Exposure dynamics of Ross River virus in horses - Horses as potential sentinels (a One Health approach).

Ross River virus (RRV), the most medically and economically important arbovirus in Australia, has been the most prevalent arbovirus infections in humans for many years. Infected humans and horses often suffer similar clinical symptoms. We conducted a prospective longitudinal study over a 3.5-year period to investigate the exposure dynamics of RRV in three foal cohorts (n = 32) born in a subtropical region of South East Queensland, Australia, between 2020 and 2022. RRV-specific seroconversion was detected in 56% (n = 18) of foals with a median time to seroconversion, after waning of maternal antibodies, of 429 days (95% CI: 294-582). The median age at seroconversion was 69 weeks (95% CI: 53-57). Seroconversion events were only detected between December and March (Southern Hemisphere summer) over the entire study period. Cox proportion hazards regression analyses revealed that seroconversions were significantly (p < 0.05) associated with air temperature in the month of seroconversion. Time-lags in meteorological variables were not significantly (p > 0.05) associated with seroconversion, except for relative humidity (p = 0.036 at 2-month time-lag). This is in contrast to research results of RRV infection in humans, which peaked between March and May (Autumn) and with a 0-3 month time-lag for various meteorological risk factors. Therefore, horses may be suitable sentinels for monitoring active arbovirus circulation and could be used for early arbovirus outbreak detection in human populations.

Systemic lupus erythematosus with high disease activity identification based on machine learning.

OBJECTIVE: Clinical evaluation of systemic lupus erythematosus (SLE) disease activity is limited and inconsistent, and high disease activity significantly, seriously impacts on SLE patients. This study aims to generate a machine learning model to identify SLE patients with high disease activity. METHOD: A total of 1014 SLE patients with low disease activity and 453 SLE patients with high disease activity were included. A total of 94 clinical, laboratory data and 17 meteorological indicators were collected. After data preprocessing, we use mutual information and multisurf to evaluate and select the importance of features. The selected features are used for machine learning modeling. Performance of the model is evaluated and verified by a series of binary classification indicators. RESULTS: We screened out hematuria, proteinuria, pyuria, low complement, precipitation, sunlight and other features for model construction by integrated feature selection. After hyperparameter optimization, the LGB has the best performance (ROC: AUC = 0.930; PRC: AUC = 0.911, APS = 0.913; balance accuracy: 0.856), and the worst is the naive bayes (ROC: AUC = 0.849; PRC: AUC = 0.719, APS = 0.714; balance accuracy: 0.705). Finally, the selection of features has good consistency in the composite feature importance bar plot. CONCLUSION: We identify SLE patients with high disease activity by a simple machine learning pipeline, especially the LGB model based on the characteristics of proteinuria, hematuria, pyuria and other feathers screened out by collective feature selection.

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis.

OBJECTIVE: Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN. METHODS: A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis. RESULTS: Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN. CONCLUSION: We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection.

Indoor Airborne Microbiome and Endotoxin: Meteorological Events and Occupant Characteristics Are Important Determinants.

Airborne bacteria and endotoxin may affect asthma and allergies. However, there is limited understanding of the environmental determinants that influence them. This study investigated the airborne microbiomes in the homes of 1038 participants from five cities in Northern Europe: Aarhus, Bergen, Reykjavik, Tartu, and Uppsala. Airborne dust particles were sampled with electrostatic dust fall collectors (EDCs) from the participants' bedrooms. The dust washed from the EDCs' clothes was used to extract DNA and endotoxin. The DNA extracts were used for quantitative polymerase chain (qPCR) measurement and 16S rRNA gene sequencing, while endotoxin was measured using the kinetic chromogenic limulus amoebocyte lysate (LAL) assay. The results showed that households in Tartu and Aarhus had a higher bacterial load and diversity than those in Bergen and Reykjavik, possibly due to elevated concentrations of outdoor bacterial taxa associated with low precipitation and high wind speeds. Bergen-Tartu had the highest difference (ANOSIM R = 0.203) in ß diversity. Multivariate regression models showed that α diversity indices and bacterial and endotoxin loads were positively associated with the occupants' age, number of occupants, cleaning frequency, presence of dogs, and age of the house. Further studies are needed to understand how meteorological factors influence the indoor bacterial community in light of climate change.

Timing of fungal spore release dictates survival during atmospheric transport.

