中国外来入侵物种的分布与传入路径分析

外来物种入侵已成为全球性的环境问题,本文采用文献调研,实地考察与专家咨询相结合的方式,调查了全国陆生,淡水水生生态系统中外来入侵微生物,无脊椎动物,两栖爬行类,鱼类,鸟类,哺乳类,杂草,树木和海洋生态系统中外来入侵物种的种类及分类地位,起源,引入路径和环境影响等内容。查明我国共有283种外来入侵物种,其中外来入侵微生物,水生植物,陆生植物,水生无脊椎动物,陆生无脊椎动物,两栖爬行类,鱼类,哺乳类分别为19种,18种,170种,25种,33种,3种,10种和5种,来源于美洲,欧洲,亚洲,非洲,大洋洲的外来入侵物种分别占55.1%,21.7%,9.9%.8.1%和0.6%。我国对外来物种的引进存在一定程度的盲目性;50.%的外来入侵植物是作为牧草或饲料,观赏植物,纤维植物,药用植物,蔬菜,草坪模特而引进的;25%的外来入侵动物是用于养殖,观赏,生物防治的引种,对外来物种只重引进,疏于管理,也可能导致外来物种逃逸到自然环境中,造成潜在的环境意传入的;76.3%的外来入侵动物是由于检查不严,随贸易物品或运输工具传入我国的。因此,我国既要加强检疫工作,又要对外来物种的有意引进进行严格管理,实行外来物种引进的风险评估制度。     

四川唐家河羚牛、鬣羚、斑羚春冬季生境选择比较研究

1998年3月－1999年2月,在四川省青川县唐家河自然保护区,运用逐步判别分析的方法,对羚牛,鬣羚,斑羚春冬季对生境的利用进行了对比研究,研究结果表明,虽然它们在生境选择上有部分重叠,但是其对生境的利用方式均有显著的差异,在春季,羚牛主要利用海拔较高,食物丰富度高,灌木较大,离灌木较远的生境,鬣羚和斑羚主要利用海拔较低,食物丰富度中等,灌木较小,离灌木较近的生境,在冬季,羚牛主要利用坡度适中,海拔适中,乔木较大,乔木较稀,离乔木较远的生境,鬣羚主要利用坡度较陡,海拔较高,乔木较小,乔木较稀,离乔木适蝇的生境;斑羚主要利用坡度较缓,海拔较低,乔木较小,乔木较密,离乔木较近的生境。     

粘细菌生态多样性的初步研究

通过对河北,云南,青藏高原不同地区的粘细菌进行生态多样的研究,从42个样品中分离得到了10个属（Archangium,Myxococcus,Cystobacter,Corallococcus,Melittangium,Sorangium,Polyangium,Chondromyces,Angiococccus,Stigmatella)的150余株粘细菌,其中包括一些尚未有描述的菌株,有待鉴定,根据这些菌株的子实体结构,菌落形态,营养细胞,粘孢子形态等特征将它们初步鉴定到属,对这些特有自然生态环境的粘细菌按不同地点,植被,营养基质进行统计比较,结果表明,粘细菌生态分布极为广泛,具有丰富的生态多样性,以上结果为粘细菌生物资源的有效开发利用奠定了基础。     

生物教学中幻灯片的绘制

我们正跨入一个教育大跃进的时代,今后要发展普通中学,各级师范学校,大办农业中学,及各种专门的农校,为蚕桑中学,林业中学,渔业中学,茶业中学,果树中学,工生学校,体育学校……等,这对我们生物教师来讲,怎样进一步来改进生物教育学去适应即将到来的文化革命是一个迫切需要的问题。有关生物各科的教学是需要多种多样的形式和教具的,但这里电化教育是很重要的,可是其中如厂播,电影在国内现有的条件下受器材和电源的限制,尚难推行,而幻灯价格较便宜,也易自已制造,有许多地方自已制造出“土幻灯”,器材简单,而且用汽灯代替电灯,不受电源的限制,现在上海电影机械厂制造的5503式幻灯机,即可用汽灯放映,价格仅二十三元,各地均可函购,这是农村或边区中学也可以办到的。可是现     

磁疗法治疗颈椎病的应用进展

颈椎病是一种常见疾病,尤其中老年人的发病率较高,是一种骨质的退行性变化,颈椎骨质增生,形成骨赘或骨刺,颈椎的椎间隙缩小变窄,椎间孔变小,压迫神经根,如颈后部疼痛,上肢发生放射性疼痛,麻木,增生的骨赘或骨刺,压迫与刺激其周围软组织,引起软组织发生渗出与肿胀,又加重其压迫症状,增加患的疾病痛苦,目前对颈椎病的治疗,尚无特殊效果的治疗方法。     

