Non-winter Season Research Articles

Factors Associated With Actionable Gastrointestinal Panel Results in Hospitalized Children.

There is uncertainty regarding which hospitalized patients with acute gastroenteritis (AGE) benefit from gastrointestinal panel (GIP) testing. Unnecessary testing may lead to increased costs, overdiagnosis, and overtreatment. In general, AGE management and outcomes are most impacted if an actionable (bacterial or parasitic) result is obtained. We aimed to assess which clinical reasons for ordering GIP testing ("order indications") and patient factors were associated with actionable results. This is a cross-sectional study of pediatric patients hospitalized between 2015 and 2018 at a large pediatric health care system with diarrhea and a GIP performed. Multivariable regression analysis was used to determine associations between actionable GIP results and order indication, stool frequency, and demographics. Findings were evaluated in patients with complex chronic conditions (CCC) and non-CCC patients. There were 1124 GIPs performed in 967 encounters. Non-CCC patients had more actionable results than CCC patients, and reasons for testing differed. Across both cohorts, age ≥1 year old was positively associated with actionable results. For non-CCC patients, actionable results were associated with "diarrhea with blood or pus" order indication and nonwinter season; international travel was associated with non-Clostridioides difficile bacteria and parasites. No order indications were associated with actionable results for CCC patients. Patient factors and order indications that may help identify children hospitalized for AGE with actionable GIP results include older age (regardless of CCC status), as well as bloody stools and international travel in previously healthy children. Prospective validation of these findings could help improve diagnostic stewardship and decrease unnecessary testing.

An unusual case of fatal hypothermia involving topical diphenhydramine.

Diphenhydramine is an antihistamine drug widely used to alleviate symptoms caused by allergies and the common cold. Diphenhydramine-involved fatalities have been reported in the past but usually involving overdose by ingestion. We report a peculiar case of fatal hypothermia during non-winter season involving topical diphenhydramine. A 23-year-old male with no known preexisting medical conditions was found dead in the bathroom of his apartment with a small amount of running water on his back. Postmortem examinations and toxicological analysis on blood and urine were performed. Color difference was apparent between the right and left cardiac blood. Wischnewski spots were observed in the gastric mucosa. Histological examination revealed no obvious findings that could attribute to serious cardiovascular events. Drug screening by gas chromatograph-tandem mass spectrometry (GC/MS/MS) detected diphenhydramine in blood and urine. Further quantification revealed the postmortem concentrations to be 0.44μg/mL in blood and 2500μg/mL in urine. The cause of death was determined to be hypothermia. Diphenhydramine-induced drowsiness and possible intrinsic cardiac factor may have led to prolonged impaired consciousness, preventing his ability to escape from the running cold water leading to hypothermia and death.

Forecasting Uncertainty Intervals for Return Period of Extreme Daily Electricity Consumption

The use of extreme value theory (EVT) is usually aimed at quantifying the asymptotic behaviour of extreme quantiles. The generalised Pareto distribution (GPD) with peaks-over-threshold (POT) approach is applied to bootstrap uncertainty intervals for the return periods of extreme daily electricity consumption in South Africa. The leeway of extremes on daily electricity consumption studied here is the impetus behind this study. To examine the effect of a time-based and extreme non-stationary trend in a dataset, a non-stationary GPD is cast-off in computing the shape parameter and, this resulted in the establishment of a type III GPD known as a Weibull class for the South African electricity sector. Results of this study revealed a non-stationary trend with a prediction power of 89.6% for the winter season and 85.65% non-winter season. This means that EVT provides a robust basis for statistical modelling of extreme values. Furthermore, a base for future researchers for conducting studies on emerging markets, more specifically in the South African context has also been contributed.