Fungi disperse spores to move across landscapes and spore liberation takes different patterns. Many species release spores intermittently; others release spores at specific times of day. Despite intriguing evidence of periodicity, why (and if) the timing of spore release would matter to a fungus remains an open question. Here we use state-of-the-art numerical simulations of atmospheric transport and meteorological data to follow the trajectory of many spores in the atmosphere at different times of day, seasons, and locations across North America. While individual spores follow unpredictable trajectories due to turbulence, in the aggregate patterns emerge: Statistically, spores released during the day fly for several days, whereas spores released at night return to ground within a few hours. Differences are caused by intense turbulence during the day and weak turbulence at night. The pattern is widespread but its reliability varies; for example, day/night patterns are stronger in southern regions. Results provide testable hypotheses explaining both intermittent and regular patterns of spore release as strategies to maximize spore survival in the air. Species with short-lived spores reproducing where there is strong turbulence during the day, for example in Mexico, maximize survival by releasing spores at night. Where cycles are weak, for example in Canada during fall, there is no benefit to releasing spores at the same time every day. Our data challenge the perception of fungal dispersal as risky, wasteful, and beyond control of individuals; our data suggest the timing of spore liberation may be finely tuned to maximize fitness during atmospheric transport.

Common snowdrop as a climate change bioindicator in Czechia.

The phenological response to climate change differs among species. We examined the beginning of flowering of the common snowdrop (Galanthus nivalis) in connection with meteorological variables in Czechia in the period 1923-2021. The long-term series were analyzed from phenological and meteorological stations of the Czech Hydrometeorological Institute (CHMI). Temporal and spatial evaluation (using Geographic Information System) in timing of beginning of flowering (BBCH 61) of G. nivalis was investigated under urban and rural settings. Furthermore, the detailed analysis of selected meteorological variables to onset of G. nivalis flowering was performed. Moreover, the trends (using Mann-Kendall test) and Pearson's correlation coefficients between phenological phase and meteorological variable were calculated. The main finding of this study was that the trend of the beginning of flowering of the common snowdrop during the studied period (1923-2021) is negative, and it varies in urban and rural environments. The results showed most significant acceleration of the beginning of flowering of G. nivalis by - 0.20 day year-1 in urban area and by - 0.11 day year-1 in rural area. Above that, a major turning point occurred between 1987 and 1988 (both, in phenological observations and meteorological variables), and the variability of the beginning of flowering is significantly higher in the second period 1988-2021. On top of, the study proved that the beginning of flowering of G. nivalis closely correlated with number of days with snow cover above 1 cm (December-March) at both types of stations (urban and rural), and with mean air temperature in February, maximum air temperature in January, and minimum air temperature in March. The Mann-Kendall test showed a reduction in the number of days with snow cover above 1 cm (December-March) during 99 years period at Klatovy station (a long-term time series) by - 0.06 day year-1, i.e., by - 5.94 days per the whole period. Conversely, air temperatures increase (maximum and minimum air temperature by 0.03 °C year-1 (2.97 °C per the whole period) and average air temperature by 0.02 °C year-1 (1.98 °C per the whole period)). Thus, our results indicate significant changes in the beginning of flowering of G. nivalis in Czechia as a consequence of climate change.

Estimation of Reference Evapotranspiration in a Semi-Arid Region of Mexico.

Reference evapotranspiration (ET0) is the first step in calculating crop irrigation demand, and numerous methods have been proposed to estimate this parameter. FAO-56 Penman-Monteith (PM) is the only standard method for defining and calculating ET0. However, it requires radiation, air temperature, atmospheric humidity, and wind speed data, limiting its application in regions where these data are unavailable; therefore, new alternatives are required. This study compared the accuracy of ET0 calculated with the Blaney-Criddle (BC) and Hargreaves-Samani (HS) methods versus PM using information from an automated weather station (AWS) and the NASA-POWER platform (NP) for different periods. The information collected corresponds to Module XII of the Lagunera Region Irrigation District 017, a semi-arid region in the North of Mexico. The HS method underestimated the reference evapotranspiration (ET0) by 5.5% compared to the PM method considering the total ET0 of the study period (26 February to 9 August 2021) and yielded the best fit in the different evaluation periods (daily, 5-day mean, and 5-day cumulative); the latter showed the best values of inferential parameters. The information about maximum and minimum temperatures from the NP platform was suitable for estimating ET0 using the HS equation. This data source is a suitable alternative, particularly in semi-arid regions with limited climatological data from weather stations.