TNF-α 在肿瘤中的作用

肿瘤坏死因子是一个特别的,并具有多重功能的细胞因子,它在免疫调节,炎症反应,机体防御当中起着关键的作用。根据不同的细胞微环境,肿瘤坏死因子可以诱导多种反应,例如凋亡,坏死,血管生成,免疫细胞激活,细胞分化,细胞迁移。TNF在肿瘤当中是一把双刃剑。一方面,TNF是一个内源性的肿瘤促进因素,因为TNF可以刺激肿瘤细胞生长,增殖,侵袭,转移,血管生成。另一方面,TNF具有杀肿瘤细胞的作用。因此,如果可以调控肿瘤坏死因子的功能,将为癌症的治疗提供可能。     

有机酸对Pd 、Cd的土壤化学行为和植株效应的影响

有机酸对Pd,Cd的络合作用将其对土壤吸附Pd,Cd的影响和植株效应差异的研究表明,柠檬酸,草酸与Pd,Cd络合能力的大小与重金属本身的性质有关,络合作用影响土壤对Pd,Cd的吸附量,柠檬酸降低土壤对Pd,Cd的吸附,草酸则增加土壤对Pd,Cd的吸附,水培试验表明,柠檬酸可减轻Pb对小麦,水稻幼苗的毒害,柠檬酸对Cd的植株外观毒性效应影响不显著,但能促使植株茎叶,根中Cd含量下降,Pd和Cd复合处理条件下,Cd存在促使水稻植株对Pd吸收量增加,Pd存在抑制水稻植株对Cd的吸收。     

宝天曼人

中原大地,藏龙卧虎,南阳古城,英才辈出。从博学的张衡,到睿智的诸葛先生,从妙手回春的张仲景,到豪迈大气的岑参,无数英雄才俊,代代墨客骚人,指点历史苍穹,吟罢江山如画,在南阳这片热土上,尽放炫目光彩,“江山代有才人出,各领风骚数百年”。而今,不再是英雄,不再是豪杰,有的只是普通的劳动者,洒着艰辛的汗水,怀着对未来的希望,在平凡中耕耘,秀美青山,快乐人生正是从这汗水和辛劳中孕育出来的。真可谓“水钟灵秀,山毓雄浑,南阳具荆秦特色;民尚忠诚,士崇道义,北楚存汉魏遗风”。     

松中美男子——刚松

你仔细品味过庞大松科家族中的各类松树的风采吗?华山松的清秀,黑松的苍劲,火炬松的飘逸,赤松的粗旷,雪松的伟岸挺拔,红松的壮志凌云,从南到北,可谓形态各异,各有千秋。然而,有一种松树却有另一种美,树体高大却透着清秀,枝叶中露着一种坚毅和不屈的男子汉品格,这     

中国汉族女性长骨碎片的身高推断

本文作者通过对69副有生前确切资料的中国汉族女性四肢长骨的研究,在女性四肢长骨推断身高的基础上,参考国内外学者的研究方法,选择左右侧肱骨,尺骨,桡骨,股骨,胫骨,腓骨的局部测量项目,其中肱骨8项,尺骨3项,桡骨3项,股骨14项,胫骨8项,腓骨3项,用SPSS软件包对测量项目进行统计分析,建立了中国汉族女性破碎长骨推断身高的回归方程。本文所建方程可以用于中国汉族女性破碎长骨的身高推断,其中肱骨,股骨,胫骨碎片推断身高的效果较好。     

Quasi Likelihood/Moment Method for Generalized and Restricted Generalized Poisson Regression Models and Its Application

This paper reviews the generalized Poisson regression model, the restricted generalized Poisson regression model and the mixed Poisson regression (negative binomial regression and Poisson inverse Gaussian regression) models which can be used for regression analysis of counts. The aim of this study is to demonstrate the quasi likelihood/moment method, which is used for estimation of the parameters of mixed Poisson regression models, also applicable to obtain the estimates of the parameters of the generalized Poisson regression and the restricted generalized Poisson regression models. Besides, at the end of this study an application related to this method for zoological data is given.     

Fitting curves to data using nonlinear regression: a practical and nonmathematical review

Many types of data are best analyzed by fitting a curve using nonlinear regression, and computer programs that perform these calculations are readily available. Like every scientific technique, however, a nonlinear regression program can produce misleading results when used inappropriately. This article reviews the use of nonlinear regression in a practical and nonmathematical manner to answer the following questions: Why is nonlinear regression superior to linear regression of transformed data? How does nonlinear regression differ from polynomial regression and cubic spline? How do nonlinear regression programs work? What choices must an investigator make before performing nonlinear regression? What do the final results mean? How can two sets of data or two fits to one set of data be compared? What problems can cause the results to be wrong? This review is designed to demystify nonlinear regression so that both its power and its limitations will be appreciated.     

