Marathon running: physiological and chemical changes accompanying late-race functional deterioration

Twenty-one experienced runners were studied before, during and immediately after a marathon race to ascertain whether either depletion of energy substrate or rise in body temperature, or both, contribute to late-race slowing of running pace. Seven runners drank a glucose/electrolyte (GE) solution ad libitum (Na+ 21 mmol l-1, K+ 2.5 mmol l-1, Cl- 17 mmol l-1, PO4(2-) 6 mmol l-1, glucose 28 mmol l-1) throughout the race; 6 drank water and 8 drank the GE solution diluted 1:1 with water. Although average running speeds for the three groups were not significantly different during the first two-thirds (29 km) of the race, rectal temperature was significantly higher (P < 0.05) and reduction of plasma volume was greater (P < 0.05) in runners who replaced sweat losses with water. During the last one-third of the race, the average running pace of the water-replacement group slowed by 37.2%; the pace slowed by 27.9% in the 8 runners who replaced their sweat loss with GE diluted 1:1 with water (1/2 GE) and 18.2% in runners who replaced fluid loss with full-strength solution (GE). Eleven runners (5 in the water group, 4 in the 1/2 GE group and 2 in the GE group) lapsed into a walk/run/walk pace during the last 6 miles of the race. Ten of these had a rectal temperature of 39 degrees C or greater after 29 km of running, and plasma volume in these runners was reduced by more than 10%.(ABSTRACT TRUNCATED AT 250 WORDS)     

Perception of effort during high-intensity exercise at low, moderate and high wet bulb globe temperatures

The purpose of this study was to determine the effect of low, moderate and high wet bulb globe temperatures (T _wbg) on cardiovascular variables and ratings of perceived exertion (RPE) during moderately prolonged, high-intensity exercise. Six subjects [four men and two women; mean (SD) age, 22.0 (1.2) years; maximum oxygen consumption ({ie519-1}), 51.0 (8.4) ml · kg^–1 · min^–1] completed 30 min of exercise (80% {ie519-2}) on a cycle ergometer at low [14.7 (2.1)°C], moderate [21.0 (1.5)° C], and high [27.4 (2.3)° C]T _wbg. Two additional subjects completed 20 min of exercise in the high temperature condition, but completed 30 min in the moderate and lowT _wbg. Heart rate (f _c), blood pressure, blood lactate (La), mean skin temperature ( _sk), , and RPE were measured at 10, 20 and 30 min. Results showed thatf _c, rate pressure product, RPE, pulmonary ventilation and ventilatory equivalent for oxygen increased (P < 0.05) across time for all conditions, while decreased across time. _sk andf _c were significantly greater across time in the high condition [35.9 (0.65)° C; 176 (12.6) beats · min^–1] compared to the moderate [34.6 (1.5)° C; 170 (17.2) beats · min^–1] and the low condition [31.7 (1.5)° C; 164 (17.1) beats-min^–1]. However, there were no differences throughout exercise in RPE [high,.16.2 (2.0); moderate, 16.4 (2.2); low, 16.3 (1.9)] and across the conditions. These data suggest that RPE is closely related to metabolic intensity but is not a valid indicator of cardiovascular strain during exercise in highT _wbg conditions.     

Empirical modelling of lake water-temperature relationships: a comparison of approaches

1.  As a result of the role that temperature plays in many aquatic processes, good predictive models of annual maximum near-surface lake water temperature across large spatial scales are needed, particularly given concerns regarding climate change. Comparisons of suitable modelling approaches are required to determine their relative merit and suitability for providing good predictions of current conditions. We developed models predicting annual maximum near-surface lake water temperatures for lakes across Canada using four statistical approaches: multiple regression, regression tree, artificial neural networks and Bayesian multiple regression.
2.  Annual maximum near-surface (from 0 to 2 m) lake water-temperature data were obtained for more than 13 000 lakes and were matched to geographic, climatic, lake morphology, physical habitat and water chemistry data. We modelled 2348 lakes and three subsets thereof encompassing different spatial scales and predictor variables to identify the relative importance of these variables at predicting lake temperature.
3.  Although artificial neural networks were marginally better for three of the four data sets, multiple regression was considered to provide the best solution based on the combination of model performance and computational complexity. Climatic variables and date of sampling were the most important variables for predicting water temperature in our models.
4.  Lake morphology did not play a substantial role in predicting lake temperature across any of the spatial scales. Maximum near-surface temperatures for Canadian lakes appeared to be dominated by large-scale climatic and geographic patterns, rather than lake-specific variables, such as lake morphology and water chemistry.     

