiome data, including high dimensionality, zero inflation, and over-dispersion, pose new statistical challenges for downstream analysis. Results: We propose a GLM-based zero-inflated generalized Poisson factor analysis (GZIGPFA) model to analyze microbiome data with complex characteristics. The GZIGPFA model is based on a zero-inflated generalized Poisson (ZIGP) distribution for modeling microbiome count data. A link function between the generalized Poisson rate and the probability of excess zeros is established within the generalized linear model (GLM) framework. The latent parameters of the GZIGPFA model constitute a low-rank matrix comprising a low-dimensional score matrix and a loading matrix. An alternating maximum likelihood algorithm is employed to estimate the unknown parameters, and cross-validation is utilized to determine the rank of the model in this study. The proposed GZIGPFA model demonstrates superior performance and advantages through comprehensive simulation studies and real data applications. [ABSTRACT FROM AUTHOR]

Fire is one of the most severe hazards to the world's forests, causing massive biodiversity loss and alterations in various ecosystems. Weather and climatic conditions, terrain properties, fire-inducing agents, and anthropogenic variables can trigger forest fires. Multiple studies have shown that in India, anthropogenic activity is the most common cause of forest fires, with the spread and extent determined mainly by the severity of the weather. We used the generalized linear mixed model to identify the drivers behind forest fire occurrences within Rajaji and Corbett tiger reserves in the northern Indian Terai Arc landscape. These two tiger reserves are spanning in a large area of the lower Shivalik Himalayas and share similar bio-geography, climates, and topography. We used 22 variables as drivers of forest fires for our analysis and found significant differences in the drivers of forest fires in Rajaji and Corbett, Tiger Reserves. Both the reserves' topographic and climatic conditions contribute to fire incidents rather than anthropogenic factors. Though anthropogenic pressure is responsible mainly for forest fires triggering globally, other natural conditions can still ignite forest fires under favorable climatic conditions and the availability of dry leaf litter. Identifying the drivers of fires in the targeted habitat or landscape is essential for controlling and managing large-scale fire incidents. Our results will help the concerned authorities frame the fire management protocol for larger landscapes. [ABSTRACT FROM AUTHOR]

The parameters of generalized linear models (GLMs) are usually estimated by the maximum likelihood estimator (MLE) which is known to be asymptotically efficient. But the MLE is computed using a Newton-Raphson-type algorithm which is time-consuming for a large number of variables or modalities, or a large sample size. An alternative closed-form estimator is proposed in this paper in the case of categorical explanatory variables. Asymptotic properties of the alternative estimator is studied. The performances in terms of both computation time and asymptotic variance of the proposed estimator are compared with the MLE for a Gamma distributed GLM. [ABSTRACT FROM AUTHOR]

Forest biomass plays a crucial role in the global carbon cycle as a significant contributor derived from both soil and trees. This study focuses on investigating tree carbon stock (TCS) and estimating aboveground biomass (AGB) based on elevation within the Srivilliputhur Wildlife Sanctuary forest, while also exploring the various factors that influence their contribution. Utilizing a non-destructive approach for carbon estimation, we found that the total tree biomass in this region ranged from 220.9 Mg/ha (in Z6) to 720.6 Mg/ha (Z2), while tree carbon stock ranged from 103.8 to 338.7 Mg/ha. While Kruskal–Wallis tests did not reveal a significant relationship (p = 0.09) between TCS and elevation, linear regression showed a weak correlation (R2 = 0.002, p < 0.05) with elevation. To delve deeper into the factors influencing TCS and biomass distribution, we employed a random forest (RF) machine learning algorithm, demonstrating that stand structural attributes, such as basal area (BA), diameter at breast height (DBH), and density, held a more prominent role than climatic variables, including temperature, precipitation, and slope. Generalized linear models (GLM) were also utilized, confirming that BA, mean DBH, and elevation significantly influenced AGB (p ≤ 0.001), with species richness, precipitation, and temperature having lower significance (p ≤ 0.01) comparatively. Overall, the RF model exhibited superior performance (R2 = 0.92, RMSE = 0.12) in terms of root mean square error (RMSE) compared to GLM (R2 = 0.88, RMSE = 0.35). These findings shed light on the intricate dynamics of biomass distribution and the importance of both stand structural and climatic factors in shaping forest ecosystems. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Explosives & Propellants is the property of Chinese Journal of Explosives & Propellants Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Herein, we explored the overall association between metal mixtures and lung functions in populations of varying ages and the relationship among the associated components. The 2007–2012 National Health and Nutrition Examination Survey data of 4382 American participants was analyzed, and generalized linear, elastic net, quantile g–computation, and Bayesian kernel machine regression models were used to evaluate the relationship between exposure to the metal mixture and lung function at various ages. The results of barium exposure at distinct stages revealed that children and adolescents exhibited greater lung function changes than those in adults and the elderly. Additionally, compared with children and adolescents, cadmium– and arsenic–containing metabolites contributed to nonconductive lung function changes in adults and the elderly exposed to metal mixtures. The results showed that the effects of exposure to metal mixtures on lung function in children and adolescents were predominantly caused by lead and barium. Altogether, children and adolescents were found to be more susceptible to metal–exposure–mediated lung function changes than adults and the elderly. [ABSTRACT FROM AUTHOR]

