The successful quest for oral factor Xa inhibitors; learnings for all of medicinal chemistry?

The medicinal chemistry of oral small molecule factor Xa inhibitors is discussed, highlighting key advances that led to clinical candidates and the first licensed medicines. Identification of neutral ligands for the primary specificity pocket was a key discovery; capitalised upon by structure based design and combinatorial methods to deliver many variations on the theme; but it was good medicinal chemistry practice, in the optimisation of physical properties, which ultimately delivered efficacious compounds with adequate oral exposure. As a retrospective appraisal, representative compounds were profiled using the more contemporary concepts of Ligand Efficiency and Property Forecast Indices; which gave clear indications of the value of these principles.     

二点委夜蛾卵巢发育分级及在预测预报中的应用

对室内饲养的二点委夜蛾Athelis lepigone(Moschler)雌成虫分时段进行解剖,观察其卵巢的结构及发育进程。结果显示二点委夜蛾具有1对卵巢,各由4个卵巢小管组成。发育进程可分为5个阶段:透明期、卵黄沉积期、成熟待产期、产卵盛期和产卵末期。河北省石家庄地区2011年7月下旬至8月上旬田间卵巢发育的监测结果显示,Ⅰ级卵巢在整个发生期所占比例较高,Ⅳ级卵巢所占比例较低,推测其下一代幼虫的发生量将较低,并与田间调查结果吻合,因此卵巢解剖分级法可以用于二点委夜蛾的预测预报工作。     

广州地区冬季鱼塘水温特征及其预测预报

冬季水温对耐寒性较差的热带、亚热带鱼类的安全越冬有重要影响。本文根据鱼塘水温和附近气象站气温观测资料 ,研究了广州地区冬季鱼塘水温特征及其预测模型。结果表明 ,与气温相比 ,鱼塘水温日振幅较小 ,日最高温度出现时间滞后 ,这种特征在逐时温度和日平均温度上表现得都非常明显 ;晴天条件下水温的日变化远大于阴天 ;随水层深度的增加 ,水温日振幅逐渐减小 ,位相滞后也愈加明显。水温与气温的交叉相关分析表明 ,水温与当日、前一日、前二日、前三日的气温有显著的相关关系 ,与前四日、前五日的关系已不明显。利用逐步回归分析 ,建立了以气温为自变量的各水层水温预报模型。各模型的回归效果都达到了显著水平 ,平均绝对误差均在 0 5℃以内 ,平均相对误差均在 1 0 %以内 ,具有较高的精度 ,可用于冬季鱼塘水温的预报。     

A new approach to consider the pollen variable in forecasting yield models

Methods for forecasting harvest yields have been improved considerably in the last 20 years with the development of new data survey (remote sensing) and statistical techniques. One of these methods, based on pollen release in the atmosphere, is especially important for anemophilous species such as olive. The aim of the present work is to use a different approach to forecast the olive harvest by considering the pollen variable as “endogenous” because it is involved in the consequential processes from the formation of pollen to fruiting, the complex of which determines, more or less, the final production. Unlike models built upon a single equation (multiple linear regression analysis), the proposed estimate, based on an incomplete system of equations, recovers the consistency associated with the inference of parameters while avoiding the errors of “over-estimation.” The study, based on 17 years of data considers the quantity of olive pollen monitored and the relative annual olive production in addition to climatic, agronomic, and pathological variables associated with production. The harvest forecast provides the possibility for planning and optimizing the various stages of olive production from cultivation to distribution, including sound management of the olive supply.

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Une nouvelle approche pour considérer la variable pollen dans les modèles de prévision des rendement de récolte

Résumé  Pendant les vingt dernières années les méthodes de prévision des rendements de récolte ont été considérablement améliorées grace au développement de nouvelles techniques statistiques et d’ enquête des données (télédétection). Parmi ces méthodes celle basée sur l’émission du pollen dans l’atmosphère se révèle particulièrement importante pour les espèces anémophiles comme l’olivier. Le but de ce travail est celui d’arriver à employer une méthode différente dans la prévision des rendements de récolte de l’olivier, le tout en considérant comme “endogène” la variable pollen. Ce dernier est, en effect, impliqué dans les processus d’évolution qui vont de sa formation à la fructification, de manière à déterminer la production finale. Contrairement aux modèles établis sur une équation simple (analyse multiple de régression linéaire), l’évaluation proposée, basée sur un système inachevé d’équations, récupère la consistance connexe à l’ inférence des paramètres tout en évitant les erreurs de “surestimation”. L’étude, basée sur dix-sept ans de données, considère la quantité de pollen d’olivier détectée et conséquemment la production oléicole annuelle, outre aux variables climatiques, agronomiques et pathologiques liées à la production. Les modèles de prévision offrent la possibilité de rationaliser les différentes phases de la filière oléicole en optimisant les procédés, de la production à la distribution, y compris la gestion rationnelle des stocks.