Seasonal Space Use and Movement of a Grey Wolf (Canis lupus) in a Protected Archipelago in Lake Superior, Ontario

Data on movement of grey wolves (Canis lupus) in island systems is largely restricted to coastal environments and little is known about their space use and movement in freshwater archipelagos. We used data from a GPS-collared wolf in a protected archipelago in Lake Superior, Ontario to examine broad seasonal patterns in space use, movement, and activity. Over approximately 1 y of monitoring, the wolf made 190 crossings between islands and showed more extensive use of the archipelago during the nonwinter season. When ice was present in the archipelago, the mean weekly inter-island crossing rate of the wolf (± se) was 6.08 ± 1.31, with crossings largely restricted to the interior islands bounded by ice, compared to 2.85 ± 0.45 during the open water season. Mean wolf activity was highest in the nonwinter season, but movement rates were comparable across seasons. Our study is the first to document wolf movements in a freshwater archipelago with seasonal ice cover and supports data collection at fine temporal scales to better understand trends in wolf space use and movement at small spatial scales.

An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark

Introduction: Cycling is one of the main forms of transportation in Denmark. However, while the number of traffic crash fatalities in the country has decreased over the past decade, the frequency of cyclists killed or seriously injured has increased. The high rate of serious injuries and fatalities associated with cycling emphasizes the increasing need for mitigating the severity of such crashes. Method: This study conducted an in-depth analysis of cyclist injury severity resulting from single and multiparty bicycle-involved crashes. Detailed information was collected using self-reporting data undertaken in Denmark for a 12-month period between 1 November 2012 and 31 October 2013. Separate multilevel logistic (MLL) regression models were applied to estimate cyclist injury severity for single and multiparty crashes. The goodness-of-fit measures favored the MLL models over the standard logistic models, capturing the intercorrelation among bicycle crashes that occurred in the same geographical area. Results: The results also showed that single bicycle-involved crashes resulted in more serious outcomes when compared to multiparty crashes. For both single and multiparty bicycle crash categories, non-urban areas were associated with more serious injury outcomes. For the single crashes, wet surface condition, autumn and summer seasons, evening and night periods, non-adverse weather conditions, cyclists aged between 45 and 64 years, male sex, riding for the purpose of work or educational activities, and bicycles with light turned-off were associated with severe injuries. For the multiparty crashes, intersections, bicycle paths, non-winter season, not being employed or retired, lower personal car ownership, and race bicycles were directly related to severe injury consequences. Practical Applications: The findings of this study demonstrated that the best way to promote cycling safety is the combination of improving the design and maintenance of cycling facilities, encouraging safe cycling behavior, and intensifying enforcement efforts.

A Point Process Characterisation of Extreme Temperatures: an Application to South African Data

The point process (PP) modelling approach is considered a more elegant alternative of extreme value analysis. This is because of its capability in modelling both the frequency and intensity rates of the occurrence of extremes. In this paper, we demonstrate the use of the PP modelling approach in which stationary and non-stationary models are used in modelling average maximum daily temperature (AMDT) in South Africa. The data constitutes average daily temperature observations that are collected by the South African Weather Services over the period 1 January 2000 to 30 August 2010. This study is interested on the occurrence of extreme high temperature and because of that the data for non-winter season (1 September to 30 April) of each year is used. A penalised regression cubic smoothing spline function is used for non-linear detrending of the data and determining a fixed threshold above which excesses are extracted and used. An extremal mixture model is then fitted to determine a threshold in which a boundary corrected kernel density is fitted to the bulk model and a generalised Pareto distribution (GPD) fitted to the tail of the distribution. The data exhibits properties of short-range dependence and strong seasonality, leading to declustering. An interval estimator method is used to decluster data for the purpose of fitting PP models to cluster maxima. The models that are used in this paper are nested and, as a result, likelihood ratio tests are conducted using the deviance statistic. The tests support the fit of the stationary PP model. We further fitted the stationary GPD and used the formal tests which are the Cramér-von Mises test and the Anderson-Darling test to diagnose fit. These tests and the diagnostic plots support fit of the stationary GPD to cluster maxima. Uncertainty of the estimates of GPD parameters is assessed in this paper using bootstrap re-sampling approach. The stationary PP model was used with the reparameterisation approach to determine frequency of the occurrence of extremely hot days, which are found to be 15 times per year. The modelling framework and results of this study are important to power utility companies in scheduling and dispatching electricity to customers during a hot spell.