Abstract This study assesses the reliability and limitations of the Geostationary Lightning Mapper (GLM) in detecting continuing currents by comparing observations from ground‐based high‐speed cameras with GLM‐16 data. Our findings show that the GLM's one‐group detection efficiency (DE_1) is 53%, while the more stringent five‐consecutive‐group detection efficiency (DE_5) is 10%. Optical signals detected by the GLM predominantly occur during the early stages of continuing currents. Additionally, there is a notable disparity in detection efficiencies between positive and negative continuing currents, with positive continuing currents being detected more frequently. The application of the logistic regression model developed by Fairman and Bitzer (2022) further illustrates the limitations in continuing current identification. The study underscores the challenges of relying solely on satellite data to monitor and analyze continuing currents, emphasizing the need for advancements in detection technologies and methodologies to reliably detect continuing current at a large spatial scale.

[Objective] This study is aimed at comprehensively evaluating the soil quality of different vegetation restoration types in the mountainous areas of Beijing, and further identifying the key factors affecting soil quality, so as to provide data support for vegetation restoration and reconstruction in the region. [Methods] The study utilized various vegetation types, including Platycladus orientalis pure forest, Pinus tabulaeformis pure forest, P. orientalis-P. tabulaeformis mixed forest, P. orientalis coniferous and broadleaved mixed forest, P. tabulaeformis coniferous and broadleaved mixed forest, deciduous broad-leaved mixed forest, and non-forest land (CK), with similar stand conditions, as research objects. Fourteen soil physical and chemical indicators were measured to establish the total data set (TDS) for evaluating soil quality. Principal component analysis (PCA) and Pearson correlation analysis were employed to determine the minimum data set (MDS) for soil quality evaluation. Two scoring methods, linear (L) and non-linear (NL), were used to calculate the soil quality index (SQI) and a general linear model (GLM) was employed to identify key factors influencing soil quality. [Results] The bulk density and sand content decreased, while the content of soil nutrients such as organic matter, total nitrogen, total potassium, available nitrogen, and available potassium increased after the vegetation restoration compared with the non-forest land. The screened MDS indicators for soil quality evaluation in the study area were total nitrogen (TN), sand content, total potassium (TK), pH, and available water capacity (AWC). Under the four methods (SQI-LT, SQI-NLT, SQI-LM, and SQI-NLM), the SQI values of different vegetation restoration types were ranked as deciduous broadleaf mixed forest > P. orientalis coniferous and broadleaved mixed forest > P. tabulaeformis pure forest > P. tabulaeformis coniferous and broadleaved mixed forest > P. orientalis-P. tabulaeformis mixed forest > P. orientalis pure forest > non-forest land, and the soil quality significantly improved after vegetation restoration. The soil quality evaluation method of SQI-NLM exhibited better applicability in the mountainous areas of Beijing. Compared with non-forested land, the SQI-NLM of other vegetation restoration types improved by 64%, 48%, 45%, 36%, 33% and 27%, respectively. The GLM model accounted for 85.24% of the total variation in the soil quality index, with vegetation type explaining the largest proportion of the soil quality index (45.09%). [Conclusion] The selection of suitable vegetation restoration types is crucial for improving regional soil quality. In future vegetation restoration efforts, priority should be given to broad-leaved species in tree species selection. Additionally, the choice of silvicultural configuration should depend on the tree species, such as introducing native broad-leaved species into Platycladus orientalis pure forest to form a Platycladus orientalis coniferous and broadleaved mixed forest, or selecting Pinus tabulaeformis pure forest as the optimal silvicultural model.

