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Galindo et. al. 2006

Galindo et. al. 2006
Galindo et. al. 2006

Current Biology16,1622–1626,August22,2006a2006Elsevier Ltd All rights reserved DOI10.1016/j.cub.2006.06.052

Report Seascape Genetics:A Coupled

Oceanographic-Genetic Model Predicts

Population Structure of Caribbean Corals

Heather M.Galindo,1,*Donald B.Olson,2

and Stephen R.Palumbi1

1Department of Biological Sciences

Stanford University

Paci?c Grove,California93950

2Rosenstiel School of Marine and Atmospheric Sciences

University of Miami

Miami,Florida33149

Summary

Population genetics is a powerful tool for measuring important larval connections between marine popula-tions[1–4].Similarly,oceanographic models based on environmental data can simulate particle movements in ocean currents and make quantitative estimates of larval connections between populations possible [5–9].However,these two powerful approaches have remained disconnected because no general models currently provide a means of directly comparing dis-persal predictions with empirical genetic data(except, see[10]).In addition,previous genetic models have considered relatively simple dispersal scenarios that are often unrealistic for marine larvae[11–15],and re-cent landscape genetic models have yet to be applied in a marine context[16–20].We have developed a genetic model that uses connectivity estimates from oceanographic models to predict genetic patterns resulting from larval dispersal in a Caribbean coral. We then compare the predictions to empirical data for threatened staghorn corals.Our coupled oceano-graphic-genetic model predicts many of the patterns observed in this and other empirical datasets;such patterns include the isolation of the Bahamas and an east-west divergence near Puerto Rico[3,21–23]. This new approach provides both a valuable tool for predicting genetic structure in marine populations and a means of explicitly testing these predictions with empirical data(Figure1).

Results

Oceanographic Model

Ocean simulations are based on the MICOM North Atlantic simulations[8]between24 S and approximately 70 N(see[6,24,25]).The model uses wind data to esti-mate currents at19vertical layers and has been tested with ocean drifters[26].To generate connectivity matri-ces for each model run,we released1000particles at 87randomly chosen Caribbean locations for each of the years in the range from1982–1986.Particles were released during the summer(Julian days205–219)and had durations of14days,consistent with expected larval duration for staghorn corals[27].Arrival calcula-tions incorporated a buffer within25km of the model coastal boundary,which is set in MICOM at25m depth. For each model run,we used observations of particle arrival at each locality on day14to estimate a connectiv-ity matrix(probability that a particle released at point i arrives at point j).(Access to the matrices is available from the authors upon request.)

Description of the Genetic Model

The genetic model uses a connectivity matrix to simu-late the effects of dispersal and genetic drift on a one-lo-cus,two-allele neutral marker without mutation.All pop-ulations begin with equal allele frequencies and diploid populations of approximately100individuals.In each nonoverlapping generation,50%of individuals are ran-domly chosen for synchronous reproduction,and a lar-val pool is then created in Hardy-Weinberg equilibrium from the selected parents.The above assumptions are characteristic of basic population genetic models[4, 14].(See Supplemental Data available with this article online for choice of parameters.)

Larval movement is simulated in two ways.The deter-ministic model uses a connectivity matrix to calculate the fraction of the larval pool that disperses between each pair of populations.In the individual-based sto-chastic model,entries in the same connectivity matrix are used as relative probabilities that a larva disperses to each of the other populations,is retained,or experi-ences mortality before settling.The two versions assess the relative impact of allowing genetic drift to occur only during reproduction(deterministic)or during both repro-duction and dispersal(stochastic).After arrival,all lar-vae settle and enter the adult population.Allele frequen-cies vary across generations,but population sizes are reset to initial conditions every generation.Multiple runs of the single-locus model are interpreted as data from multiple loci because each run is independent. Five sets of ten runs each used the connectivity matrix for each of the years from1982–1986.An additional set of simulations used the matrices for all?ve years in a repeated sequence(the‘‘all-years’’simulations).Each of these six sets was run for100generations with both the deterministic and stochastic model versions for a total of twelve oceanographic https://www.doczj.com/doc/e42812650.html,st,we ran a set of ten runs with each of the two model versions by using a panmictic matrix to provide a null model for data analysis.(Additional runs with larger populations and longer durations are described in the Supplemental Data).

