Specific Meteorological Factors Research Articles

Rapid weather changes are associated with daily hospital visitors for atrial fibrillation accompanied by abnormal ECG repolarization: a case-crossover study

BackgroundAtrial fibrillation (AF) is highly prevalent in the population, yet the factors contributing to AF events in susceptible individuals remain partially understood. The potential relationship between meteorological factors and AF, particularly with abnormal electrocardiograph (ECG) repolarization, has not been adequately studied. This case-crossover study aims to investigate the association between meteorological factors and daily hospital visits for AF with abnormal ECG repolarization in Shanghai, China.MethodsThe study cohort comprised 10,325 patients with ECG-confirmed AF who sought treatment at Shanghai Sixth People's Hospital between 2015 and 2018. Meteorological and air pollutant concentration data were matched with the patient records. Using a case-crossover design, we analyzed the association between meteorological factors and the daily count of hospital visitors for AF with abnormal ECG repolarization at our AF center. Lag analysis models were applied to examine the temporal relationship between meteorological factors and AF events.ResultsThe analysis revealed statistically significant associations between AF occurrence and specific meteorological factors. AF events were significantly associated with average atmospheric pressure (lag 0 day, OR 0.9901, 95% CI 0.9825–0.9977, P < 0.05), average temperature (lag 1 day, OR 0.9890, 95% CI 0.9789–0.9992, P < 0.05), daily pressure range (lag 7 days, OR 1.0195, 95% CI 1.0079–1.0312, P < 0.01), and daily temperature range (lag 5 days, OR 1.0208, 95% CI 1.0087–1.0331, P < 0.01). Moreover, a significant correlation was observed between daily pressure range and daily temperature range with AF patients, particularly those with abnormal ECG repolarization, as evident in the case-crossover analysis.ConclusionThis study highlights a significant correlation between meteorological factors and daily hospital visits for AF accompanied by abnormal ECG repolarization in Shanghai, China. In addition, AF patients with abnormal ECG repolarization were found to be more vulnerable to rapid daily changes in pressure and temperature compared to AF patients without such repolarization abnormalities.

Assessing suitability of major meteorological factors for facility agriculture in mainland China

In China, more than two-thirds of protected cultivation occurs in low-tech facilities with limited ability to withstand adverse weather conditions. However, the specific meteorological factors that hinder facility agriculture production in various locations remain unclear. Here, we evaluated temperature and sunlight for assessing facility agriculture suitability in mainland China across different transplanting dates and ENSO phases (El Niño, La Niña, and Neutral) and to determine the optimal transplanting window. This aids in reducing climatic risks, and enhancing adaptation to changing climates. The results showed that growth cycles starting from March to June provide suitable temperature and sunlight, making them ideal transplanting window for many parts of northern China. However, both El Niño and La Niña significantly increased the high-temperature days and shortened the optimal transplanting window. For growth cycles starting from July to the following January, low temperatures are the primary factor limiting facility agriculture production in northern and western China. In southern China, sparse sunlight is the primary limiting factor year-round, and El Niño exacerbates this, particularly for growth cycles starting from September to November. This combined assessment of major meteorological factors, transplanting dates, ENSO phases, and regions, can assist decision-makers and growers in adapting to the changing climate and minimizing production risks.

Assessing Short-Term Weather-Induced Immune Response In Russian Far East Residents With Respiratory Diseases

Objective — To identify criteria for contrasting meteorological changes affecting the temporal dynamics of the immune response in individuals with bronchopulmonary pathology in the maritime monsoon climate of Vladivostok. Methods — We examined 178 patients with stable chronic obstructive pulmonary disease (COPD) of mild and moderate severity, 212 patients with controlled bronchial asthma (BA) and 60 apparently healthy volunteers. We assessed the cumulative response of the components of adaptive and innate immune systems to the effects of day-to-day variability in temperature, air pressure and humidity, wind speed and direction, and precipitation relative to the day of the patient’s medical examination ± 2 days. The response of the immune system was measured by an integral indicator calculated as the ratio of the sum of statistically significant correlations (p&lt;0.05) to the total sum of correlation matrix elements. Results — The short-term compensatory severity and activity of the immune response to the weather in healthy people (∑D%=2.56; ∑r=117) was almost twice as high as the similar response in patients with COPD (∑D%=1.28; ∑r=72) or BA (∑D%=1.35; ∑r=69). The immune response of apparently healthy subjects to the action of specific meteorological factors on the day of medical examination was reduced by 40%, compared with the short-term response (±2 days) in patients with COPD, but the difference was not significant. The values of the criteria for the day-to-day contrast of influencing meteorological factors in apparently healthy patients were reduced. Conclusion — The urban population in maritime monsoon climate exhibited weak and moderate weather-induced immune system responses. Wind regime, humidity and precipitation had a pronounced effect on the immune system. In patients with COPD and BA, the maximum pathogenic response of the immune system to the impact of climatic factors was observed within ±1 day.