Quantitative assessment of the variations in monthly precipitation trends induced by the impact of three gorges dam.

There are many studies that have examined the impact of the Three Gorges Dam (TGD) on changes in meteorological data, and most of them concluded that the TGD significantly reduced precipitation without taking into account the negative trends that had already existed before the impoundment. In this study, the investigation focused on the monthly precipitation data, and the Mann-Kendall (MK) trend analysis was conducted to show that the TGD had little effect on the trends of the precipitation data. Monthly data (1980-2018) from 19 stations upstream and downstream of the TGD and 5 stations located far from the main river were extracted. The analysis and results showed that although the linear long-term (1980-2018) precipitation trend upstream of the TGD was downward, the MK trend analysis showed that the precipitation trends became upward after impoundment. This situation existed even for station data located outside the region. Also, the analysis of monthly trends in different seasons showed that in spring and winter, there was only a very weak downward trend in monthly precipitation, while in summer and autumn, the trends were upward with steeper slopes. Following the upward trends of the monthly precipitation, the TGD generally positively intensified the monthly precipitation trends upstream and downstream of the dam, with the exception of a few months when total precipitation amounts were consistently low. In contrast to the trend analysis, which showed small and insignificant variations in precipitation data, the 12-month SPEI analysis showed a significant deterioration (about 20%) in the wetness index after impoundment both upstream and downstream of the TGD, while this situation did not occur outside the region. Thus, the TGD has extended dry periods both upstream and downstream of the dam over the past two decades.

Thermal Analysis and Prediction Methods for Temperature Distribution of Slab Track Using Meteorological Data.

The structural temperature distribution, especially temperature difference caused by solar radiation, has a great impact on the deformation and curvature of the concrete slab tracks of high-speed railways. Previous studies mainly focused on the temperature prediction of slab tracks, while how the temperature distribution is affected by environmental conditions has been rarely investigated. Based on the integral transformation method, this work presents an analytical method to determine and decompose the temperature distribution of the concrete slab track. A field temperature test of a half-scaled specimen of concrete slab track was conducted to validate the developed methodology. In the proposed method, we decompose the temperature distribution of the slab track into an initial temperature component and a boundary temperature component. Then, the boundary temperature components caused by solar radiation and atmospheric temperature are investigated, respectively. The results show that the solar radiation plays a significant role in the nonlinear temperature distribution, while the atmospheric temperature has little effect. By contrast, the temperature change in the slab surface resulting from the atmospheric temperature accounts on average for only 5% in the hot weather condition. The proposed method establishes a relation between the structural temperature and meteorological parameters (i.e., the solar radiation and atmospheric temperature). Consequently, the temperature distribution of the concrete slab track is predicted via the meteorological parameters.

Modeling of atmospheric particulate matters via artificial intelligence methods.

Nowadays, pollutants continue to be released into the atmosphere in increasing amounts with each passing day. Some of them may turn into more harmful forms by accumulating in different layers of the atmosphere at different times and can be transported to other regions with atmospheric events. Particulate matter (PM) is one of the most important air pollutants in the atmosphere, and it can be released into the atmosphere by natural and anthropogenic processes or can be formed in the atmosphere as a result of chemical reactions. In this study, it was aimed to predict PM10 and PM2.5 components measured in an industrial zone selected by adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), classification and regression trees (CART), random forest (RF), k-nearest neighbor (KNN), and extreme learning machine (ELM) methods. To this end, in the first stage of the study, the dataset consisting of air pollutants and meteorological data was created, the temporal and qualitative evaluation of these data was performed, and the PM (PM10 and PM2.5) components were modeled using the "R" software environment by artificial intelligence methods. The ANFIS model was more successful in predicting the PM10 (R2 = 0.95, RMSE = 5.87, MAE = 4.75) and PM2.5 (R2 = 0.97, RMSE = 3.05, MAE = 2.18) values in comparison with other methods. As a result of the study, it was clearly observed that the ANFIS model could be used in the prediction of air pollutants.

Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil.

In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.

AgriLogger: A New Wireless Sensor for Monitoring Agrometeorological Data in Areas Lacking Communication Networks.