Genetic Results and Testing A Priori Hypotheses

We analyzed data from120model runs by using Arle-quin2.0[28]to calculate genetic differences among populations(F ST),among groups de?ned a priori(F CT), and among populations within groups(F SC).For all twelve oceanographic regimes,we found strong popu-lation genetic structure.Mean F ST values for all87

*Correspondence:hgalindo@https://www.doczj.com/doc/e42812650.html,

populations fall between 0.115and 0.568,with every value being signi?cantly different from zero (data not shown).In contrast,the mean F ST for the null panmictic model was 20.007;none of these runs were signi?cant.Next,data from the 87populations were grouped in two ways.First,we created groups corresponding to collection sites across the Caribbean for a genetic anal-ysis of staghorn corals (Vollmer and S.R.P.,unpublished data).For the simulated populations,genetic differ-ences were strong among regions,but not among pop-ulations within regions (data are displayed in Figure 2).Mean F CT values ranged between 0.130and 0.590and were signi?cant for 90%–100%of the runs in each oceanographic regime except for the 1984deterministic regime (60%runs signi?cant).In contrast,F SC values were nonsigni?cant except for 10%of runs for the 1983stochastic regime (F SC =0.095),50%for the 1984stochastic regime (F SC =0.015–0.206),and 100%for the 1984deterministic regime (F SC =0.014–0.272).Mean F CT values were 0.000and 0.002for the null deter-ministic and stochastic models respectively with only one stochastic run being signi?cant.Null model F SC values were all nonsigni?cant and had means of 20.010to 20.005.

We then created an east-west comparison of all 87Ca-ribbean populations by separating them across a line running NE-SW between Puerto Rico and the Dominican Republic [29].Compared to results for the coral group-ings,evidence for an east-west divide in populations was less robust.Only 20%–70%of the runs under deter-ministic regimes showed signi?cant F CT values (mean values from 0.034–0.062).However,results from the sto-chastic model showed a higher east-west division;there were signi?cant values in 60%–90%of the runs,with mean values ranging from 0.141–0.302.For all runs in both versions of the model,signi?cant F SC values indi-cated that variation within regions was substantial (mean values ranged from 0.077–0.285).Null model mean F CT /F SC values were 0.000/20.010and 0.000/20.005for the deterministic and stochastic models,re-spectively,with only two stochastic runs having signi?-cant F CT values.

Overall,genetic structure from all-years simulations tended to be lower than those run for a single year at a time (Figure 2),re?ecting differences in currents from year to year.The individual-based stochastic model often predicts higher genetic differentiation because of additional genetic drift during dispersal.However,the patterns are qualitatively the same as those of the deter-ministic model.

Geographic Inferences from Genetic Simulations

To explore geographic predictions of the model,we used two common clustering methods.First,we used PRIMER 5.0[30]to calculate Bray-Curtis distances for all pairs of 87populations across all ten loci and to pro-duce dendrograms that clustered genetically similar populations.The reliability of population clusters was estimated by jackkni?ng across loci.Clusters that were robust across all twelve oceanographic regimes are shown in an insert in Figure 3.This approach is anal-ogous to methods used to combine phylogenetic trees from genetic analyses to show population groups that have similar gene frequencies.Membership in some of these groups varies among runs or years.For example,population 42(Turks and Caicos Isl.)sometimes joins the Hispaniola group.However,some regional group-ings,especially those in the eastern Caribbean and those along the Meso-American Barrier Reef,were robust (Figure 3).

Next,we used the software STRUCTURE to de?ne the most likely genetic clusters based on the relative proba-bilities of the data given a certain number of clusters (Pr(X j K)).Populations were then grouped according to the similarity of their patterns of relative membership (q values [31])in each of the genetic clusters as deter-mined by the model.(Details are available in the Supple-mental Data ).