     

枣实蝇蛹发育起点温度和有效积温

室内设定21,24,27,30和33℃共5个温度梯度,分别测定不同恒温条件下枣实蝇越冬蛹的发育历期,利用最小二乘法计算出蛹的发育起点温度为13.63℃,有效积温为807.55日.度。根据吐鲁番20年(1988～2007)月均地表温度数据,使用小样本方法估算出月均地表温度的置信区间,利用有效积温法则,推算得出2008年枣实蝇越冬代成虫初次出现日期范围为5月13日至22日,与2008年野外实测越冬成虫初次出现日期5月15日接近,测报结果较准确。     

黄杨绒蚧的生物学特性及卵期预测预报

黄杨绒蚧Eriococcus abeliceae Kuwana是危害园林树木的重要害虫之一。该蚧在西宁地区1年发生1代,以2龄若虫在树干裂皮缝中越冬。翌年3月下旬2龄若虫蜕皮变为3龄若虫,开始为害,雌虫5月上旬开始产卵,卵期18~23d,每雌虫产卵量134~424粒,平均287粒。6月上旬卵开始孵化,孵化率100%。雌虫3龄,雄虫2龄。在室内自然变温条件下,对黄杨绒蚧卵的发育起点温度和有效积温进行测定。结果表明:卵发育起点温度12.98℃,有效积温39.03日.度;预测式为N=(39.03±2.66)/T-(12.98±0.18)。     

比较逻辑斯蒂与地理加权逻辑斯蒂回归模型在福建林火发生的适用性

林火预测预报是科学有效进行林火管理的前提,是林业管理部门和科研工作者的广泛关注的领域。逻辑斯蒂回归(Logistic Regression,LR)是目前国内外广泛应用于森林火灾预测的模型方法,然而近年来有学者发现该方法没有充分考虑林火影响因子的空间相关性和异质性,从而导致模型拟合结果偏差。地理加权逻辑斯蒂回归(Geographically weighted logistic regression,GWR)模型考虑到了模型变量之间的空间相关性,有效提高的模型的拟合能力。为探讨GWLR模型在福建林火预测上的适用性,本研究应用LR和GWLR两种方法分别建立福建省森林火灾与气象因子的预测模型,通过模型拟合能力对比,判断在GWLR的适用性。研究以2000—2005年福建地区森林火灾卫星火点数据和每日气象因子为基础,将全样本分为60%的建模数据和40%的校验数据,并重复5次,建立5个样本组。选择在5个样本组中3个及以上表现显著的变量进入最终模型。研究结果表明GWLR在模型拟合度、模型残差、空间自相关性以及预测准确率等方面均优于LR模型,说明充分考虑模型变量的空间异质性有助于提高模型的预测精度,同时也验证了GWLR在福建地区林火预测上的适应性。此外,模型参数结果显示,"日最高地表气温"、"日最低地表气温"、"日平均风速"、"24小时降水量"、"日最高本站气压"、"日照时数"、"日最高气温"和"日最小相对湿度"8个因子对福建省林火发生有显著影响,研究结论为福建地区林火预测预报提供了新的方法。     

Climatic impacts on winter wheat in Oklahoma and potential applications to climatic and crop yield prediction

Climatic anomalies can pose severe challenges for farmers and resource managers. This is particularly significant with respect to gradually developing anomalies such as droughts. The impact of the 1995-1996 drought on the Oklahoma wheat crop, and the possibility that predictive information might have reduced some of the losses, is examined through a combined modeling approach using climatological data and a crop growth model that takes into account an extensive range of soil, climatic, and plant variables. The results show potential outcomes and also illustrate the point at which all possible climatic outcomes were predicting a significantly low wheat yield. Based on anecdotal evidence of the 1995-1996 drought, which suggested that farmers who planted at different times experienced different yields, the model was run assuming a variety of different planting dates. Results indicate that there is indeed a noticeable difference in the modeled wheat yields given different planting dates. The information regarding effectiveness of planting date can be used in conjunction with current long-range forecasts to develop improved predictions for the current growing season. This approach produces information regarding the likelihood of extreme precipitation events and the impact on crop yield, which can provide a powerful tool to farmers and others during periods of drought or other climatic extremes.