Inpatient burden of juvenile dermatomyositis among children in the United States

BackgroundJuvenile dermatomyositis (JDM) is a rare autoimmune disease that causes significant morbidity and quality of life impairment. Little is known about the inpatient burden of JDM in the US. Our goal was to determine the prevalence and risk factors for hospitalization with juvenile dermatomyositis and assess inpatient burden of JDM.MethodsData on 14,401,668 pediatric hospitalizations from the 2002–2012 Nationwide Inpatient Sample (NIS) was analyzed. ICD-9-CM coding was used to identify hospitalizations with a diagnosis of JDM.ResultsThere were 909 and 495 weighted admissions with a primary or secondary diagnosis of JDM, respectively. In multivariable logistic regression models with stepwise selection, female sex (logistic regression; adjusted odds ratio [95% confidence interval]) (2.22 [2.05–2.42]), non-winter season (fall: 1.18[1.06–1.33]; spring (1.13 [1.01–1.27]; summer (1.53 [1.37–1.71]), non-Medicaid administered government insurance coverage (2.59 [2.26–2.97]), and multiple chronic conditions (2–5: 1.41[1.30–1.54]; 6+: 1.24[1.00–1.52]) were all associated with higher rates of hospitalization for JDM. The weighted total length of stay (LOS) and inflation-adjusted cost of care for patients with a primary inpatient diagnosis of JDM was 19,159 days and $49,339,995 with geometric means [95% CI] of 2.50 [2.27–2.76] days and $7350 [$6228–$8674], respectively. Costs of hospitalization in primary JDM and length of stay and cost in secondary JDM were significantly higher compared to those without JDM. Notably, race/ethnicity was associated with increased LOS (log-linear regression; adjusted beta [95% confidence interval]) (Hispanic: 0.28 [0.14–0.41]; other non-white: 0.59 [0.31–0.86]) and cost of care (Hispanic: 0.30 [0.05–0.55]).ConclusionJDM contributes to both increased length of hospitalization and inpatient cost of care. Non-Medicaid government insurance was associated with higher rates of hospitalization for JDM while Hispanic and other non-white racial/ethnic groups demonstrated increased LOS and cost of care.

Biomass and fossil fuel combustion contributions to elemental carbon across the San Francisco Bay Area

Ambient black carbon (BC) has detrimental effects on both human health and near-term global warming. To mitigate these negative effects, there have been significant efforts to reduce emissions of BC from anthropogenic and biomass burning sources in California's Bay Area since the 1960s. Recent reductions in BC have mainly been from fossil fuel combustion sources such as diesel but additional reductions may be needed for contemporary carbon sources like biomass burning and meat cooking. In this study, PM10 (particulate matter with aerodynamic diameter of less than or equal to 10 μm) samples were collected at seven sites across the San Francisco Bay Area from November 2011 to October 2012 to create winter and non-winter composites for each site. Radiocarbon (14C) abundance and chemical mass balance (CMB) modeling were used for source apportionment of ambient elemental carbon (EC, a proxy for BC). The 14C abundance in the EC fraction was used to quantify the relative contributions of fossil carbon and contemporary carbon sources. The average biomass burning contributions are 48 ± 8% and 41 ± 5% for winter and non-winter seasons, respectively, across the Bay Area. Ambient concentrations of EC are approximately two to three times higher during the winter compared to the non-winter season, except for Cupertino. A CMB model, using bulk aerosol composition and select inorganic compounds, was used to understand the contributions of natural gas combustion, gasoline exhaust, and diesel exhaust to fossil carbon as well as the contribution of biomass burning and meat cooking to contemporary carbon. The different apportionment methods for EC (14C and CMB analysis) agree within 16 ± 12% for fraction fossil and biomass burning for both winter and non-winter seasons. The contemporary contribution to EC is much higher than estimated by current emission inventories, suggesting that the inventories may overestimate the contribution from fossil fuels, particularly diesel exhaust. The results from this study are compared to literature values from other 14C-EC or BC studies from across the world.