The wind power industry has experienced a significant increase and popularity in recent times, and the latest statistics indicate that this sector is still thriving. However, one of the essential steps in developing wind energy projects is finding suitable sites for wind farms, which involves understanding the nature of wind speed, wind direction, terrain, and environmental impacts. To predict the wind energy production over the expected lifespan of a wind farm, vertical wind speed extrapolation to the hub height of the wind turbine is necessary. Therefore, this study presents a comprehensive evaluation of seven statistical approaches for vertical wind speed extrapolation, including Generalized Linear Models (GLM), Linear Regression (LR), Support Vector Machines (SVM), Generalized Additive Models (GAM), Gaussian Process Regression (GPR), Regression Tree (RT), and Ensemble Regression (ER). The accuracy of these methods is assessed using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Normalized MSE (NMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and R-squared (R2). The study concludes that, on average, GLM performs the best out of all seven statistical methods.

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.

Grapes are among the most widely cultivated horticultural crops and have a long history of domestication. They exhibit genetic variation due to natural crossbreeding, bud mutations, and the changing demand for different types of wine and table grapes. Identifying and distinguishing autochthonous grape cultivars is an essential first step in breeding. In this research, an autochthonous grapevine (Vitis vinifera L.) population was identified using retrotransposon markers called iPBS (Inter primer binding sites), and genetic relationships with other cultivars from Türkiye and Europe were examined. The association between loci and specific traits was determined using GLM (general linear model) and MLM (mixed linear model) analyses. A total of 136 loci were generated by eight iPBS markers, of which 106 were polymorphic. Genotypes and standard cultivars were clustered into three main groups and seven subclusters by the neighbor-joining method. Structure analysis further classified the genotypes and cultivars into seven populations. Molecular variance analysis revealed that most of the variability occurred among individuals. In the association mapping, 36 loci were correlated with quantitative traits in GLM, while 21 were in MLM. The diversity assessments uncovered significant diversity within the autochthonous grape population, even among individuals with the same name. This diversity retains value for breeding research as it allows identifying distinct genotypes with desirable characteristics. The loci identified through both mapping approaches have the potential to serve as functional markers for selecting genotypes with desired traits. [ABSTRACT FROM AUTHOR]

Abstract.This article gives large sample theory for the one component partial least squares estimator, including some hypothesis tests for high dimensional data, under much weaker conditions than those in the literature. Simple theory is also given for some data splitting estimators and the marginal maximum likelihood estimators. It is shown that lasso, one component partial least squares, and ordinary least squares often estimate different population multiple linear regression models. The article also proves that there are often many valid population models for regression methods such as binary regression. [ABSTRACT FROM AUTHOR]

In the Mediterranean, one of the most prized small pelagic species is the European anchovy (Engraulis encrasicolus). This species has long been targeted by fishermen in the southern Alboran Sea and the Mediterranean Sea. The objective of this research was to identify any linkages that may exist between environmental changes in the southern Alboran Sea and the reported drop in anchovy landings between 1983 and 2020. The method of forward stepwise regression was used, taking into account environmental indicators, such as sea surface salinity (SSS), sea surface temperature (SST), surface chlorophyll a (Chl-a), U and V wind components, NAO index, and Atlantic jet velocity. To select the best prediction models, generalized linear models (GLMs) were created and organized based on their corrected akaike information criteria (AICc) values. Trended time data were used to create six top models, which explained 60 to 79% of the variation in anchovy landings. Temperature, salinity, and the U wind component all displayed a negative association with anchovy landings. On the other hand, the Atlantic jets' velocity as they traveled through the Strait of Gibraltar was positively correlated with anchovy landings. When the trend component was taken out; however, none of the environmental variables could explain the variations in anchovy landings. This may imply that while environmental influences have an effect over the long term, they have little effect on the inter-annual time scale. Hence, the decline in landings of this species may be due to factors other than environmental change. Overfishing may have played a substantial role in the long-term decline in landings since this species was the most targeted of the small pelagic species in the study area. [ABSTRACT FROM AUTHOR]