465. A Multifaceted and Multi-Institutional Analysis of the COVID19-Associated Mucormycosis Outbreak in the Delhi Area Indicates the Simultaneous Convergence of Multiple Risk Factors

Abstract Background A major outbreak of COVID19-associated mucormycosis (CAM) in India in spring 2021 aggravated the death toll of COVID19. As the causes of that CAM outbreak remain unclear, we performed a multifaceted study of host, pathogen, environmental, and heath care-related factors in adult CAM patients (pts) in the metropolitan New Delhi area. Methods We reviewed the records of all pts diagnosed with culture- or biopsy-proven CAM at 7 hospitals in the New Delhi area (April 1 – June 30, 2021). We used a multivariate logistic regression model to compare clinical characteristics of either all CAM cases (analysis 1, n = 50) or only pts with CAM after moderate or severe COVID19 (analysis 2, n = 31). As controls for both analyses, we used 69 COVID19-hospitalized contemporary pts. Selected hospital fomites were cultured for Mucorales. Additionally, we compared meteorological data and fungal spore concentrations in outdoor air before the CAM outbreak (January-March 2021) and during the outbreak (April-June 2021). Mucorales isolates from CAM pts were identified by MALDI-TOF-MS and ITS sequencing. A subset of 15 isolates underwent whole genome sequencing (WGS). Results Risk factors for CAM in both analyses were newly diagnosed diabetes mellitus (odds ratio [OR] 8.26/5.67) and active cancer (OR 5.98/5.68) (Figure 1). Supplemental oxygen for COVID19 was associated with a lower CAM risk in both analyses (OR 0.13/0.17). Another significant CAM risk predictor identified only in analysis 1 was severe COVID19 (WHO score ≥ 6, OR 4.09), while remdesivir therapy (OR 0.40) and ICU admission for COVID19 were protective (OR 0.41) (Figure 1). No Mucorales were cultured from hospital fomites. The CAM incidence peak coincided with a significant uptick in environmental spore concentrations but was not linked to specific meteorological factors. Rhizopus was the predominant Mucorales genus (64%) identified by MALDI-TOF-MS and ITS sequencing; WGS found no clonal population of isolates but detected 2 cases of the rare pathogen Lichtheimia ornata. Figure 1 Conclusion An intersection of host, environmental, pathogen and healthcare-related factors might have contributed to the emergence of CAM. Surrogates of access to advanced treatment of COVID19 were associated with lower CAM risk. Disclosures Dimiitrios P. Kontoyiannis, MD, ScD, PhD (hon), AbbVie: Advisor/Consultant|Astellas Pharma: Advisor/Consultant|Astellas Pharma: Grant/Research Support|Astellas Pharma: Honoraria|Cidara Therapeutics: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Grant/Research Support|Gilead Sciences: Honoraria|Merck: Advisor/Consultant.

The altered drivers of evapotranspiration trends around the recent warming hiatus in China

AbstractThis study focuses on the trends and the causes of variation in actual evapotranspiration (AET) around the warming hiatus over China by a comprehensive analysis applying various temporal–spatial methods. It is observed that the annual AET showed a different trend around 2000 for China as a whole. By employing segmented regression analysis for detecting warming hiatus points, high temporal inconsistency can be found in eight climatic regions of China. The impacts of meteorological variables on AET were further identified by affecting the intensity and relative change of meteorological factors. AET was highly correlated (p &lt; .01) with solar radiation in the southeast (R = 0.80) and air specific humidity in the northwest areas (R = 0.83). AET changes presented the highest sensitivity to specific humidity in Northwest before 2006 and in north central China after 2003, with sensitivity coefficients of 1.48 and 1.74, respectively. Three variables, including air specific humidity (with an average contribution rate of ~17% in the northwest), short‐wave radiation and air temperature, can be the main factors that lead to the changes in AET. The specific meteorological factors varied from region to region: the changes in AET can be ascribed to the increased wind and short‐wave radiation in north central China and east China, the decreased air temperature in Tibetan Plateau, the increased specific humidity in southeast China during warming hiatus, and so on. After the warming hiatus occurred, the dominant factor of AET trends changed from air specific humidity to short‐wave radiation and other factors. Generally, air specific humidity and air temperature have played leading roles in AET trends during the past 30 years.

Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem

Environmental monitoring using satellite remote sensing is challenging because of data gaps in eddy-covariance (EC)-based in situ flux tower observations. In this study, we obtain the latent heat flux (LE) from an EC station and perform gap filling using two deep learning methods (two-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) neural networks) and two machine learning (ML) models (support vector machine (SVM), and random forest (RF)), and we investigate their accuracies and uncertainties. The average model performance based on ~25 input and hysteresis combinations show that the mean absolute error is in an acceptable range (34.9 to 38.5 Wm−2), which indicates a marginal difference among the performances of the four models. In fact, the model performance is ranked in the following order: SVM &gt; CNN &gt; RF &gt; LSTM. We conduct a robust analysis of variance and post-hoc tests, which yielded statistically insignificant results (p-value ranging from 0.28 to 0.76). This indicates that the distribution of means is equal within groups and among pairs, thereby implying similar performances among the four models. The time-series analysis and Taylor diagram indicate that the improved two-dimensional CNN captures the temporal trend of LE the best, i.e., with a Pearson’s correlation of &gt;0.87 and a normalized standard deviation of ~0.86, which are similar to those of in situ datasets, thereby demonstrating its superiority over other models. The factor elimination analysis reveals that the CNN performs better when specific meteorological factors are removed from the training stage. Additionally, a strong coupling between the hysteresis time factor and the accuracy of the ML models is observed.

Влияние метеорологических факторов на возникновение кровотечений из варикозно расширенных вен пищевода

BACKGROUND: The problem of liver cirrhosis and associated bleedings from the varicose veins of the esophagus, unfortunately, remains highly relevant for Russia. Undoubtedly, it is important to identify factors that provoke an episode of bleeding. From a practical point of view, the most interesting ones are those that have an easily detectable nature and the maximum possible predictability. One of these factors, which is constantly varying throughout the year, is meteorological conditions. AIM: To evaluate the influence of meteorological factors on the occurrence of bleeding from varicose veins of the esophagus and the possibility of using this information to improve the schemes of primary prevention. MATERIALS AND METHODS: The material for the study was the results of examination and treatment of 86 patients hospitalized in 2016–2018 at the Emergency Hospital of Ryazan with a diagnosis of bleeding from esophageal varices. All these patients underwent fibroesophago-gastroduodenoscopy upon admission. The analysis of meteorological observations for 2016–2018 was carried out based on information from archived summaries of weather data. The results obtained were statistically processed using a standard set of programs. RESULTS: When analyzing the number of hospitalizations of patients with bleeding from varicose veins of the esophagus in different months of the year, a pronounced unevenness of admissions is revealed, the coefficient of variability is 53.65%. After excluding extreme values from the calculations, it was found that the incidence of the pathology increases in spring and autumn, decreases in summer and winter. The maximum incidence occurs in April and September, and the minimum in July and February. To determine the effect on patients of specific meteorological factors, a meteorological profile for a number of months was created from the archived weather data. In the course of statistical processing of the data, it was found that the only meteorological factor demonstrating a stable strong direct correlation with the number of hospitalizations of patients with bleeding from varicose veins of the esophagus (Pearson's correlation coefficient 0.9449) is the average atmospheric pressure gradient. This fact proves the ability of sudden changes in atmospheric pressure to provoke episodes of bleeding from the veins of the esophagus. CONCLUSIONS: When planning a course of primary pharmacological prevention of bleeding emerging the varicose veins of the esophagus, it is necessary to take into account the morbidity peaks occurring in April and September, as well as the ability of sudden changes of the atmospheric pressure to trigger the bleeding episodes.

Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression

AbstractRecently, due to accelerations in urban and industrial development, the health impact of air pollution has become a topic of key concern. Of the various forms of air pollution, fine atmospheric particulate matter (PM2.5; particles less than 2.5 micrometers in diameter) appears to pose the greatest risk to human health. While even moderate levels of PM2.5 can be detrimental to health, spikes in PM2.5 to atypically high levels are even more dangerous. These spikes are believed to be associated with regionally specific meteorological factors. To quantify these associations, we develop a Bayesian spatiotemporal quantile regression model to estimate the spatially varying effects of meteorological variables purported to be related to PM2.5 levels. By adopting a quantile regression model, we are able to examine the entire distribution of PM2.5 levels; for example, we are able to identify which meteorological drivers are related to abnormally high PM2.5 levels. Our approach uses penalized splines to model the spatially varying meteorological effects and to account for spatiotemporal dependence. The performance of the methodology is evaluated through extensive numerical studies. We apply our modeling techniques to 5 years of daily PM2.5 data collected throughout the eastern United States to reveal the effects of various meteorological drivers.

Dynamic Monitoring of Haze Pollution Using Satellite Remote Sensing

The severe haze pollution episode from December 1 to 10, 2013 occurred in Shanghai area, and seriously threatened human health and transportation safety. As regional air pollution, it is practical significance to the rapid monitoring and comprehensive analysis for haze treatment. Based on the moderate resolution imaging spectro- radiometer (MODIS) and cloud-aerosol LiDAR with orthogonal polarization (CALIOP) data, via combing with meteorological features during the haze pollution period, the current study analyzed and explored the spatial distribution, aerosol vertical structure, source and migration path of haze pollution. The results showed that: (1) There are obvious aerosol layers over Shanghai area, aerosol optical depth (AOD) fluctuates frequently and has distinctive features such as complexity, regionality and seasonality. (2) The aerosol layer is mainly at the heights ranging from 0 to 2 km and composed mostly of spherical aerosol but there is also non-spherical aerosol. (3) The total backscattering intensity of aerosol is centralized at the heights ranging from 0 to 1 km, especially at the heights ranging from 0 to 0.5 km. (4) The haze pollution episode was affected by both specific meteorological factors and migration of the non-local particles in the surrounding areas and northern China.

What is the role of meteorological variables on involuntary admission in psychiatric ward? An Italian cross-sectional study

Weather affects physical and mental health through several modalities which are not fully elucidated. The aim of the present study was to investigate the impact of meteorological variables and other indexes in a large sample of hospitalized patients, focusing on subjects who were involuntarily admitted. We hypothesized a direct relation between the amount of involuntary admissions and mean sunshine hours. Furthermore, we supposed that specific meteorological factors may significantly influence hospitalizations of patients affected by severe psychiatric conditions. All subjects were consecutively recruited from the Psychiatric Inpatient Unit of San Luigi Gonzaga Hospital, Orbassano (Turin, Italy) from September 2013 to August 2015. Socio-demographic and clinical characteristics were carefully collected. Meteorological data were derived by the Italian Meteorology's Climate Data Service of Physics Department of the University of Turin (Latitude: 45°03′07,15″ Nord, Longitude: 007°40′53,30″ Est, Altitude: 254 m above the sea level) (http://www.meteo.dfg.unito.it/). Our data indicate significant differences regarding temperature (minimum, maximum, and medium), solar radiation, humidex and windchill index, and hours of sunshine in psychiatric patients who were involuntarily hospitalized. After logistic regression analyses, only maximum and medium temperature, and humidex index remained significantly associated with involuntary admission in an emergency psychiatric ward. The limitations of this study include the cross-sectional study design and the single hospital for patients' recruitment. Furthermore, results and seasonal patterns obtained by patients requiring hospitalization might significantly differ from those who were not hospitalized. Exploring in a more detailed manner those environmental factors associated with involuntary admissions could lead to early intervention and prevention strategies for such distressing hospitalizations.

Effects of meteorological factor and air pollution on sudden sensorineural hearing loss using the health claims data in Busan, Republic of Korea.

Specific meteorological factors, including air pollution in the form of particulate matter (PM), affect the development of otologic disease and have adverse effects on the cardiovascular and respiratory systems. This study investigated relationships between the development of sudden sensorineural hearing loss(SSNHL) and meteorological factor with air pollution including PM. The daily patient number in 2015 admitted to the hospital with SSNHL were extracted from the Health Insurance Review and Assessment Service Bigdata in Busan. The meteorological factors and air pollution data of Busan area were obtained from meteorological stations in Busan. The relationship between the number of hospitalizations and the climatic factors was checked. SSNHL patient group showed more common in women, and the highest rates were observed in patients in their 50s. The daily mean patient numbers were 2.27. The number of SSNHL patients in spring was statistically significantly higher than that in summer. The mean daily PM10 and PM2.5 concentrations were 48.0 and 29.4 μg/m3, respectively. The mean wind speed, maximum wind speed and daily atmospheric pressure range was weakly positively associated with SSNHL patient number. There were weak negative correlations between maximum PM2.5 and SSNHL admissions. The mean temperature and wind chill index showed non-significantly negative relationships with SSNHL admissions. In Busan area, statistically significant weak relationships were detected between the daily numbers of patients admitted to the hospital with SSNHL and meteorological data, including PM level. Further investigation of these associations is required.