Modelling average maximum daily temperature using r largest order statistics: An application to South African data.

Natural hazards (events that may cause actual disasters) are established in the literature as major causes of various massive and destructive problems worldwide. The occurrences of earthquakes, floods and heat waves affect millions of people through several impacts. These include cases of hospitalisation, loss of lives and economic challenges. The focus of this study was on the risk reduction of the disasters that occur because of extremely high temperatures and heat waves. Modelling average maximum daily temperature (AMDT) guards against the disaster risk and may also help countries towards preparing for extreme heat. This study discusses the use of the r largest order statistics approach of extreme value theory towards modelling AMDT over the period of 11 years, that is, 2000–2010. A generalised extreme value distribution for r largest order statistics is fitted to the annual maxima. This is performed in an effort to study the behaviour of the r largest order statistics. The method of maximum likelihood is used in estimating the target parameters and the frequency of occurrences of the hottest days is assessed. The study presents a case study of South Africa in which the data for the non-winter season (September–April of each year) are used. The meteorological data used are the AMDT that are collected by the South African Weather Service and provided by Eskom. The estimation of the shape parameter reveals evidence of a Weibull class as an appropriate distribution for modelling AMDT in South Africa. The extreme quantiles for specified return periods are estimated using the quantile function and the best model is chosen through the use of the deviance statistic with the support of the graphical diagnostic tools. The Entropy Difference Test (EDT) is used as a specification test for diagnosing the fit of the models to the data.

Statistical Investigation of Truck Type Distribution on Cold Region Highways During Winter Months

Recent research on impact of weather interaction on classified traffic volume variations on provincial highways in cold regions showed that the total truck volume is not affected with severity of snowfall and cold temperature. However, no research is conducted to analyze the variations in truck distributions, despite of its importance for truck counting and monitoring program. Described in this paper is the statistical investigation of association of truck type distribution on cold region highways during severe winter months and seasons in a year. The investigation is based on weigh-in-motion data collected from six sites located on five provincial highways in Alberta, Canada. Trucks were classified into three types such as straight unit, single trailer, and multi trailer using the FHWA vehicle classification scheme. Two statistical tests namely Chi-squared test and Binomial probability test are applied to analyze the distributional change in three different truck classes during high snowfall and low temperature conditions. The analysis suggested that the truck type distribution does not change from winter to non-winter season for regional commuter road (Highway 2A) and long distance roads (Highway 2 and Highway 16). Also, no change in truck distribution from month to month was noticed during sever winter months. Consistent results were not found for special roads such as Highway 44 due to difference in road user characteristics. The study findings have practical implications for rationalization of the length and frequency of traffic counts including classified traffic monitoring programs throughout the year. The knowledge about independency of truck type distribution with various seasons is likely to help in effective traffic monitoring and estimation of the highway planning and design parameters like Truck Annual Average Daily Traffic (TAADT), Truck Average Daily Traffic (TADT) and Design Hour Truck Volume etc.

Widespread and persistent ozone pollution in eastern China during the non-winter season of 2015: observations and source attributions