Copyright of Polish Statistician / Wiadomości Statystyczne is the property of State Treasury - Statistics Poland and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The satellite‐constellation passive‐microwave Brightness Temperature (TB) observations, with global coverage, and more additions from upcoming CubeSats, have been mainly used in surface precipitation retrievals. However, these observations can also be used to indicate the intensity of convective systems. This study attempts to relate Global Precipitation Mission (GPM) Microwave Imager (GMI) TBs to Geostationary Lightning Mapper (GLM) lightning flashes by using 4 years (02/2018–04/2022) of GPM Precipitation Feature (PF) database. GMI TBs are collocated to the GLM lightning counts, and to the ERA5 reanalysis 2‐m air temperature in PFs. Three Artificial Intelligent Neural Network Models (AI‐NN) are trained to classify PFs producing lightning in a 20‐min window respectively for land, ocean, or coast. The flash rate of the determined Lightning producing PFs (LPFs) is then quantified by three other AI‐NNs, each trained for one of the three regions. Though the models clearly capture the global geographical distribution of LPFs with a Probability Of Detection over 90%, high False Alarm Rates are found, ranging from 49.9% over land to 91.5% over the ocean. The importance of TB at each passive microwave channel varies regionally, corresponding to the different microphysical properties in various types of precipitation systems. The global lightning distribution is derived by applying the AI models to global PFs and is well compared to the lightning climatology from Lightning Imaging Sensor and Optical Transient Detector. This suggests that the use of passive microwave TBs can help to fill the gaps in lightning monitoring thanks to their global coverage. Plain Language Summary: We are taking advantage of the indirect relationship between brightness temperatures (TBs) (from GPM‐GMI core satellite) and lightning rate (from GOES‐GLM) (i.e., low TBs ∼ lightning vs. high TBs ∼ non‐lightning) to build a set of AI Shallow Neural Networks. The goal of the study is to find the extent to which TBs alone can determine the flash rate in a 20‐min window (10‐min before and after) surrounding the measurement of the TBs of a Precipitation Feature (PF). First, models are trained to classify Lightning PFs (LPFs) versus non‐Lightning PFs (nLPFs) and next, models are trained to quantify the flash count (regression). Models are trained in the GLM domain (American continents + surrounding oceans) and then tested globally. Results show that TBs allow a lightning determination and quantification to some extent, but produces high false alarm rates (i.e., ∼50% for land to ∼90% for ocean) due to limitations resulting from the indirect relationship between lightning and TBs (i.e., the limit between LPFs and nLPFs is not clear enough for the models to do better). Global application of the models show similar results to literature lightning climatologies, showing that AI can pick most areal information. This work fits the recent emergence of passive‐microwave only CubeSats. Key Points: Classification and regression AI shallow neural network models are built using GPM‐MI brightness temperature to predict lightningAI models can accurately determine extremes but brightness temperatures lack information to differentiate lightning in weaker systemsA global lightning climatology is derived from the models' results and shows similar results to satellite derived climatology [ABSTRACT FROM AUTHOR]

Simple Summary: Poor quality of biological information, such as the relationship between length and weight parameters, could be a source of variability with a significant impact on stock assessment results. This relationship between the length and weight of individuals could differ between males and females as well as two separated islands and/or linked to the reproduction period according the studied species. In total, 109 fish species (24,996 individuals) were sampled around Guadeloupe and Martinique Islands from October 2021 to September 2022 to estimate the morphometric relationships between total length and weight (Length Weight Relationship: LWR) of each fish species according to potential spatial, temporal and sex differences. Of these species, this is the first time that the LWR was estimated in the Atlantic Ocean for 16 species. There is a significant relationship between length and weight for all tested species. For 83 tested species, the sex effect on the LWR showed significant sexual dimorphism for 24 species. Additionally, a link between the temporal effect and the reproduction period was tested for 68 species, of which 35 presented significant differences relative to the annual quarter of sampling. Finally, the geographical effect (i.e., the difference between samples from around Guadeloupe Island and those from Martinique Island) was significant for 60 species. This island effect was significant for 25 species. [ABSTRACT FROM AUTHOR]

The possibilities of creating artificial absence points (pseudo-absences) to build species distribution models are considered. An approach proposed by R. Engler and adapted by T. Hengl is described, which takes into account habitat suitability indices and distances to presence points to create pseudo-absences. Using the example of the common bilberry (Vaccinium myrtillus) in the Central Forest Nature Reserve and its buffer zone, generalized linear models based on the Engler–Hengl approach, traveled tracks, and distances to presence points are compared, and the model built using the maximum entropy method. The results obtained indicate the superiority of the model based on the Engler–Hengl approach both in terms of quality assessments and in terms of the realism of the constructed spatial distribution maps. [ABSTRACT FROM AUTHOR]

We present a potential growth thermal index (PGTI) and assess its correlation with juvenile Atlantic salmon Salmo salar fork length data collected near the end of the growth season in a range of latitudinal locations and geographic scales (watershed, regional, continental) across the American north‐east. The PGTI is based on two components: a water temperature‐dependent growth curve and a water temperature time series continuously describing the thermal environment preceding fish sampling. Testing various shapes and characteristics of the temperature–growth curve against fish length data revealed strong positive correlations for all combinations. PGTI warming, calculated only from the beginning of the growth season until maximum summer temperature is reached, consistently performed well in explaining fish size‐at‐age across the latitudinal gradient and the three geographic scales that were considered. Varying thermal contrasts created by repeat subsampling of the dataset showed that fish length is better explained by the level of thermal contrast within the dataset than the geographical scale of analysis. A simple generalized linear model using a log link function with PGTI warming, fish density and water discharge as predictors explained 87% of the variance of size‐at‐age of 0+ and 1+ juvenile Atlantic salmon. [ABSTRACT FROM AUTHOR]