Climatic factors controlling interannual variability of the onset of vegetation phenology in the northern Sub-Saharan Africa from 1988 to 2013

Satellite-based evaluations of change in vegetation phenology have been explored extensively but land cover-specific climate factors driving these anomalous changes are not fully understood in northern Sub-Saharan Africa. In this study, we identified the climatic factors controlling the start of the season (SOS) extracted from GIMMS NDVI from 1988 to 2013 with onset of rainy season (ORS), annual mean temperature (Temp) and precipitation (PP) through the stepwise regression analysis. The results showed that the SOS shifted towards a late onset in a northward direction with distinct earlier and later trends in grassland and cropland, respectively. The stepwise regression has successfully built a model between SOS and its drivers in 46.0% of the total pixels, where its primary factor differed regionally across land covers. The ORS explained the local anomalous SOS change primarily at 44.7% of the pixels where the model was built. Although the ORS was the primary dominant factor in savannah and cropland, the Temp and PP were leading in grassland and shrubland, respectively, and all factors contributed evenly in evergreen forest. The difference of land cover-specific primary factor implicates complex process in dependency of local vegetation phenology on physiological traits and climate regime across land cover in Sub-Saharan Africa.     

生态公益林补偿政策对植被覆盖时空格局的影响——以杭州市临安区为例

生态补偿政策绩效评价对完善政策调控和促进植被恢复具有重要意义。分析了临安2001—2015年植被覆盖的时空格局和变化,建立了植被退化地区土地利用变化矩阵。在Arc GIS软件中建立了临安地区归一化植被指数(NDVI)、国家级和省级公益林覆盖边界(重点生态公益林补偿边界)、道路、海拔、中心城镇用地边界、重点森林保护区、河流边界和地形湿度指数的多要素地理空间数据集。采用多元回归模型量化了重点生态公益林补偿政策、自然因素、城市化和其他人类活动对植被覆盖变化的影响。结果表明,2001—2015年间,临安大约有97%的林地呈现出不同程度的退化,其中中等退化程度的林地所占比例最高。多元线性回归模型的结果表明重点生态公益林补偿政策、海拔、河流距离、重点森林保护区距离和中心城镇距离对植被退化有抑制作用,道路和地形湿度指数对植被退化有促进作用。海拔在所有的外部影响因素中对植被退化的影响最大。政策覆盖地区的植被退化面积在每一个退化等级上都小于无政策覆盖地区。生态公益林补偿政策的实施对加强植被的保护,限制不合理的林地利用和改善区域环境质量具有十分重要的积极作用。     

Predicting probability of occurrence and factors affecting distribution and abundance of three Ozark endemic crayfish species at multiple spatial scales

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温室番茄蒸腾量与其影响因子的相关分析及模型模拟

采用盆栽方法,研究不同灌溉量处理下温室番茄日蒸腾量与单株总叶面积、土壤相对含水量、空气温度、空气相对湿度、太阳辐射等因子的相关关系,并建立日蒸腾量的回归模型.结果表明:番茄日蒸腾量与单株总叶面积、土壤相对含水量、空气温度、空气相对湿度和太阳辐射等因子呈显著的线性关系,各因子之间存在复杂的相互作用;土壤水分状况是番茄蒸腾量的主要决策因子,决策系数为27.4％;日最低空气相对湿度是主要限制因子,决策系数为-119.7％;番茄日蒸腾量预测值和实测值的回归系数平方值(R2)为0.81,回归估计标准误差(RMSE)和相对误差(RE)分别为68.52 g和19.4％.根据通径分析筛选主要影响因子建立的番茄日蒸腾量回归模型能够较好地模拟温室番茄日蒸腾量.     