The use of wireless technologies in the field of agriculture, or so-called smart or precision agriculture, is considered as one of the main efforts applied nowadays to multiply the food production on earth. However, wireless sensor network (WSN) technology is still at its early development stage and its application in agriculture and food industry is still rare due to the lack of farmers' awareness and outreach about the matter. This paper presents a new agro-sensor named AgriLogger with an aim to collect, store for long periods and transmit agrometeorological data represented by temperature and relative humidity in remote areas hard to reach and not served by telecommunication networks. The sensor exhibits long battery life, in the order of 10 years, thanks to low consumption technologies and to hardware sleep/wake up approach. It can be remotely placed on preselected sites through a customized drone. This latter, equipped with a dedicated payload, can then return on the sites where sensors have been placed, and, while hovering, wakes up the single devices and uploads their collected data through local wireless network. Field tests have demonstrated that the sensor, after being placed manually in two different positions, inside and outside a vineyard canopy, is able to collect and store successfully agrometeorological data like temperature and relative humidity. Moreover, the use of a drone potentially allows the collection of data from remote areas and, therefore, is able to provide a periodical monitoring of agro-ecological conditions.

Following in the footsteps of Hippocrates-interrelation between the incidence of odontogenic abscess and meteorological parameters.

OBJECTIVES: Although many physicians in daily practice assume a connection between odontogenic infections and meteorological parameters, this has not yet been scientifically proven. Therefore, the aim of the present study was to evaluate the incidence of odontogenic abscess (OA) in relation to outdoor temperature and atmospheric pressure. PATIENTS AND METHODS: An analysis of patients with an odontogenic abscess who presented at the emergency department within a period of 24 months was performed. Only patients who had not received surgical or antibiotic treatment prior to presentation and who lived in Berlin/Brandenburg were included. The OA incidence was correlated with the mean/maximum outdoor temperature and atmospheric pressure starting from 14 days before presentation. The statistical analysis was carried out using Poisson regression models with OA incidence as dependent and meteorological parameters as independent variables. RESULTS: A total of 535 patients (mean age 39.4 years; range 1 to 95 years) with 538 cases were included. Of these, 227 were hospitalized. The most frequent diagnosis was a canine fossa abscess. A significant association between mean (p = 0.0153) and maximum temperature (p = 0.008) on the day of the presentation and abscess incidence was observed. Furthermore, a significant correlation between OA incidence and maximum temperature 2 days before presentation was found (p = 0.034). The deviation of the mean temperature on the day of the presentation from the monthly mean temperature had a significant influence (p = 0.021) on the incidence of OA. In contrast to temperature, atmospheric pressure had no significant influence on the incidence of OA. CONCLUSION: This study supports a relationship between the incidence of odontogenic abscess and outdoor temperature, but not atmospheric pressure. A significantly higher frequency of patients with an OA presented at our emergency department on days with (comparably) low and high outdoor temperatures. Furthermore, a significant correlation between incidence and maximum temperature 2 days before presentation was found. CLINICAL RELEVANCE: The treatment of odontogenic infections has become a significant economic burden to public health care facilities. The results of this study may help to adapt the numbers of doctors/dentists on duty in relation to different weather conditions. In any case, it is an impetus to think outside the box.

Meteorological Factors and Seasonal Stroke Rates: A Four-year Comprehensive Study.

INTRODUCTION: There is a growing body of evidence suggesting that acute cardiovascular events including stroke are not distributed randomly over time but instead depend on months/season of the year. We report the impact of meteorological variables in extremely hot and arid climate on stroke. METHODS: Acute stroke patients admitted from January 2014 to December 2017 were included. The data included demographics, clinical risk factors, temperature, solar radiation, relative humidity, dew point, wind speed, and atmospheric pressure. We calculated stroke rates/100,000/month. RESULTS: There were 3654 cases of stroke (ischemic stroke [IS]: 2956 [80.9%]; and intracerebral hemorrhage [ICH]: 698 [19.1%]) with no difference in hematocrit, creatinine, and blood urea between hot and cold seasons (p > .05). We observed a positive significant correlation of IS with the mean temperature (AOR: 1.023; 95% CI: 1.009-1.036; P = .001) and mean solar radiation (AOR: 1.268; 95% CI: 1.021-1.575; P = .032) showing a 2.3% and 26.8% higher risk relative to ICH respectively, a negative correlation between IS with relative humidity (AOR: 0.99; 95% CI: 0.984-0.997; P = .002), and atmospheric pressure (AOR: 0.977; 95% CI: 0.966-0.989; P < .001) was observed, 1% increase in the relative humidity correlate with 2.4% and 1% lower risk of IS incidence relative to ICH respectively. CONCLUSION: We demonstrated a distinct seasonal pattern in the incidence of stroke with an increase in IS rates relative to ICH during the summer months with higher solar radiations that cannot be explained by physiological measures suggestive of dehydration or hem-concentration.

Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study.

BACKGROUND: Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest. METHODS: Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates. RESULTS: The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true. CONCLUSION: Researchers wishing to utilize publicly available climate data - whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates.

Occupational heat stress assessment and protective strategies in the context of climate change.

Global warming will unquestionably increase the impact of heat on individuals who work in already hot workplaces in hot climate areas. The increasing prevalence of this environmental health risk requires the improvement of assessment methods linked to meteorological data. Such new methods will help to reveal the size of the problem and design appropriate interventions at individual, workplace and societal level. The evaluation of occupational heat stress requires measurement of four thermal climate factors (air temperature, humidity, air velocity and heat radiation); available weather station data may serve this purpose. However, the use of meteorological data for occupational heat stress assessment is limited because weather stations do not traditionally and directly measure some important climate factors, e.g. solar radiation. In addition, local workplace environmental conditions such as local heat sources, metabolic heat production within the human body, and clothing properties, all affect the exchange of heat between the body and the environment. A robust occupational heat stress index should properly address all these factors. This article reviews and highlights a number of selected heat stress indices, indicating their advantages and disadvantages in relation to meteorological data, local workplace environments, body heat production and the use of protective clothing. These heat stress and heat strain indices include Wet Bulb Globe Temperature, Discomfort Index, Predicted Heat Strain index, and Universal Thermal Climate Index. In some cases, individuals may be monitored for heat strain through physiological measurements and medical supervision prior to and during exposure. Relevant protective and preventive strategies for alleviating heat strain are also reviewed and proposed.

High accuracy monitoring system to estimate forest road surface degradation on horizontal curves.

Well-maintained pavements reduce occurring severe accidents on horizontal curves. For this reason, the monitoring and evaluation of pavement conditions are important. This study evaluates pavement conditions considering volumetric degradation or displacement on 11 horizontal curves in forest roads, depending on meteorological conditions, traffic effects, and curve parameters. Within this context, pavement displacement (degradation) was investigated and measured with terrestrial laser scanning (TLS) for a year on a monthly basis. In this study, two multiple regression models were developed to estimate the degradation values of a forest road. According to model 1, which was developed to estimate the loss volume values, the adjusted R2 was 0.658. For model 2, which was developed to estimate the gain volume values, the adjusted R2 was 0.490. Validations of models were evaluated with different statistical tests. In conclusion, volumetric degradation can be calculated with TLS-based data. Forest road designers should determine horizontal curve characteristics, taking into consideration the pavement degradation and traffic safety.

Respiratory syncytial virus bronchiolitis, weather conditions and air pollution in an Italian urban area: An observational study.

BACKGROUND: In this study we sought to evaluate the association between viral bronchiolitis, weather conditions, and air pollution in an urban area in Italy. METHODS: We included infants hospitalized for acute bronchiolitis from 2004 to 2014. All infants underwent a nasal washing for virus detection. A regional agency network collected meteorological data (mean temperature, relative humidity and wind velocity) and the following air pollutants: sulfur dioxide, nitrogen oxide, carbon monoxide, ozone, benzene and suspended particulate matter measuring less than 10µm (PM10) and less than 2.5µm (PM2.5) in aerodynamic diameter. We obtained mean weekly concentration data for the day of admission, from the urban background monitoring sites nearest to each child's home address. Overdispersed Poisson regression model was fitted and adjusted for seasonality of the respiratory syncytial virus (RSV) infection, to evaluate the impact of individual characteristics and environmental factors on the probability of a being positive RSV. RESULTS: Of the 723 nasal washings from the infants enrolled, 266 (68%) contained RSV, 63 (16.1%) rhinovirus, 26 (6.6%) human bocavirus, 20 (5.1%) human metapneumovirus, and 16 (2.2%) other viruses. The number of RSV-positive infants correlated negatively with temperature (p < 0.001), and positively with relative humidity (p < 0.001). Air pollutant concentrations differed significantly during the peak RSV months and the other months. Benzene concentration was independently associated with RSV incidence (p = 0.0124). CONCLUSIONS: Seasonal weather conditions and concentration of air pollutants seem to influence RSV-related bronchiolitis epidemics in an Italian urban area.

Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin.

Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series, respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.