Habitat suitability refers to the mechanism that enables organisms to choose suitable habitats to survive. In the present study, we studied the habitat suitability of young golden mahseer (fingerlings and juveniles) in the streams of the Ramganga River, one of the prominent rivers of the western Himalaya. Different habitat types and microhabitat features were documented. Habitat suitability was evaluated by generating habitat suitability criteria curves in response to varying habitat availability and use by young golden mahseer during different seasons. Generalised linear modelling (GLM) was used to analyse environmental characteristics responsible for selecting golden mahseer habitats. We studied mahseer based on body size into two main classes, that is, fingerlings (1.5–10 cm) and juveniles (10–30 cm). Golden mahseer fingerlings preferred mean depth (0.1–0.6 m) and mean stream velocity (0–0.2 m/s) at stream reach dominated by diverse substrate compositions such as sand, gravel, and small cobbles. Similarly, juveniles preferred mean depth (0.1–1.5 m) and mean water velocity (0.1–0.5 m/s), with cobbles, bedrock, and sand dominating their habitats. The GLM results indicated that dissolved oxygen, temperature, and water velocity were the significant factors influencing habitat suitability. High dissolved oxygen positively influenced fingerlings, whereas moderate to high water velocity affected juveniles the most. Therefore, to understand the flow requirement for threatened species like golden mahseer, it is essential to characterise critical habitats and develop criteria based on habitat suitability curves. [ABSTRACT FROM AUTHOR]

This paper introduces the Bradley–Terry regression trunk model, a novel probabilistic approach for the analysis of preference data expressed through paired comparison rankings. In some cases, it may be reasonable to assume that the preferences expressed by individuals depend on their characteristics. Within the framework of tree-based partitioning, we specify a tree-based model estimating the joint effects of subject-specific covariates over and above their main effects. We, therefore, combine a tree-based model and the log-linear Bradley-Terry model using the outcome of the comparisons as response variable. The proposed model provides a solution to discover interaction effects when no a-priori hypotheses are available. It produces a small tree, called trunk, that represents a fair compromise between a simple interpretation of the interaction effects and an easy to read partition of judges based on their characteristics and the preferences they have expressed. We present an application on a real dataset following two different approaches, and a simulation study to test the model's performance. Simulations showed that the quality of the model performance increases when the number of rankings and objects increases. In addition, the performance is considerably amplified when the judges' characteristics have a high impact on their choices. [ABSTRACT FROM AUTHOR]

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A strong and fast-moving electrical storm occurred in the Southwest Florida region overnight, from 01:00 UTC on 17 April to 07:00 UTC on 17 April 2023. Video recordings were conducted in the region at Latitude N 26.34° and Longitude W 81.79° for 5 h and 15 min, from 01:45 UTC to 07:00 UTC. The camera captured the flashes transforming from pinkish, violet, blue, and then emerald green in the sky twice: the first colored flash lasted 2.0 s, and the second one lasted 0.5 s. The characteristics of the flashes were analyzed using video images integrated with lightning flash data from the Geostationary Lightning Mapper (GLM). To gain deeper insights into the associated atmospheric conditions, the Advanced Baseline Imager (ABI) was also used to help understand the spectral anomalies. Both events had similarities: the same pattern of changing luminous colors in the optical images and the trajectory of the lightning discharges, showing clusters and horizontal distributions. Event 1 occurred mainly over the ocean and featured more intense storms, heavier rain, and denser, higher cloud-tops compared to Event 2, which occurred inland and involved dissipating storms. Moreover, the group energy detected in Event 1 was an order of magnitude higher than in Event 2. We attribute the wavelength of the recorded colored luminosity to varying atmospheric molecular concentrations, which ultimately contributed to the unique spectral line. In this study, we explore the correlation between colored flashes and specific atmospheric concentrations.