When overlaid on a map of the Caribbean,results from both the PRIMER and STRUCTURE analyses

indicate

Figure 1.Conceptual Model of Coupled Oceanographic-Genetic

Approach

Figure 2.Fixation Indices across Oceanographic Models Run for 1982–1986and All Years

Data show mean overall levels of genetic differentiation for model populations grouped according to A.cervicornis collection sites.Population groupings:Bahamas =34,35,38;Turks and Caicos =42;Puerto Rico =16,17;Curacao =11,12;Panama =4,5;Belize =61,70,71;Yucatan =63,Jamaica =83,numbers from Figure 3.)Error bars represent one standard deviation.Results from the deter-ministic (closed circles)versus stochastic versions of the model (open circles)are shown.A.cervicornis data represent a range of values across mitochondrial and nuclear loci (Vollmer and S.R.P.,unpublished data).

A Coupled Seascape Model Predicts Genetic Structure 1623

very similar geographic groupings of genetically clus-tered populations (Figure 3).These clusters include the northern Gulf of Mexico (Populations 56–58,brown cir-cles in Figure 3),eastern Florida straits (31–33,light blue),the Bahamas (38,yellow),Panama (1,3–7,laven-der),and Turks and Caicos/Dominican Republic (13,14,41–47,dark green).Results from the two analyses differ slightly in a few cases.STRUCTURE indicates a single south Cuba/Jamaica cluster (8–10,81–87),whereas PRIMER divides this cluster into two closely re-lated groups (8–10,83–87,turquoise;81–82purple).A similar situation occurs for the single north-Cuba cluster (28–30,36,37,39,40)from STRUCTURE and the two related clusters from PRIMER (28–30,36–37,39,dark blue;40,dark purple).In contrast,STRUCTURE divides the Yucatan/Belize group (2,60–65,68–73,77)from the group southwest of Cuba (74–76,78,80),whereas PRIMER put them in a single group (2,60–80,green).Last,both analyses group the U.S.Virgin Islands (16,17,red)separately from the Lesser Antilles (19–27,pink)but differ in their placement of population 18.Although results occasionally differ in their resolution,they are entirely consistent at the regional https://www.doczj.com/doc/e42812650.html,parison to Empirical Data

Staghorn corals,Acropora cervicornis ,in the Caribbean have been recently shown to exhibit strong population structure,especially for populations more than 500km apart (Vollmer and S.R.P,unpublished data).Across major regions of the Caribbean (Panama,Belize,the Bahamas,Turks and Caicos,Puerto Rico and Curacao),data from three nuclear introns and mtDNA indicate that genetic differentiation for staghorn corals is high,with an average F ST of 0.021–0.235(Vollmer and S.R.P.,unpublished data).These values are similar to those from the AMOVA analysis on the coral groupings in the model data,particularly for the all-years simulations (Figure 2).

Comparing the model results to genetic data shows broad concordance at the regional scale.For collection sites across the east-west divide,analysis of A.cervi-cornis data gives a highly signi?cant F CT of 0.08(p value,0.001).Although differing quantitatively,results from analysis of the all-years stochastic dataset indicate a mean F CT of 0.31(signi?cant for 60%of the runs).For comparison,the mean F CT for the null panmictic sim-ulations with the stochastic model was 20.001,with none of the runs being signi?cant.Empirical results also indicate that the Bahamas are isolated from the rest of the Caribbean (F CT =0.06;p =0.003).The all-years stochastic dataset has a high mean F CT of 0.20across runs,but none of the runs have a signi?cant F CT because of high variation among populations.Mean F CT for the null model was 0.0009,with two of the runs being signi?cant by chance.However,the STRUCTURE and PRIMER results clearly distinguish between the Bahamas (population 38)and the rest of the Caribbean (Figure 3

).

Figure 3.Results of Caribbean Model

The 87randomly chosen larval-release locations are indicated here by black or colored circles.Each release location corresponds to a 25km arrival zone (shaded gray)where larval arrivals were counted.Release locations were chosen along a rhumbline,which occasionally resulted in the locations being far from the coast (e.g.,48,51,79).Collection sites for A.cervicornis are indicated by black stars.Colored circles indicate arrival locations with genetically similar populations as determined by the consensus dendrogram (insert)from PRIMER.These groupings also represent the genetic cluster analysis from STRUCTURE.The curved dotted line demarks the east-west genetic boundary suggested by Baums et al.[47]and Taylor and Hellberg [3].

