重组斑马鱼Gli2蛋白N端肽段的表达及纯化

Hedgehog(Hh)信号及其信号传递系统在生物机体发育中起着非常重要的作用。从Hhs分泌到其靶基因收到来自Hhs的信号的过程中，包括很多步骤，而每一步骤都有不同的因子参与作用。在其传递的过程中，参与受体细胞将胞外的信号传到胞内调节其目标基因的一个重要因子是果蝇分节-极性基因cubitus in terruptus(Ci)及其脊椎动物同源基 因-G li编码的蛋白。     

基于紧密堆积理论的低密度固井水泥浆设计

合理的颗粒级配是提高固井水泥环内部密实度、早期力学强度和界面胶结能力的关键。基于此,本文从几种典型的连续颗粒级配的紧密堆积理论中优选出DFE进行固井水泥浆体系设计,并通过大量实验首次确定了适宜G级油井水泥基深水固井水泥浆体系设计的n值最优范围：0.33~0.40。结合激光粒度分析仪实测结果,在粒径≤3.38 μm、3.38~70.70 μm、≥70.70 μm三个范围内依次采用纳米CaCO₃（nano CaCO₃,以下简称“NC”）、G级油井水泥和漂珠,并按DFE（n=0.33~0.40）体积分布曲线进行混配。在此基础上,提出一种密度为1.50 g/cm³的低成本低温早强三元固相级配水泥浆体系,并与漂珠二元固相水泥浆体系和NC二元固相水泥浆体系的性能进行对比。研究结果表明：该三元体系水泥石的抗压强度、抗折强度和二界面胶结强度相比于漂珠二元体系和NC二元体系分别提高了7%~21.1%、13.4%~51.9%和41.4%~122.2%。三元体系的固井水泥石在垂向上的最大密度差为0.022 g/cm³。基于DFE设计的三元体系具有良好的流变性、稳定性和力学性能。DFE对于多元固井水泥浆体系的设计和应用具有一定的指导意义,在保证低密度的前提下能够有效地提高固井水泥石的早期强度和综合性能。     

Characterization,tissue distribution,and expression of neuropeptide Y in olive flounder Paralichthys olivaceus

Neuropeptide Y(NPY) is a 36-amino acid peptide of the neuropeptide Y family that plays key roles in the regulation of food intake. In this study,we focused on NPY m RNA expression changes around feeding time and during food deprivation in olive flounder. The olive flounder NPY m RNA levels were analyzed in different tissues and a high level of expression was detected in the brain. We also demonstrated a correlation between NPY expression levels in the brain and feeding schedule. NPY expression levels in olive flounder maintained on a daily scheduled feeding regimen increased shortly before feeding and decreased after the scheduled feeding time. Compared with the-1 h group before feeding,NPY expression in the 3 h group after feeding decreased significantly( P <0.05). Food deprivation led to an 81.7% decrease in NPY m RNA levels in the 24 h fasted group(P <0.05) and a 91.7% decrease in the 48 h fasted group(P <0.05). Therefore,our study demonstrates that NPY expression is associated with food intake in olive flounder. This result reveals the function of NPY in regulating food intake and its potential importance in olive flounder aquaculture.     

商丘雾变化的气候特征及天气分型

依据商丘市8个站1961~2004年雾资料,分析了大雾天气的分布和气候变化特征。结果表明:商丘市雾的地理分布是西部睢县至宁陵一带为多雾区,南部柘城至夏邑一带为少雾区。宁陵出现大雾最多,睢县次之,柘城雾日最少。年际变化总体呈上升趋势。月际变化呈“V”型特征,秋冬季雾最多,夏季最少。雾的日变化一般在下半夜到清晨日出前后形成,05:00~06:00最易生成大雾,雾消时间一般在06:00~12:00,日出后07:00~08:00雾最容易消散。最长连雾日一般出现在11至次年1月,而1月出现最长连雾日的次数最多。雾的持续时间3 h以下的短雾最多,12~24 h的最少,没有超过24 h的长雾,连雾时间最长为23.3 h。年最多雾日,宁陵最多为120 d,柘城最少只有32 d,其余各站在40~77 d之间。商丘市雾发生时的地面天气形势主要有大陆高压型、冷锋前暖区型、均压场型和(低压)倒槽型。     

2009年6月3日商丘市强飑线过程分析

利用商丘新一代天气雷达(CINRAD/SB)观测资料,以及MICAPS资料和自动气象站观测资料,对2009年6月3日发生在商丘的一次以大风为主,并伴有降水、雷电发生的强飑线天气过程进行了分析。分析表明:地面辐合线是这次强对流和飑线天气过程的触发机制。高空冷涡后部冷空气南下,近地层为暖中心,形成了上冷下暖的位势不稳定层结;风力突增、气压涌升、气温骤降、湿度猛升等气象要素变化符合飑线特征。飑线最大回波强度达69 dBz,最大负径向速度值达-37 m/s,强回波区呈"厂"字形,位于回波区前沿,移动速度快,并具有径向风速辐合等特征。径向速度场上表现为高层负径向速度中心值的迅速减小和低层负径向速度中心值的快速增大。     