Effect of environmental,economic and health factors on CoVid-19 transmission

The severe acute respiratory syndrome (SARS) is affected by meteorological parameters such as temperature and humidity. It is also observed that people having asthma are at risk for SARS. Therefore, it is of interest to report the effect of environmental, economic, and health factors on the spread of CoVid-19. We used data reporting CoVid-19 cases from 24 cities in eight different countries for this analysis. Data was analyzed using multiple linear regressions between these parameters. Data shows that temperature has effects on CoVid-19. A one-degree rise in temperature causes a -0.19 decrease in CoVid-19 cases per million people (log natural value per million populations). The effect of humidity is not significant at a p value of 0.26. Moreover, one-unit increase in asthma and GDP cases per million people show 0.06 and 0.46 increases in CoVid-19 cases, respectively.     

温室番茄蒸腾量与其影响因子的相关分析及模型模拟

采用盆栽方法,研究不同灌溉量处理下温室番茄日蒸腾量与单株总叶面积、土壤相对含水量、空气温度、空气相对湿度、太阳辐射等因子的相关关系,并建立日蒸腾量的回归模型.结果表明：番茄日蒸腾量与单株总叶面积、土壤相对含水量、空气温度、空气相对湿度和太阳辐射等因子呈显著的线性关系,各因子之间存在复杂的相互作用;土壤水分状况是番茄蒸腾量的主要决策因子,决策系数为27.4%;日最低空气相对湿度是主要限制因子,决策系数为-119.7%;番茄日蒸腾量预测值和实测值的回归系数平方值(R²)为0.81,回归估计标准误差(RMSE)和相对误差(RE)分别为68.52 g和19.4%.根据通径分析筛选主要影响因子建立的番茄日蒸腾量回归模型能够较好地模拟温室番茄日蒸腾量.     

New method for estimation of adult skeletal age at death from the morphology of the auricular surface of the ilium

A new method for estimating skeletal age at death from the morphology of the auricular surface of the ilium is presented. It uses a multiple regression analysis with dummy variables, and is based on the examination of 700 modern Japanese skeletal remains with age records. The observer using this method needs only to check for the presence or absence of nine (for a male) or seven (for a female) features on the auricular surface and to select the parameter estimates of each feature, calculated by multiple regression analysis with dummy variables. The observer can obtain an estimated age from the sum of parameter estimates. It is shown that a fine granular texture of the auricular surface is typical of younger individuals, whereas a heavily porous texture is characteristic of older individuals, and that both of these features are very useful for estimating age. Our method is shown here to be more accurate than other methods, especially in the older age ranges. Since the auricular surface allows more expedient observations than other parts of the skeleton, this new method can be expected to improve the overall accuracy of estimating skeletal age at death.     

Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions

Aim

Assessing the influence of land cover in species distribution modelling is limited by the availability of fine‐resolution land‐cover data appropriate for most species responses. Remote‐sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote‐sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon.

Location

Central Coast Range, Cascade and Klamath Mountains Oregon, USA.

Methods

Information on bird observations was collected from 4598 fixed‐radius point counts. Reflectance data were obtained using 30‐m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree (BRT) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve (AUC) values.

Results

Prediction success of models using all reflectance values was high (mean AUC = 0.79 ± 0.10 SD). Further, model performance using individual reflectance bands exceeded those that used only Normalized Difference Vegetation Index (NDVI). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest.

Main Conclusions

We demonstrate that unclassified remote‐sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high‐resolution predictions are required, including habitat change detection studies, identification of fine‐scale biodiversity hotspots and reserve design.

     

Can tissue element concentration patterns at the individual species level indicate the factors underlying vegetation gradients in wetlands?

Question: Do tissue element concentrations at the individual species level vary along major vegetation gradients in wetlands, and can they indicate environmental conditions? Location: West Carpathians. Methods: Total plant species composition was recorded in plots distributed along a poor to rich gradient within spring fens and along the gradient from fens to wet meadows. Eriophorum angustifolium (Cyperaceae) and three broadleaf dicotyledonous herb species were collected from the vegetation plots. Tissue N, P, K, Ca and Fe concentrations, N:P and N:K ratios of the species were determined. Each variable was correlated with the sample scores along the first two axes of the DCA ordination, which represented the two main vegetation gradients. Results: K and Ca concentrations in a particular species correlated well with the vegetation gradients, thus indicating changes in the element availability to the species. The trends were sometimes contradictory to known patterns at the community level, but the differences could be ecologically interpreted. Contrary to Ca and K, patterns in N, P and Fe concentration appeared to be more species‐specific. E. angustifolium had a lower K and Ca concentration than the broadleaf herbs. Conclusions: Compared to community‐level measurements, element concentrations in individual species correlated less with observed vegetation gradients. Trends found at the species level may indicate changes in ecological conditions affecting the species, although they need not correspond with trends found at the community level. We conclude that the species‐level approach cannot substitute, but can advance, the community‐level approach in searching for mechanisms underlying vegetation gradients within wetlands.     