Current Biology 1624

Discussion

A New Tool for Investigation of Marine Larval Dispersal

We show that advanced oceanographic simulations can be used to predict genetic structure of marine popula-tions and that these predictions can then be tested against empirical https://www.doczj.com/doc/e42812650.html,rval dispersal connects many marine populations,and knowledge of these connec-tions is fundamental to the design of marine managed areas,?sheries management,and conservation plans [1,32,33,34].However,tracking larval dispersal trajec-tories is very dif?cult[35],and a detailed understanding of marine connectivity requires integrating approaches from oceanography,marine ecology,tagging studies, and population genetics[1,6].The seascape model pre-sented here is a step forward in explicitly using ocean-ography to predict population genetic structure and then directly comparing these predictions to empirical genetic results and known biogeographic patterns. Among Caribbean corals,regional genetic differences for four species([21–23]and Vollmer and S.R.P.,unpub-lished data)are consistent with our modeling results.In addition,biogeographic and genetic studies of reef?sh (e.g.,[3,29])and reef faunal assemblages[36–38]high-lighted an east-west biogeographic divide,as well as north-south distinctions,in the Caribbean.In general, our coupled model predicts genetic divisions for low-dispersal species from east to west and from north to south across the Caribbean Sea.However,these con-clusions may not hold for species with wider dispersal, and empirical support for strong Caribbean genetic structure is rarer in these species[39–42].

Our simple oceanographic-genetic model success-fully predicts broad-scale genetic structure and pro-vides a seascape view of marine neighborhoods.How-ever,larval behavior[43],local adaptation[44],or larval mortality may reduce connectivity to levels below those produced in our model[6].Many marine species have longer planktonic durations than the simulations we perform here[1,2],and genetic structure in these species may depend on factors other than oceanogra-phy.In these cases,deviations of empirical data from model predictions may signal important larval-dispersal features not explainable by simple oceanography. Limitations

Although our coupled oceanographic-genetic model successfully predicts the qualitative pattern of genetic structure,it cannot accurately predict the magnitude of genetic differences(e.g.,levels of F ST in Figure2).Bet-ter numerical predictions would require additional bio-logical data(such as spawning,recruitment,mortality, and population sizes[26])and further oceanographic information,particularly for species with complicated larval behavior.In addition,model areas that by chance have low density of particle releases may restrict step-ping-stone dispersal.Because the rate of genetic drift and the build-up of F ST depend strongly on dif?cult-to-measure parameters such as effective population size [13],accurate quantitative predictions of F ST are un-likely.In addition,a more complex genetic model involv-ing multiple alleles at several loci,mutation,and recom-bination would allow a drift-migration equilibrium to be attained and rare dispersal events to be incorporated. Finally,small-scale differences in genetic structure can-not be easily predicted by our current models because the oceanographic simulations do not perform well in shallow water[26].Yet,some closely spaced marine populations have strong genetic differences([3]and Vollmer and S.R.P.,unpublished data).A higher-resolu-tion oceanographic circulation model is needed to make comparisons with genetic datasets on a?ner geo-graphic scale.

Future Applications

Our genetic model provides a?exible framework appli-cable to any dispersal scenario for which a connectivity matrix can be provided.Sources of such matrices could include other oceanographic models or empirical data from natural or arti?cial tagging studies(e.g.,[45,46]). Model parameters such as population sizes,initial con-ditions,and reproductive patterns are easily modi?ed to ?t the life-history strategies for a wide variety of species. Comparisons between the deterministic and stochastic models indicate that both predict similar geographic patterns of genetic structure,but the added level of ge-netic drift during the dispersal phase in the stochastic model leads to more distinct structure.Data analysis with Arlequin,PRIMER,and STRUCTURE provide sim-ple comparisons to empirical data sets and similar geo-graphic clusters of related populations.The coupled seascape model can be a powerful source of insight into the role of oceanographic features in marine popu-lation biology.