Association between viral seasonality and meteorological factors

Numerous viruses can cause upper respiratory tract infections. They often precede serious lower respiratory tract infections. Each virus has a seasonal pattern, with peaks in activity in different seasons. We examined the effects of daily local meteorological data (temperature, relative humidity, “humidity-range” and dew point) from Edinburgh, Scotland on the seasonal variations in viral transmission. We identified the seasonality of rhinovirus, adenovirus, influenza A and B viruses, human parainfluenza viruses 1–3 (HPIV), respiratory syncytial virus (RSV) and human metapneumovirus (HMPV) from the 52060 respiratory samples tested between 2009 and 2015 and then confirmed the same by a generalised linear model. We also investigated the relationship between meteorological factors and viral seasonality. Non-enveloped viruses were present throughout the year. Following logistic regression adenovirus, influenza viruses A, B, RSV and HMPV preferred low temperatures; RSV and influenza A virus preferred a narrow “humidity-range” and HPIV type 3 preferred the season with lower humidity. A change (i.e. increase or decrease) in specific meteorological factors is associated with an increase in activity of specific viruses at certain times of the year.

Meteorological Factors Affecting Airport Operations during the Winter Season in the Midwest

Abstract During the coldest months of the year, weather systems bring a variety of winter weather to most of the continental United States in the form of snow, sleet, and freezing rain, which along with strong winds, low clouds, and reduced visibilities may create dangerous conditions. These weather conditions can result in major disruptions in air travel, leading to delays and cancellations of hundreds or thousands of flights, thus affecting the plans of millions of travelers. To assess the specific meteorological factors that prompt flight delays and cancellations in the Midwest region of the United States during wintertime, a comprehensive study was performed on nine of the largest airports (by passenger boardings) in the area. Flight delay and cancellation data from 11 winter seasons (2005–06 to 2015–16) were collected from the Bureau of Transportation Statistics (BTS) and analyzed along with climatological data from the National Centers for Environmental Information (NCEI). A classification scheme was developed, and each flight was categorized according to the meteorological factor that could have prompted its delay. The results of the study revealed that visibility was the main meteorological factor affecting midwestern airports, with low ceilings as a close second. Blizzards were the main cause for flight cancellations.

Identifying and modeling meteorological risk factors associated with pre-harvest contamination of Listeria species in a mixed produce and dairy farm

This study sought to investigate the prevalence of Listeria species (including L. monocytogenes) in a mixed produce and dairy farm and to identify specific meteorological factors affecting Listeria spp. presence. Environmental samples were collected monthly from locations within the mixed farm over 14months and were analyzed for Listeria spp. Meteorological factors were evaluated for their association with the presence of Listeria spp. by using logistic regression (LR) and random forest (RF). The developed LR model identified wind speed and precipitation as significant risk factors (P<0.05), indicating higher wind speed at day 2 prior to sampling and higher average precipitation over the previous 25days before sampling increased the probability of isolation of Listeria spp. from the mixed farm. Results from RF revealed that average wind speed at day 2 prior to sampling and average precipitation in the previous 25days before sampling were the most important factors influencing the presence of Listeria spp., which supported the findings from LR. These findings indicate that the occurrence of Listeria spp. was influenced by wind speed and precipitation, suggesting run-off and wind-driven dust might be possible routes of pathogen transmission in mixed farms. The developed LR and RF models, with robust predictive performances as measured by the area under the receiver operating characteristic curves, can be used to predict Listeria spp. contamination risk in a mixed farm under different weather conditions and can help with the evaluation of farm management practices and the development of control strategies aimed at reducing pre-harvest microbial contamination in a mixed farming system.

Association between lower air pressure and the onset of ischemic colitis: a case-control study.