不同植被防护措施对三峡库区土质道路边坡侵蚀的影响

为了探明沙湖浮游动物密度、生物量、分布与水环境因子之间的关系,运用多元逐步回归分析、通径分析和典范对应分析方法对2009年4月、7月、10月、2010年1月测定的沙湖浮游动物密度、生物量数据与水环境因子进行多元分析。结果表明:沙湖浮游动物密度与叶绿素a含量、总氮、总磷之间呈显著线性相关,影响沙湖浮游动物密度的主要水环境因子依次为叶绿素a含量、总氮、总磷;浮游动物生物量与叶绿素a含量、总氮、透明度、化学需氧量(CODMn)之间呈显著线性相关,影响浮游动物生物量的主要水环境因子依次为叶绿素a含量、总氮、透明度、化学需氧量;叶绿素a含量和总氮对浮游动物的直接影响作用最强,其他水环境因子主要通过影响叶绿素a含量间接影响浮游动物密度及生物量。浮游动物与水环境因子的CCA排序结果将分别适应不同水环境的16种浮游动物分为3组,叶绿素a含量、化学需氧量、水温和总磷是影响沙湖浮游动物群落特征及时空分布的主要水环境因子。     

Estimating the body mass of extinct ungulates: a study on the use of multiple regression

The correlation between body mass and both skeletal and dental measures in living mammals has enabled paleontologists to obtain reliable estimates of body size for extinct species, usually using log-transformed bivariate least-squares regression equations. Multiple regression, however, has rarely been used for estimating the mass of extinct species, although this technique can clearly improve the predictive equations compared with those adjusted by simple regression. However, the use of multiple regression is problematical, because even those functions explaining a high percentage of the variance of the dependent variable (i.e. body mass) can show a rather limited predictive power. After analyzing which factors determine the predictive ability of multiple regression equations, we propose a new set of algorithms that allow the estimation of the body mass of extinct ungulates. These algorithms are finally applied to three Miocene ungulate species, Dinohippus leidyanus , Stenomylus hitchcocki and Aletomeryx scotti .     

Contribution of dissolved calcium and magnesium to phytoplanktonic particulate phosphorus concentration at the heads of two river reservoirs

At the heads of two river reservoirs, the Ishitegawa Dam and the Nomura Dam Reservoirs in Japan, the concentrations of phytoplanktonic particulate phosphorus (PP) were compared with those of dissolved calcium (Ca) and magnesium (Mg), using multiple regression analyses on the data taken from samples which registered more than 6.0 µg l^–1 in chlorophyll a concentration. Of the 27 monthly samples, 16 from the Ishitegawa Reservoir and 17 from the Nomura Reservoir were used. A significant regression line, logPP = k ₁F + k ₂, was obtained, where k ₁ (> 0) and k ₂ were constants and F (named the Ca-Mg index) consisted of log(Ca/Mg) – 0.5 log(ca + Mg) in mol concentration in Ca and Mg, in common with the two reservoirs (r ² = 0.730 & 0.913).     

基于GWR模型的土地覆盖与地表温度的关系——以京津唐城市群为例

基于Landsat TM土地覆盖分类数据和MODIS地表温度数据,探讨京津唐城市群不同土地覆盖的地表温度(7日),并采用常用的普通线性回归(OLS)和地理加权回归(GWR)方法分别拟合土地覆盖比例与地表温度的关系.结果表明: 研究区不同土地覆盖类型的地表温度差异明显,人工表面(40.92±3.49 ℃)和耕地(39.74±3.74 ℃)的平均温度较高,林地(34.43±4.16 ℃)和湿地(35.42±4.33 ℃)的平均温度较低;土地覆盖比例与地表温度显著相关,且两者之间的定量关系存在空间非稳定性,地理位置以及周围环境影响的差异是空间非稳定性产生的主要原因;GWR模型的拟合结果优于OLS模型(R_GWR²>R_OLS²),并且GWR模型可以量化土地覆盖比例与地表温度两者关系的空间非稳定性特征.     