Embracing asymmetry in nature: How to account for skewness in ecological data

Asymmetric regression is an alternative to conventional linear regression that allows us to model the relationship between predictor variables and the response variable while accommodating skewness. Advantages of asymmetric regression include incorporating realistic ecological patterns observed in data, robustness to model misspecification and less sensitivity to outliers. Bayesian asymmetric regression relies on asymmetric distributions such as the asymmetric Laplace (ALD) or asymmetric normal (AND) in place of the normal distribution used in classic linear regression models. Asymmetric regression concepts can be used for process and parameter components of hierarchical Bayesian models and have a wide range of applications in data analyses. In particular, asymmetric regression allows us to fit more realistic statistical models to skewed data and pairs well with Bayesian inference. We first describe asymmetric regression using the ALD and AND. Second, we show how the ALD and AND can be used for Bayesian quantile and expectile regression for continuous response data. Third, we consider an extension to generalize Bayesian asymmetric regression to survey data consisting of counts of objects. Fourth, we describe a regression model using the ALD, and show that it can be applied to add needed flexibility, resulting in better predictive models compared to Poisson or negative binomial regression. We demonstrate concepts by analyzing a data set consisting of counts of Henslow’s sparrows following prescribed fire and provide annotated computer code to facilitate implementation. Our results suggest Bayesian asymmetric regression is an essential component of a scientist’s statistical toolbox.     

A new method for dealing with measurement error in explanatory variables of regression models

We introduce a new method, moment reconstruction, of correcting for measurement error in covariates in regression models. The central idea is similar to regression calibration in that the values of the covariates that are measured with error are replaced by "adjusted" values. In regression calibration the adjusted value is the expectation of the true value conditional on the measured value. In moment reconstruction the adjusted value is the variance-preserving empirical Bayes estimate of the true value conditional on the outcome variable. The adjusted values thereby have the same first two moments and the same covariance with the outcome variable as the unobserved "true" covariate values. We show that moment reconstruction is equivalent to regression calibration in the case of linear regression, but leads to different results for logistic regression. For case-control studies with logistic regression and covariates that are normally distributed within cases and controls, we show that the resulting estimates of the regression coefficients are consistent. In simulations we demonstrate that for logistic regression, moment reconstruction carries less bias than regression calibration, and for case-control studies is superior in mean-square error to the standard regression calibration approach. Finally, we give an example of the use of moment reconstruction in linear discriminant analysis and a nonstandard problem where we wish to adjust a classification tree for measurement error in the explanatory variables.     

Complicated relationship between autism with regression and epilepsy

We retrospectively evaluated a set of 205 children with autism and compared it to the partial sub-set of 71 (34.6%) children with a history of regression. From 71 children with regression, signs of epileptic processes were present in 43 (60.6%), 28 (65.12%) suffered clinical epileptic seizures, and 15 (34.9%) just had an epileptiform abnormality on the EEG. In our analysis, autistic regression is substantially more associated with epileptic process symptoms than in children with autism and no history of regression. More than 90% of children with a history of regression also show IQ < 70 and reduced functionality. Functionality and IQ further worsens with the occurrence of epileptic seizures (98% of children with regression and epilepsy have IQ < 70). We proved that low IQ and reduced functionality significantly correlate rather with epileptic seizures than just sub-clinical epileptiform abnormality on EEG. Clinical epileptic seizures associated with regression significantly influence the age of regression and its clinical type. The age of regression is higher compared to children with regression without epileptic seizures (in median: 35 months of age in patients with seizures while only 24 months in other patients). Patients with seizures revealed regression after 24th months of age in 68% of cases, while patients without seizures only in 27%. However, coincidence with epilepsy also increased the occurrence of regression before the 18th month of age (23% of patients), while only 4% of patients without epilepsy revealed regression before the 18th month. Epileptic seizures are significantly associated especially with behaviour regression rather than speech regression or regression in both behaviour and speech. Also epileptic seizures diagnosed before correct diagnosis of autism were significantly associated with delayed regression (both behavioural and speech regression).     