Supplemental Data

Supplemental Data are available with this article online at http:// https://www.doczj.com/doc/e42812650.html,/cgi/content/full/16/16/1622/DC1/. Acknowledgments

We would like to thank Geoff Samuels for running the drift simula-tions and compiling the connectivity tables and Claire B.Paris for initial work on the oceanographic model.Paul R.Armsworth,Tom Oliver,Steven L.H.Teo,and Tomo Eguchi provided valuable help in the development of the genetic model.We thank Arjun Sivasundar, Mollie Manier,Liz Alter,Margaret McManus,Anna Pfeiffer-Hoyt, Paul Barber,Matt Hare,Richard Grosberg,and four anonymous reviewers for comments leading to the signi?cant improvement of the manuscript and Steve Vollmer for use of coral data.The authors were supported through the Bahamian Biocomplexity Project spon-sored by the National Science Foundation(NSF OCE-0119976), NOAA-FL Sea Grant NA16RG-2195,and grants from the David and Lucille Packard and Gordon and Betty Moore Foundations to PISCO.In addition,this material is based upon work supported un-der a National Science Foundation Graduate Research Fellowship awarded to H.M.G.

Received:April7,2006

Revised:June9,2006

Accepted:June12,2006

Published:August21,2006

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Current Biology 1626

护理操作并发症及处理措施

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ZB-WXZ微机消谐装置带通讯说明书

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● 1、2:装置工作电源,可接交流220V 或直流220V (不区分正负极, 110V 需特殊订货)。 ● 3:接大地。 ● 4和8接PT 开口三角电压,5、6、7为空位。 图1-2:WXZ 型微机消谐装置开孔尺寸图 图三:WXZ 型微机消谐装置端子排图(1) 图二:WXZ 型微机消谐装置开孔尺寸图

●9:备用端子。 ●10、11、12:为RS232/RS485通讯接口。 当通讯接口为485时,10为A+, 11为B-,12为备用空端子。 端子接线介绍 ●13、14:备用端子。 ●15、16:失电告警信号输出点(无源干结点,不是标准配置,需要时 订货时请说明)。 ●17、18:装置故障告警信号输出点(无源干结点)。 ●19、20:系统接地告警信号输出点(无源干结点)。 ●21、22:系统谐振告警信号输出点(无源干结点)。 ●23、24:系统过电压告警信号输出点(无源干结点)。 图三:WXZ型微机消谐装置端子排图(2) 五、装置特点 (1) 适用于35KV以下各种电压等级,各种谐振频率(1/3分频、1/2分频、 工频、3倍频)适用范围广泛; (2) 无需整定和调试,开机自检后自动进入运行状态,设备维护量低; (3) 在铁磁谐振时进行快速消除,在过电压,单相接地等故障时给出报警信 号并显示保存相关信息; (4) 实时显示,记录铁磁谐振发生时间及相关参数(电压和频率); (5) 可存储20重故障信息供追忆和显示; (6) 以接点闭合方式输出或语音报警输出; (7) 可以配置通信接口(RS485接口); (8) 可以配置微型打印机可以及时打印输出故障报告(故障类型、故障时间 及4种频率的电压分量) 六、装置的工作原理 本装置采用高性能的单片微机作为核心元件,对PT开口三角电压(即零序电压)进行遁环检测。正常工作情况下,该电压小于30V,装置内的大功率消谐

微机消谐器

微机消谐器 使用说明书 Baoding Enuoer Electric Equipment Co., Ltd 保定市伊诺尔电气设备有限公司

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临床护理技术操作常见并发症的预防及处理考试题

临床护理技术无菌操作及操作常见并发症的预防及处理考试题科室:姓名:分数: 一:填空题(每空2分,共24分) 1、肌肉注射神经损伤的发生原因:①;②注射药量过大或者推药速度过快。 2、静脉输液法发生静脉炎的三个因素是:化学因素、机械因素、。 3、静脉输液法发生发热反应的处理:(1)或停止输液(2)对症处理(3)。 4、皮内注射最严重的并发症是:。 5、静脉输液技术操作的并发症:、、、发热、空气栓塞、血栓栓塞。 6、静脉输血操作并发症:、、、急性左心衰、过敏反应、出血倾向、。 二:选择题(每题4分,共12分) 1、下列与输液时滴数调节无关的是() A、病人的性别 B、病人的年龄 C、药物的作用 D、药液的性质 E、病人的病情 2、静脉输液发生空气栓塞时应采取的卧位是() A、半卧位 B、端坐位 C、右侧卧位,头低足高位 D、左侧卧位,头低足高位 E、左侧卧位,头高足低位 3、皮下注射法进针时,与皮肤呈:() A、10°~15°角 B、20°~25°角 C、30°~40°角 D、45°~50°角 E、50°~60°角 4、、打开无菌包时不正确的是()A.查看灭菌日期B.无菌包应放在清洁、干燥处 C.手不可触及包布的内面 D.用清洁的手取出所需物品 E.包内所剩物品应在有效期内使用 5、取用无菌溶液时先倒出少量溶液的目的是为了() A、检查瓶口有无裂缝 B、冲洗瓶口 C、查看溶液的颜色 D、检查溶液有无沉淀 E、嗅察溶液有无异味