Determinants of the dramatic seasonal changes in the intake of energy and protein by Japanese monkeys in a cool temperate forest

Daily intake of gross energy (DGEI) and protein (DGPI) of Japanese monkeys in a cool temperature forest were investigated in different four seasons. As a result, dramatic seasonal differences in DGEI and DGPI were found: DGEI and DGPI in winter were significantly lower than those in spring and autumn, being only about 17–42% of those in the two seasons. DGEI and DGPI in summer were also lower than those in autumn and spring, being only about 29–52% of those in the two latter seasons. Seasonal comparisons of some variables on food qualities revealed that much lower values of DGEI and DGPI in winter and summer were influenced srongly not so much by the lower calorie and protein content of food as by the lower speed of dry weight intake. Multiple regression analyses for each food item revealed that the unit weight was a more important factor in determining the speed of dry weight intake than was the speed of unit intake. In addition, the speed of dry weight intake contributed much more to the speed of calorie and protein intake than did the content of calorie and protein. Multiple regression analyses, employing DGEI and DGPI (as dependent variables) and daily mean values of some variables related to food quality (as independent variables), revealed that the mean values of speed of calorie and protein intake on food items eaten in a day could explain 79.0% and 85.9% of the variance in DGEI and DGPI, respectively. In addition, the mean value of the speed of dry weight intake of food items eaten in a day can be used to explain 74.3% and 52.8% of the variance in DGEI and DGPI, respectively. These findings suggested that not only nutritional content of food but also the speed of dry weight intake strongly determined the nutritional condition of nonhuman primates. Am. J. Primatol. 41:267–288, 1997. © 1997 Wiley-Liss, Inc.     

Focus: Personalized Medicine: The Sociology of the Deceased Harvard Medical Unit at Boston City Hospital

Many graduates of the Harvard Medical Unit (HMU) at Boston City Hospital, in either the clinical training/residency program or the research program at the Thorndike Memorial Laboratory, contributed in major ways to the HMU and constantly relived their HMU experiences. The HMU staff physicians, descending from founder and mentor physicians Francis W. Peabody, Soma Weiss, and George R. Minot, were dedicated to the teaching, development, and leadership of its clinical and research trainees, whose confidence and dedication to patient care as a result of their mentorship led many to lifelong achievements as clinicians, teachers, and mentors. Their experience also led to a lifelong love of the HMU (despite its loss), camaraderie, happiness, and intense friendships with their associates.     

Avoiding fallacious significance tests in stepwise regression: a Monte Carlo method applied to a meteorological theory for the Canadian lynx cycle

Stepwise regression is often used in ecology to identify critical factors. From a large number of possible predictors, the procedure selects the subset generating the highest coefficient of determination,R ². This work presents a method for testing the significance of this coefficient. Monte Carlo simulations are used to calculate the statistical distribution ofR ² under the null hypothesis that the response variable is independent of the predictors. The method is illustrated by an application to a previously published analysis of the Canadian lynx population cycle where more than 75% of the variance could be explained by four meteorological factors.     

Investigating the relationship between environmental factors and tick abundance in a small,highly heterogeneous region

The tick Ixodes ricinus (L.) is the most important vector of tick‐borne zoonoses in Europe. Apart from factors related to human behavior, tick abundance is a major driver of the incidence of tick‐borne diseases in a given area and related data represent critical information for promoting effective public health policies. The present study analyzed the relationship between different environmental factors and tick abundance in order to improve the understanding of I. ricinus autecology and develop spatial predictive models that can be implemented in tick‐borne disease prevention strategies. Ticks were sampled in 27 sites over a four‐year period and different environmental variables were studied. Five simple models were developed that explain a large part of variation in tick abundance. Precipitation seems to play the most important role, followed by temperature, woodland coverage, and solar radiation. Model equations obtained in this study may enable the spatial interpolation and extension of tick abundance predicted values to sites of the same area, in order to build regional predictive maps. They could also be useful for the validation of large‐scale spatial predictive maps.