Ischemic colitis (IC) often affects the elderly. Proarteriosclerotic factors, such as hypertension and smoking, and cardiovascular disease are considered major contributors to IC. Although a possible link between certain cerebrocardiovascular disorders and meteorological phenomena has been reported, the relationship between IC onset and weather changes remains uninvestigated. This study examined whether specific meteorological factors were associated with the occurrence of IC. We retrospectively enrolled 303 patients who had been diagnosed with IC between January 2003 and June 2010 at Suwa Red Cross Hospital in Nagano Prefecture, Japan. The meteorological data of the days on which IC patients visited the hospital (IC+ days) were compared with those of the days on which IC patients did not (IC- days). Univariate analysis indicated that IC+ days had significantly lower air pressure (P<0.001), depressed air pressure from the previous day (P<0.001), and fewer daylight hours (P<0.001), as well as higher air temperature (P=0.017), air humidity (P=0.004), wind velocity (P<0.001), and rainfall (P=0.012) compared with IC- days. Multivariate logistic regression analysis of the meteorological data showed that air pressure (odds ratio: 0.935, P<0.001) and change in air pressure from the previous day (odds ratio: 0.934, P<0.001) were related to onset of IC. Lower air pressure and decrease in air pressure from the previous day are possible novel factors associated with the development of IC.

Evaluation of meteorological factors associated with pre-harvest contamination risk of generic Escherichia coli in a mixed produce and dairy farm

Produce products from mixed farms may be at risk due to its unique setting and practice. This study aimed to identify specific meteorological factors affecting the presence and population levels of generic Escherichia coli (as an indicator for fecal contamination) in a mixed produce and dairy farm. Over 14 months, environmental samples were collected from locations within a mixed produce and dairy farm, and enumerated for generic E. coli. Local weather factors were evaluated for their association with the presence of generic E. coli by using logistic regression and classification tree method. In addition, negative binomial and regression tree models were developed to identify factors affecting population levels of generic E. coli from a sample location. The developed models identified monthly precipitation and monthly temperature as risk factors. The higher prevalence or higher population levels of generic E. coli were observed under higher average precipitation (>1.4 mm) and higher average temperature (>20.2 °C) in the previous 30 days. However, prevalence and population level of generic E. coli were negatively correlated with precipitation and wind speed at day 2 before sampling, suggesting that higher precipitation (>0.5 mm) and higher wind speed (>2.5 m/s) two days before sampling may lower the prevalence and count of generic E. coli within the mixed farm. Results suggest that presence and population level of E. coli in mixed farms is influenced by temperature, precipitation, and wind speed. Meteorological factors should be considered when evaluating farm management practices to reduce pre-harvest pathogen contamination.

Highs and lows, ups and downs: Meteorology and mood in bipolar disorder

Seasonal variation of manic and depressive symptoms is a controversial topic in bipolar disorder research. Several studies report seasonal patterns of hospital admissions for depression and mania and variation in symptoms that appear to follow a seasonal pattern, whereas others fail to report such patterns. Differences in research methodologies, data analysis strategies, and temporal resolution of data may partly explain the variation in findings between studies. The current study adds a novel perspective to the literature by investigating specific meteorological factors such as atmospheric pressure, hours of sunshine, relative humidity, and daily maximum and minimum temperatures as more proximal predictors of self-reported daily mood change in people diagnosed with bipolar disorder. The results showed that daily maximum temperature was the only meteorological variable to predict clinically-relevant mood change, with increases in temperature associated with greater odds of a transition into manic mood states. The mediating effects of sleep and activity were also investigated and suggest at least partial influence on the prospective relationship between maximum temperature and mood. Limitations include the small sample size and the fact that the number and valence of social interactions and exposure to natural light were not investigated as potentially important mediators of relationships between meteorological factors and mood. The current data make an important contribution to the literature, serving to clarify the specific meteorological factors that influence mood change in bipolar disorder. From a clinical perspective, greater understanding of seasonal patterns of symptoms in bipolar disorder will help mood episode prophylaxis in vulnerable individuals.

Statistical modeling of insect behavioral response to changes in weather conditions in Brassica napus L.