The Western Visayas is the most deforested region of the Philippines. The Northwest Panay Peninsula Natural Park, an Important Bird and Biodiversity Area on the island of Panay, is the largest remaining tract of lowland forest in the region supporting a number of bird species of conservation importance. The aim of this study was to quantify the diversity and community composition of bird assemblages across a gradient of habitat disturbance, identifying key species and guilds contributing to habitat-specific community differences and informing conservation management of the park. In 2020, we conducted bird surveys using distance sampling point transects with a 10-minute count period in 4 habitat types: primary forest, secondary forest, plantations, and open areas. We produced measures of taxonomic, species, and functional diversity based on ecological guilds described by diet, body size and feeding stratum, and endemicity, and used generalized linear modeling to investigate the effect of habitat on bird assemblages. We used analysis of similarities to test for differences in community composition across habitats and similarity of percentages to determine what species and guilds contributed to community dissimilarities. We recorded 61 species, including 6 globally threatened species, with the highest and lowest taxonomic and species richness and endemicity in primary forest and open areas, respectively. Primary forest had the largest, positive effect on bird diversity and endemicity, while open habitat had a consistent negative effect. Plantations had a small positive effect on functional diversity only. Community dissimilarities across habitats were largely driven by medium general-level and upper-storey frugivores, small lower-storey insectivores, and small lower-storey and general-level omnivores. Secondary forest and plantations may play an important role in supporting at least some of the primary forest bird community. Our findings emphasize the importance of the site’s primary forest for regional bird conservation, and we provide recommendations for management of this important lowland forest landscape.

Statistical estimates for the exploitable stock of giant grenadier are determined, by fishing zones in the Russian Far East, and interannual dynamics of the species biomass is considered using historical data on catches and abundance. The assessments relationship with reference points of the biomass dynamic models is examined for the first time. With using the Bayesian approach, a priori assumptions about virgin biomass, which were made earlier on results of scientific surveys extrapolated to unsurveyed areas, are accounted in JABBA model; some errors in the catch and abundance indices are revealed. The parameters of instantaneous rate of recruitment and initial depletion of population are determined. Overfishing is not detected yet in any fishing zone, but a negative trend of the biomass dynamics is noticed for the Western Bering Sea fishing zone since 2015 (if the stock estimations based on surveys are realistic). The decline is reasoned by implementation of too high target for exploitation rate of vulnerable biomass (φF = 0.10) that is higher than such target for other fishing zones (< 0.08 or even 0.01 for the stocks exploited with the total allowable catch limits). That’s why annual catches exceeded the surplus production in the Western Bering Sea fishing zone in 2012 and since 2015 to nowadays. The sum of median estimations for the maximum sustainable yields in all fishing zones is about 47.4 . 103 t that exceeds the values of total annual catch. The instantaneous rate of recruitment is almost equal in the neighboring fishing zones in the western Bering Sea and at East Kamchatka (0.086–0.085 by median, with 95 % credible intervals from 0.05 to 0.14) and is much higher at Kuril Islands with the maximum in the North-Kuril fishing zone. The latter case is possibly conditioned by partially external recruitment. Therefore, population structure of giant grenadier should be clarified for correct modeling of the recruitment; until then, the stocks assessments by fishing zones are available.

Yifei Zeng,1,2 Dongxiao Zhang,1 Wenjie Zhao,1 Na Fu,1 Qiao Huang,1 Shuqi Li,2 Chang Gao,2 Jiale Yu2 1Department of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China; 2Graduate School, Beijing University of Chinese Medicine, Beijing, People’s Republic of ChinaCorrespondence: Dongxiao Zhang, Department of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, No. 23, Art Museum Back Street, Dongcheng District, Beijing, People’s Republic of China, Tel +86 13811077684, Email morningdong@163.comBackground: Despite the rising incidence rate of granulomatous lobular mastitis (GLM), uncertainties persist about its etiologic and predisposing factors to guide clinical treatment and early prevention. The objective of this study is to explore the predisposing factors for GLM.Patients and methods: This case-control study was conducted from 2018 to 2021 at Beijing Hospital of Traditional Chinese Medicine, Capital Medical University. Patients with GLM (cases) were matched with healthy examinees (controls) in a 1:1 ratio according to gender and living area. We analyzed their demographic features and investigated 75 factors that may be relevant to GLM using a standard questionnaire. Univariate and multivariable binary conditional logistic regression analyses were used to compare the differences between the two groups and evaluate the predisposing factors that may induce GLM.Results: There were 594 female GLM patients and 594 matched controls included in the study. The average age of the cases was 32.78 years (mainly 20 to 40). The incidence was high within five years after childbirth, and lesions were mainly in the unilateral breast. Univariate and multivariable conditional logistic regression analyses obtained six relevant factors and six high-risk factors. The six relevant factors included age, marriage, emotional abnormality, high prolactin, psychiatric drug intake, and sex hormone intake. Additionally, the independent high-risk factors for GLM included gestation, nipple invagination, blunt trauma, non-iatrogenic massage, lactation disorder, and nipple discharge (odds ratio (OR)=17.378, 8.518, 4.887, 3.116, 2.522, 1.685, P< 0.05). Menopause was an independent protective factor (OR=0.249, P< 0.05).Conclusion: The factors that increase milk and secretion production in the mammary duct are the main risk factors of GLM, especially when the nipples are invaginated. These factors can obstruct the duct and induce inflammation. Additionally, hormonal disorders, extrinsic trauma, and emotional abnormalities can accelerate the occurrence of GLM.Keywords: granulomatous lobular mastitis, GLM, predisposing factor, risk factor, case-control study, etiology