Analytical methods matter too: Establishing a framework for estimating maximum metabolic rate for fishes

Advances in experimental design and equipment have simplified the collection of maximum metabolic rate (MMR) data for a more diverse array of water‐breathing animals. However, little attention has been given to the consequences of analytical choices in the estimation of MMR. Using different analytical methods can reduce the comparability of MMR estimates across species and studies and has consequences for the burgeoning number of macroecological meta‐analyses using metabolic rate data. Two key analytical choices that require standardization are the time interval, or regression window width, over which MMR is estimated, and the method used to locate that regression window within the raw oxygen depletion trace. Here, we consider the effect of both choices by estimating MMR for two shark and two salmonid species of different activity levels using multiple regression window widths and three analytical methods: rolling regression, sequential regression, and segmented regression. Shorter regression windows yielded higher metabolic rate estimates, with a risk that the shortest windows (<1‐min) reflect more system noise than MMR signal. Rolling regression was the best candidate model and produced the highest MMR estimates. Sequential regression models consistently produced lower relative estimates than rolling regression models, while the segmented regression model was unable to produce consistent MMR estimates across individuals. The time‐point of the MMR regression window along the oxygen consumption trace varied considerably across individuals but not across models. We show that choice of analytical method, in addition to more widely understood experimental choices, profoundly affect the resultant estimates of MMR. We recommend that researchers (1) employ a rolling regression model with a reliable regression window tailored to their experimental system and (2) explicitly report their analytical methods, including publishing raw data and code.     

Photoperiodic control of the termination of breeding and the induction ofmoult in House Sparrows Passer domesticus

Two photoperiodic mechanisms controlling gonadal regression in birds have been identified: absolute photorefractoriness, typical of species with short breeding seasons, where gonadal regression occurs spontaneously during long days, and relative photorefractoriness, where a decrease in daylength is required to induce regression. An experiment was designed to test whether these simply represent extremes of one underlying mechanism. Three groups of male House Sparrows Passer domesticus were transferred from a short photoperiod, 8 h of light: 16 h of darkness per day (8L:16D) to long photoperiods of either 18L:6D, 16L:8D or 13L:11D. Gonadal maturation rates were similar in all three groups; gonadal regression and moult began latest in the 13L:11D group. Four additional groups of sparrows were transferred from 8L:16D to 18L:6D and then transferred to either 13L: 11D or 16L:8D prior to, or shortly after, the onset of gonadal regression. The decrease in daylength prior to regression had no effect on the timing of regression but did advance the onset of moult. Decrease in daylength after the onset of regression increased the rate of regression and the rate of moult. Because a decrease in daylength did not affect the timing of regression, the data do not support the hypothesis that absolute and relative photorefractoriness represent extremes of a single underlying photoperiodic control mechanism. The adaptive significance of the effects of decreasing daylength on the rate of regression and moult is discussed.     

Climate variations and salmonellosis transmission in Adelaide,South Australia: a comparison between regression models

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990–2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.     

Non-linear heteroscedastic regression model for determination of methotrexate in human plasma by high-performance liquid chromatography

Generalized least squares regression with variance function estimation was used to derive the calibration function for measurement of methotrexate plasma concentration and its results were compared with weighted least squares regression by usual weight factors and also with that of ordinary least squares method. In the calibration curve range of 0.05 to 100 microM, both heteroscedasticity and non-linearity were present therefore ordinary least squares linear regression methods could result in large errors in the calculation of methotrexate concentration. Generalized least squares regression with variance function estimation worked better than both the weighted regression with the usual weight factors and ordinary least squares regression and gave better estimates for methotrexate concentration.     

Regression of papillomas induced by cottontail rabbit papillomavirus is associated with infiltration of CD8+ cells and persistence of viral DNA after regression.

Cottontail rabbit papillomavirus (CRPV) is a highly oncogenic papillomavirus and has been successfully used as a model to develop protective vaccines against papillomaviruses. Papillomas induced by the virus may spontaneously regress, suggesting that CRPV can also serve as a model to develop therapeutic vaccines. As a first step toward this goal, we have analyzed immunologic and viral aspects associated with papilloma regression and have identified several features unique to regression. Immunohistochemical staining of biopsies from growing and regressing papillomas and from sites after complete regression showed infiltration of CD8+ cells into the basal and suprabasal layers of the epidermis only during active regression. In situ hybridizations with mRNA-specific probes were strongly positive for E6 and E7 mRNAs during regression, but no late mRNA was present. Viral DNA was detected by in situ hybridization during regression but not after regression. However, analysis by PCR revealed persistence of viral DNA for several months at the majority of regression sites. The results suggest that stimulation of a strong CD8+ response to virus-infected cells is important for an effective therapeutic vaccine and that special attention should be given to the suppression of latent infection.