Abstract. Rapid growth of industrialization, transportation, and urbanization has caused increasing emissions of ozone (O3) precursors recently, enhancing the O3 formation in eastern China. We show here that eastern China has experienced widespread and persistent O3 pollution from April to September 2015 based on the O3 observations in 223 cities. The observed maximum 1 h O3 concentrations exceed 200 µg m−3 in almost all the cities, 400 µg m−3 in more than 25 % of the cities, and even 800 µg m−3 in six cities in eastern China. The average daily maximum 1 h O3 concentrations are more than 160 µg m−3 in 45 % of the cities, and the 1 h O3 concentrations of 200 µg m−3 have been exceeded on over 10 % of days from April to September in 129 cities. Analyses of pollutant observations from 2013 to 2015 have shown that the concentrations of CO, SO2, NO2, and PM2.5 from April to September in eastern China have considerably decreased, but the O3 concentrations have increased by 9.9 %. A widespread and severe O3 pollution episode from 22 to 28 May 2015 in eastern China has been simulated using the Weather Research and Forecasting model coupled to chemistry (WRF-CHEM) to evaluate the O3 contribution of biogenic and various anthropogenic sources. The model generally performs reasonably well in simulating the temporal variations and spatial distributions of near-surface O3 concentrations. Using the factor separation approach, sensitivity studies have indicated that the industry source plays the most important role in the O3 formation and constitutes the culprit of the severe O3 pollution in eastern China. The transportation source contributes considerably to the O3 formation, and the O3 contribution of the residential source is not significant generally. The biogenic source provides a background O3 source, and also plays an important role in the south of eastern China. Further model studies are needed to comprehensively investigate O3 formation for supporting the design and implementation of O3 control strategies, considering rapid changes of emission inventories and photolysis caused by the Atmospheric Pollution Prevention and Control Action Plan released by the Chinese State Council in 2013.

A general method for calculating the optimal leaf longevity from the viewpoint of carbon economy.

According to the viewpoint of the optimal strategy theory, a tree is expected to shed its leaves when they no longer contribute to maximisation of net carbon gain. Several theoretical models have been proposed in which a tree was assumed to strategically shed an old deteriorated leaf to develop a new leaf. We mathematically refined an index used in a previous theoretical model [Kikuzawa (Am Nat 138:1250-1263, 1991)] so that the index is exactly proportional to a tree's lifelong net carbon gain. We also incorporated a tree's strategy that determines the timing of leaf expansion, and examined three kinds of strategies. Specifically, we assumed that a new leaf is expanded (1) immediately after shedding of an old leaf, (2) only at the beginning of spring, or (3) immediately after shedding of an old leaf if the shedding occurs during a non-winter season and at the beginning of spring otherwise. We derived a measure of optimal leaf longevity maximising the value of an appropriate index reflecting total net carbon gain and show that use of this index yielded results that are qualitatively consistent with empirical records. The model predicted that expanding a new leaf at the beginning of spring than immediately after shedding usually yields higher carbon gain, and combined strategy of the immediate replacement and the spring flushing earned the highest gain. In addition, our numerical analyses suggested that multiple flushing seen in a few species of subtropical zones can be explained in terms of carbon economy.

25-Hydroxyvitamin D Levels and the Risk of Mortality in the General Population

Previous studies have suggested that low 25-hydroxyvitamin D (25[OH]D) levels are associated with diabetes mellitus, hypertension, and cancers. Low levels are also associated with all-cause mortality in hemodialysis patients. Although there is a reduced risk of all-cause mortality in incident dialysis patients treated with vitamin D agents, including calcitriol and activated vitamin D3, no published studies have evaluated the risk of mortality in the general population. To test the association of low 25(OH)D levels with all-cause cancer, and cardiovascular disease (CVD) mortality, the authors analyzed the data from the Third National Health and Nutrition Examination Survey (NHANES III)-linked mortality files for 13,331 adults 20 years or older. Patient mortality rates were assessed from 1988 through 1994, with a median 8.7 years of follow-up. In multivariate logistic regression analyses, independent predictors of 25(OH)D deficiency were defined as being in the lowest quartile 25(OH)D levels ≤17.8 ng/ml. Independent risk factors associated with higher odds of 25(OH)D deficiency (≤17.8 ng/ml) were increasing age, female sex, nonwhite race/ethnicity, diabetes, higher body mass index, and current smoking. Decreased odds of deficiency were associated with vitamin D supplementation, physical activity, and nonwinter season. During the median 8.7 years of follow-up, 1806 deaths were recorded, including 777 from CVD. The all-cause mortality rate was adjusted in multivariate models for baseline demographics, season, and traditional and novel CVD risk factors. Compared with the highest quartile, the lowest quartile (25[OH]D levels <17.8 ng/ml) was associated with a 26% increased rate of all-cause mortality (mortality rate ratio,1.26; 95% confidence interval [CI], 1.08-1.46) and a population attributable risk of 3.1%. There was a higher, although statistically insignificant, risk of CVD and cancer mortality. No association was found for other causes of death. These data show that low levels of 25(OH)D (≤17.8 ng/ml) are associated with a higher risk of all-cause mortality in the general US population. To the authors' knowledge, this is the first study to evaluate this association.