Antecedentes: Allium glandulosum Link & Otto se distribuye ampliamente desde el sur de Estados Unidos, México y Centroamérica. En México se distribuye en 27 estados, en ambientes contrastantes. Sin embargo, no se ha estudiado si su patrón de distribución se refleja en una amplia variación morfológica. Pregunta y/o Hipótesis: ¿La variación morfológica vegetativa y floral en Allium glandulosum a lo largo de su distribución en México permite diferenciar entre poblaciones de esta especie? Especie de estudio: Allium glandulosum Link & Otto. Sitio y fechas de estudio: Poblaciones de Allium glandulosum con distribución en el Eje Volcánico Transmexicano y la Altiplanicie Mexicana, durante el 2020. Métodos: Se determinaron y midieron caracteres morfológicos cualitativos y cuantitativos de ejemplares herborizados y de especímenes recolectados en campo. Se emplearon métodos estadísticos univariados y multivariados para describir y sintetizar la variabilidad de las recolectas de campo y materiales herborizados. Resultados: El análisis de componentes principales mostró que 14 caracteres representan el 51.29 % de la variación total acumulada en los primeros tres componentes. El análisis discriminante lineal resulto en la formación de cuatro grupos y el porcentaje de clasificación de los individuos fue de 71.53 %. El análisis de conglomerados mostró cuatro agrupaciones, diferenciados por caracteres cuantitativos y cualitativos. Conclusiones: La variación morfológica vegetativa y floral de Allium glandulosum permite diferenciar entre las poblaciones de la especie a lo largo de su distribución en México.

Adipose tissue is the major energy depot of the body and is considered an endocrine organ. Adipose tissue involves many different cell types, first and foremost, the adipocytes. White adipose cells that store fat and brown adipocytes that take part in lipid oxidation and heat generation are the most common cell types in adipose tissue. Even though brown adipocytes which have a high number of mitochondria and high fat-burning capacity are rare in adults, they are abundant in newborns and rodents. White adipocytes can gain a temporal brown-like character with a process called browning, which can be induced with cold exposure providing white adipocytes with increased fat-burning capacity. Adipose tissue is the main tissue associated with obesity; therefore, the browning process has the potential to be used in the treatment of obesity. Here, we made use of machine learning techniques to better understand the browning mechanisms. We applied a computational approach based on generalized linear models (GLM) and decision trees for the identification and classification of alternative splicing events, followed by downstream bioinformatics analysis for the detection of differential regulatory events in the transcriptome of the adipocyte browning. Our analyses identified possible extracellular alterations in response to changes in cellular shape via alternative splicing events and remarkably an intron retention event on the Upstream stimulatory factor2 (Usf2) gene that may alter the activity of the regulator and take part in the regulation of the browning process. Targeted therapies for induction of the browning process via regulation of Usf2 may prevent and treat obesity which is a widespread health condition. To the best of our knowledge, this is the first study that combines alternative splicing events with regulatory network inference to reveal the mechanism of the browning process. Our methodology has the potential to reveal many other disease-related mechanisms and lead to novel therapy strategies. [ABSTRACT FROM AUTHOR]

This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be "thinner" than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving. [ABSTRACT FROM AUTHOR]

A precise framework of species occurrence and distribution trends is crucial for conservation measures. Traditional survey methods are often labour‐intensive and time‐consuming, and they can be ineffective in detecting the presence of rare and elusive taxa, especially in harsh environments.The effectiveness and feasibility of an environmental DNA (eDNA)‐based approach was tested to detect the occurrence of the semiaquatic Eurasian otter (Lutra lutra) in six rivers in the Trans‐Himalaya, comparing the results with those obtained from a traditional field survey.Water samples were collected and filtered on‐site at 15 locations, from 2,660 to 3,819 m a.s.l. Otter scats were actively searched for at the same locations in a 900 m buffer along the river bank. Fifteen environmental parameters were recorded at each sampling site. After eDNA extraction and target quantitative realt‐time polymerase chain reaction (qPCR) assay, statistical analyses were run to explore the relationship between environmental factors and the presence/absence of otters at each site.Otter DNA was found at 73% of sites, whereas traditional field survey results showed that 53% of sites were positive for otters. Results from principal component analysis showed that the sites avoided by otters, as measured through eDNA, were clearly segregated along PC1 and PC2, with both axes explaining 57% of the cumulative variance. The best‐performing generalized linear model suggested that the occurrence of otter eDNA was influenced by channel width, surface velocity, nitrate level, total dissolved solids, and average water depth.The results of this study highlight that, compared with traditional field surveys, eDNA‐based methods increased the detection of positive sites by 20%, thus demonstrating their reliability for monitoring the presence of otters in the study area and providing new insights into the ecology of this species in the Indian Trans‐Himalaya. [ABSTRACT FROM AUTHOR]