一次暴雨过程的动力诊断

应用常规观测资料及NCEP再分析资料,对2008年5月21日福建中部地区暴雨成因进行诊断分析。结果表明:此次强降水位于水汽通量散度平流项与散度项辐合配置较好区域;高层较强涡度平流促进福建中部地区上升运动;差动假相当位温平流增强大气不稳定度,促进垂直上升运动;低层水平运动锋生中心的位置和强度与未来6h降水中心位置和强度较为一致。     

江西省新余市地下水系统防污性能评价

在简单介绍新余市的自然条件和水文地质概况的基础上,采用DRASTIC方法对评价区的地下水系统防污性能进行评价,根据相对程度将其划分为防污性能好、防污性能较好、防污性能中等、防污性能较差和防污性能差5个等级区,为制定地下水污染防治和地下水资源保护区划提供了依据。     

基于生态适宜性评价的西南丘陵区土地整治工程布局研究

如何科学合理地识别土地整治工程的生态适宜性,是评判当前土地整治工程是否实现“有利生产,方便生活,兼顾生态”目标的重要依据,更是土地整治理论与规划亟待解决的重要命题。选取川东平行岭谷区的垫江县为研究区,立足146个土地整治项目,借助生态位思想进行土地整治工程生态适宜性评价,计算土地生态整治工程布局潜力指数,对土地整治工程生态化布局进行定向。结果表明：① 土地整治工程生态适宜性指标影响显示出显著异质性,其中耕地连片度、坡度、灌溉保证率是开展土地整治工程生态适宜性评价的首选因素。② 土地整治项目中单项工程的生态适宜性水平差异较大,其中土地平整和灌排系统的生态适宜性低于临界水平,田间道路和生态景观工程的生态适宜性高于临界水平。③ 土地整治项目整体工程的生态适宜性程度差异显著,各项目土地整治工程生态适宜性均值为0.72,处于生态适性的临界水平。适宜等涉及土地整治面积7434 hm²,占9%。临界适宜等涉及土地整治面积54530 hm²,占67%。不适宜等涉及土地整治面积18845 hm²,占24%。④ 研究区土地整治工程生态适宜性布局整体呈多样化格局,Ⅰ等潜力区主要位于研究区中西部,布局方向为发展多功能农业,应注意如何保证其经济价值实现的同时不损害其生态价值。Ⅱ等潜力区分布范围较广,布局方向为发展大宗粮油产业,应思考在保证其粮食生产的同时尽量避免对农田生态系统的破坏。Ⅲ等潜力区主要位于鹤大台地区,布局方向为发展生态立体农业,应强调如何在保证生态环境不受破坏的前提下发展具有山区特色的生态农业。     

Single epoch estimation of the Galileo integrity chain sensor station clock offsets

The Galileo integrity chain depends on a number of key factors, one of which is contamination of the signal-in-space errors with residual errors other than imperfect modelling of satellite orbits and clocks. A potential consequence of this is that the user protection limit is driven not by the errors associated with the imperfect orbit and clock modelling, but by the distortions induced by noise and bias in the integrity chain. These distortions increase the minimum bias the integrity chain can guarantee to detect, which is reflected in the user protection limit. A contributor to this distortion is the inaccuracy associated with the estimation of the offset between the Galileo sensor station (GSS) receiver clocks and the Galileo system time (GST). This offset is termed the receiver clock synchronization error (CSE). This paper describes the research carried out to determine both the CSE and its associated error using GPS data as captured with the Galileo System Test Bed Version 1 (GSTB-V1). In the study we simulate open access to a time datum using IGS data. Two methods are compared for determining CSE and the corresponding uncertainty (noise) across a global network of tracking stations. The single-epoch single-station method is an ‘averaging’ technique that uses a single epoch of data, and is carried out at individual sensor stations, without recourse to the data from other stations. The global network solution method is also single epoch based, but uses the inversion of a linearised model of the global system to solve for the CSE simultaneously at all GSS along with a number of other parameters that would otherwise be absorbed into the CSE estimate in the averaging technique. To test the effectiveness of various configurations in the two methods the estimated synchronisation errors across the GSS network (comprising 25 stations) are compared to the same values as estimated by the International GPS Service (IGS) using a global tracking network of around 150 stations, as well as precise orbit and satellite clock models determined by a combination of global analysis centres. The results show that the averaging technique is vulnerable to unmodelled errors in the satellite clock offsets from system time, leading to receiver CSE errors in the region of 12 ns (3.7 m), this value being largely driven by the satellite CSE errors. The global network approach is capable of delivering CSE errors at the level of 1.5 ns (46 cm) depending on the number of parameters in the linearised model. The International GNSS Service (IGS) receiver clock estimates were used as a truth model for comparative assessment.