25-Hydroxyvitamin D Levels and the Risk of Mortality in the General Population

In patients undergoing dialysis, therapy with calcitriol or paricalcitol or other vitamin D agents is associated with reduced mortality. Observational data suggests that low 25-hydroxyvitamin D levels (25[OH]D) are associated with diabetes mellitus, hypertension, and cancers. However, whether low serum 25(OH)D levels are associated with mortality in the general population is unknown. We tested the association of low 25(OH)D levels with all-cause, cancer, and cardiovascular disease (CVD) mortality in 13 331 nationally representative adults 20 years or older from the Third National Health and Nutrition Examination Survey (NHANES III) linked mortality files. Participant vitamin D levels were collected from 1988 through 1994, and individuals were passively followed for mortality through 2000. In cross-sectional multivariate analyses, increasing age, female sex, nonwhite race/ethnicity, diabetes, current smoking, and higher body mass index were all independently associated with higher odds of 25(OH)D deficiency (lowest quartile of 25(OH)D level, <17.8 ng/mL [to convert to nanomoles per liter, multiply by 2.496]), while greater physical activity, vitamin D supplementation, and nonwinter season were inversely associated. During a median 8.7 years of follow-up, there were 1806 deaths, including 777 from CVD. In multivariate models (adjusted for baseline demographics, season, and traditional and novel CVD risk factors), compared with the highest quartile, being in the lowest quartile (25[OH]D levels <17.8 ng/mL) was associated with a 26% increased rate of all-cause mortality (mortality rate ratio, 1.26; 95% CI, 1.08-1.46) and a population attributable risk of 3.1%. The adjusted models of CVD and cancer mortality revealed a higher risk, which was not statistically significant. The lowest quartile of 25(OH)D level (<17.8 ng/mL) is independently associated with all-cause mortality in the general population.

Seasonal chemical composition variations of wet deposition in Urumchi, Northwestern China

Urumchi suffered serious air quality problem in recent years. Although the main air pollutants and wet deposition monitoring have been undertaken by Urumchi Environmental Monitoring Center for many years, researches on chemical composition and variations of wet deposition were not there till now. In this paper, wet deposition monitoring data in Urumchi from 2000 and 2005 were selected to perform this evaluation based on data quality and data integrity. The volume-weighted mean (VWM) concentrations of parameters of wet deposition, namely pH, conductivity, SO 4 2−, NO 3 −, F −, Cl −, NH 4 +,Ca 2+, Mg 2+, Na + and K + concentrations, were 6.86 and 91.04 μs cm −1, 14.3, 1.63, 0.37, 3.78, 1.22, 4.79, 0.59, 1.05, 0.74 mg L −1, respectively. Acid precipitation appeared only in wintertime in few cases, and its extreme value was 4.96; the maximum emerged during sand storm event in the non-winter season with the value of 9.35. Wet deposition and air pollution characteristics varied with seasons. In order to make a certainty of possible sources of ions in different seasons, principal component analysis was applied, and conclusions were drawn that in wintertime, the predominant contributor to the wet deposition was coal combustion for residential heating; however, in the non-winter season, the situation was more complicated, dust and soil from outside the urban, dust re-suspension, local industries process, motor vehicle emissions all played their roles.