We investigated the influences of oceanographic variables on recruitment patterns of the acorn barnacle Chthamalus malayensis and the rock oyster Saccostrea cuccullata, key space occupiers on tropical intertidal rocky shores. Recruitment data and nearshore environmental variables were obtained at spatial (regional and local) and temporal (monthly) scales on the west coast of Thailand. The relationship between the recruitment of each species and combinations of environmental variables was modeled. The climate of the study sites is influenced by the southwest and northeast monsoons of the Indochinese monsoon system. Observations showed that recruitment of both species and oceanographic variables exhibited differences at either local scale or monthly scale or both. Recruitment of both species was positively related to temperature, while barnacle recruitment was negatively related to salinity, but oyster recruitment was positively related to salinity. Barnacles showed a greater rate of recruitment when south-westerly onshore winds predominated during the southwest monsoon season, while the influence of wind patterns on rock oyster recruitment was not clear. Differential larval delivery regulated by monsoon-driven currents is, therefore, a potential determinant of barnacle recruitment. Our study highlights the roles of monsoon-influenced oceanographic processes as predictors of recruitment patterns of intertidal species in a tropical system. [ABSTRACT FROM AUTHOR]

As two metrics that depict convection features, lightning and hail are generated through different physical mechanisms but are related. The Geostationary Lightning Mapper (GLM) provides a unique approach to monitoring and studying the continual evolution of lightning in hailstorms. In this study, 3 years of GLM observations were used to analyze the lightning evolution in hailstorms over the contiguous United States. The lightning rate had two peaks at −2 min (t1) (relative to the hail occurrence time) and 59 min (t3), with a valley at 23 min (t2). The lightning rates of large hail were lower at t1 and higher at t2 than those of small hail. The maximum lightning rate grid generally moved from northwest to southeast but suddenly moved westward around 23 min. Observations of WWLLN confirmed these results but also highlighted differences, such as the high ratio between the WWLLN and GLM lightning rates around t1 and t3. The events per flash and flash duration/area/energy showed opposite variations with lightning rate, and their low values were accompanied by high lightning rate and low detection efficiency of GLM relative to WWLLN. However, the event energy continued to decrease with two local maxima at approximately t1 and t3. Furthermore, if the regional event rate was used in the lightning jump (LJ) algorithm, LJs preceded 71% of the hail reports, and the mean lead time reached 29 min. These results provide new insights into lightning features in hailstorms and verify the performance of the GLM in studying and monitoring hailstorms. Plain Language Summary: Hail and lightning are two kinds of common phenomena that occur in convection cells and are both used as metrics to measure convection intensity. However, they are generated via different microphysical processes and the literature has shown their different spatial distribution patterns in many regimes, including the CONUS. Here, we used the observations from the Geostationary Lightning Mapper (GLM) to investigate how lightning evolves during hailstorms. The temporal evolution of lightning rate shows a two‐peak and one‐valley feature. Lightning rate began to drop 2 min before hail occurrence time and reached a local minimum of around 23 min. Spatially, the location with the highest lightning rate moved from northwest to southeast, but suddenly moved 0.2° to west around 23 min. Moreover, the lightning rates of large hail were lower at the first peak and higher at the second peak than those of small hail. Other lightning features, such as flash energy, flash area, and flash duration, were highly related to event counts per flash and showed opposite variations with the lightning rate, resulting in two‐valley and one‐peak features over time. These results would promote our understanding in lightning and hail processes and aid in monitoring severe convection with the GLM. Key Points: Two peaks and a valley are revealed in the temporal evolution and sudden backward movement of the maximum lightning gridDuring the compact lightning period, the events per flash and flash duration/area/energy values are lowBased on the event rate, the lightning jump percentages and mean lead time reach 71% and 29 min, respectively [ABSTRACT FROM AUTHOR]