Understanding specific meteorological factors that affect the foraging activity of pollinator insects can provide valuable information to ensure appropriate levels of pollination of crops. This research was carried out to verify the spectrum of pollinating insects and their foraging behavior in relation to specific meteorological variables in the Brassica napus (rapeseed) crop. Data collected consisted of the following observations: number of visits by each pollinator, floral resources collected by Africanized honeybee Apis mellifera L., number of flowers visited by Africanized honeybees in 1 min, number of flowers visited by Africanized honeybees in one specific plant during the timed observation of 1 min, and the time taken by the honeybees to visit each flower. In this study the analyses were made through different generalized linear models. The Africanized honeybees were the most abundant pollinating insects (88%) and most visitors were observed collecting nectar (90%). The Africanized honeybees visited a total of 12.9 ± 1.40 flowers in 1 min and 2.96 ± 1.09 flowers of a single plant in 1 min. The time the honeybees spent on the rapeseed flowers was 4.2 ± 1.6 s. The number of floral visitors correlated closely to the variation of abiotic factors, especially temperature, relative humidity, wind speed, and solar radiation. Africanized honeybees were more active at different times of the day as compared to the other floral visitors and they foraged more intensely on the Hyola 433 hybrid than on the Hyola 61 hybrid. The Hyola 61 hybrid was more attractive to Diabrotica speciosa and Lepidoptera.

Suitability of four stomatal conductance models in agro-pastoral ecotone in North China: A case study for potato and oil sunflower.

The suitability of four popular empirical and semi-empirical stomatal conductance models (Jarvis model, Ball-Berry model, Leuning model and Medlyn model) was evaluated based on para-llel observation data of leaf stomatal conductance, leaf net photosynthetic rate and meteorological factors during the vigorous growing period of potato and oil sunflower at Wuchuan experimental station in agro-pastoral ecotone in North China. It was found that there was a significant linear relationship between leaf stomatal conductance and leaf net photosynthetic rate for potato, whereas the linear relationship appeared weaker for oil sunflower. The results of model evaluation showed that Ball-Berry model performed best in simulating leaf stomatal conductance of potato, followed by Leuning model and Medlyn model, while Jarvis model was the last in the performance rating. The root-mean-square error (RMSE) was 0.0331, 0.0371, 0.0456 and 0.0794 mol·m-2·s-1, the normalized root-mean-square error (NRMSE) was 26.8%, 30.0%, 36.9% and 64.3%, and R-squared (R2) was 0.96, 0.61, 0.91 and 0.88 between simulated and observed leaf stomatal conductance of potato for Ball-Berry model, Leuning model, Medlyn model and Jarvis model, respectively. For leaf stomatal conductance of oil sunflower, Jarvis model performed slightly better than Leuning model, Ball-Berry model and Medlyn model. RMSE was 0.2221, 0.2534, 0.2547 and 0.2758 mol·m-2·s-1, NRMSE was 40.3%, 46.0%, 46.2% and 50.1%, and R2 was 0.38, 0.22, 0.23 and 0.20 between simulated and observed leaf stomatal conductance of oil sunflower for Jarvis model, Leuning model, Ball-Berry model and Medlyn model, respectively. The path analysis was conducted to identify effects of specific meteorological factors on leaf stomatal conductance. The diurnal variation of leaf stomatal conductance was principally affected by vapour pressure saturation deficit for both potato and oil sunflower. The model evaluation suggested that the stomatal conductance models for oil sunflower are to be improved in further research.

Geographical and climatic factors and depression risk in the SUN project

Depression incidence has been related with seasonal periodicity and climate. The aim of the study was to estimate the possible association between depression and specific meteorological factors, namely temperature, light and rain. In total, 13,938 participants from the SUN (Seguimiento Universidad de Navarra) cohort study were included in the analysis. Subjects were classified according to daily mean temperature, number of daylight hours and amount of rain, by year, at their geographical area of residence, data supplied by the Spanish Agency of Meteorology. Participants were considered as incident cases of depression whenever they reported a physician diagnosis of depression or the use of antidepressant medication in any of the follow-up questionnaires. Cox regression models were fit to assess the relationship between climatic and geographical factors and the incidence of depression. Male subjects living in the south and centre areas of Spain showed a higher risk to develop depression compared with those living in the north area (hazard ratio = 1.6, 95% CI = 1.16-2.23 and hazard ratio = 1.41, 95% CI = 1.06-1.87, respectively). Moreover, among males, a direct association between the number of daily light hours and mean temperature and the risk of depression was also found. For men, living in rainy areas was associated with a lower risk of developing depression. Our results suggest that climate-depression relationship is more complex than previously thought, and strongly different between men and women.