Reduction of Pneumonia Risk by an Angiotensin I–Converting Enzyme Inhibitor in Elderly Japanese Inpatients According to Insertion/Deletion Polymorphism of the Angiotensin I–Converting Enzyme Gene

We have reported that use of angiotensin I-converting enzyme (ACE) inhibitor, which is a class of antihypertensive agent that induces cough, is an independent factor in reducing risk of pneumonia among elderly inpatients. Insertion/deletion (I/D) polymorphism of the ACE gene (ACE) has been associated with the risk of pneumonia in elderly individuals. However, the ability of ACE inhibitors to reduce pneumonia-related morbidity in individuals with the ACE polymorphism is unclear. Therefore we determined the association of ACE inhibitor use and ACE genotypes with reduction of pneumonia risk in the Japanese elderly population. We conducted a hospital-based, retrospective, case-control study to evaluate the effect of an ACE inhibitor and ACE polymorphism on incidence of pneumonia. Case subjects were pneumonia patients (N = 105) >or=65 years of age, during an 8-month period of a nonwinter season. Control subjects (n = 420) were elderly patients who were frequency matched to the case subjects by age (within +/- 2 years) and gender. Data were collected on medication with the ACE inhibitor temocapril and on known risk factors for pneumonia. The significances of differences for the risk factors were analyzed using univariate and multivariate comparisons of the case and control subjects. After adjustment for potential confounders by multiple logistic regression analysis, the odds ratio (OR) estimates for pneumonia were 0.458 (95% confidential interval [CI]: 0.230 to 0.909, P = .026) for ACE inhibitor use. Conditional logistic regression analysis according to ACE genotypes revealed significant reduction of pneumonia risk by use of temocapril compared with that in nonhypertensive individuals (ie, the reference group) in those with ACE ID + II (OR: 0. 416, 95% CI: 0.177 to 0.976, P = .044), but not in those with ACE DD (OR: 0.706, 95% CI: 0.198 to 2.518, P = .592). These results suggest that use of an ACE inhibitor is beneficial for reducing risk of pneumonia, particularly in individuals with the ACE genotypes ID + II.

Relationship between latex yield of Hevea brasiliensis and antecedent environmental parameters.

A study on the relationship between latex yield and antecedent environmental data was undertaken for five clones (RRII203, RRII118, RRIM600, RRII105 and GT1) of Hevea brasiliensis (rubber) in Agartala, northeast India, a region in which rubber is not traditionally cultivated. The explained variance for the regression equations based on parameters determined on the day of tapping and up to 3 days prior to it, varied from 72% to 37% during the NWT period and 94-83% during the WT period. Soil moisture storage, 1 and 3 days prior to tapping, was found to be the primary parameter affecting yield for the NWT and WT periods, respectively. It was observed that the clone RRII105, with a comparatively lower yield to that of RRIM600, was more susceptible to daily WD conditions during the non-winter season. RRIM600 and RRII105 being high-yielding clones were also found to be fairly dependent on the AT of the day prior to tapping. The mean lag period correlation of this parameter with yield was also found to be higher during the WT period than during the NWT period. As a whole, the mean lag period based on prior measurements of environmental variables showed optimum correlation with yield at 15-20 days prior to the day of tapping. The study also confirms that varied responses of yield with environmental factors in this non-traditional region of rubber cultivation depend on clonal character.

Winter respiratory viruses and health care use: a population-based study in the northwest United States.

To quantify health care use among adults during influenza and respiratory syncytial virus (RSV) seasons, we identified a cohort of veterans aged >or=18 years who used Department of Veterans Affairs (VA) facilities in Oregon and Washington states as their source of health care. During 1998-2000, veterans accrued 237,159 person-years of follow-up. Using VA data sources, we measured acute cardiopulmonary hospitalizations and primary care and urgent care visits. Differences between rates of study events when influenza and/or RSV were circulating and event rates when neither virus was circulating were used to calculate winter virus-attributable morbidity. Inpatient and outpatient event rates were consistently higher during winter virus season, compared with non-winter virus season. Annual rates of cardiopulmonary hospitalizations attributable to influenza or RSV infection ranged from 0.8 (95% confidence interval [CI], 0.1-1.5) per 1000 low-risk individuals aged 18-49 years, to 10.6 (95% CI, 7.5-13.6) per 1000 high-risk individuals aged >or=65 years. Each year, circulation of influenza and RSV coincide with predictable increases in medical care use.