6、无菌持物钳正确使用方法是:()A.可用于夹取任何无菌物品 B.到远处夹取物品要速去速回 C.取放无菌持物钳,钳端均应闭合 D.钳端向上,不可跨越无菌区 E.盛放无菌持物钳的容器消毒液面应与轴节相平 7、小剂量、单包装的无菌消毒液,开启后其有效期为()A、三天B、每天C、一周D、两周 E、两天 8、无菌贮槽一经打开其有效使用时间为()A、2小时B、4小时C、12小时D、24小时E、7天内 9、 三:简答题: 1、皮内注射发生过敏性休克(最严重的并发症)的预防及处理(21分) 2、皮内注射疼痛的预防措施有哪些(22分)

微机消谐说明书

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1 概述 WXX-2微机消谐装置(以下简称产品),主要用于电压等级3kV ~66kV 的中性点不接地、经消弧线圈接地或经小电阻接地的电力系统中,作为消除非永久性铁磁谐振过电压故障的元件。 1.1 主要功能 a ) 两路3U 0信号输入,两路消谐出口,可分别控制两段母线,通过对3U 0进行实时 监控,满足条件时执行相应操作; b ) 谐振时有相应提示并自动保存事件; c ) 参数设置简单,可断电保存; d ) 失电监测功能,产品上电后异常信号触点断开,失电后触点闭合; e ) 系统自检功能,故障时发异常信号,触点闭合,信号保持; f ) 可选择消谐方式; g ) 可通过开入控制手动消谐或遥信手动消谐; h ) 分频消谐方式下,系统过电压时可由用户通过菜单选择是否进行手动消谐; i ) 遥信、遥测、遥控功能。 1.2 主要特点 a ) 本产品为微机保护装置,其元器件采用工业品,稳定性、可靠性高,数据采集、 运算、逻辑判断、控制输出等速度快,精度高,自检及自恢复能力强; b ) 抗干扰性能强,保护硬件设计采用了多种隔离、屏蔽措施,软件设计采用良好的 保护算法及其它抗干扰措施,使得保护抗干扰性能大大提高; c ) 硬件、软件设计标准化、模块化,便于现场维护。 d ) 可选择分频消谐方式或综合消谐方式,分频消谐方式下,系统过电压时可由用户 选择是否进行手动消谐,防止产品因接地等其他原因误动而导致事故扩大; e ) 产品的人机接口功能强大,符合人机工程设计要求,菜单化设计,全中文液晶显 日 期 签 字 底图总号 旧底图总号 描 校 描 写

常见并发症处理

目录 1、临床护理技术操作常见并发症的预防与处理规范……………l 2、使用吸引器并发症预案及处理 (23) 3、使用除颤仪并发症预案及处理 (24) 4、使用输液泵并发症预案及处理 (25) 5、输液泵的操作流程 (26) 6、微量注射泵操作规程 (27) 7、电动吸引器操作程序与保养 (28) 8、心电图机操作规程与使用方法 (29) 临床护理技术操作 常见并发症的预防与处理规范

一、测体温(口表)操作并发症预防及处理 1)体温表破损 预防: ①护士测体温前,检查体温表的质量。 ②患者神志清者,叮嘱患者不要说话、勿咬破。 ③患者神志不清者,测腋温或肛温,有人看管。 处理: ①检查患者是否吞入水银及口腔黏膜完好程度。 ②立即报告护士长。 ③嘱患者漱口、吐出。 ④如有吞入,立即给患者吞服蛋清、牛奶等,食粗纤维蔬菜。 二、口腔护理并发症预防及处理 1)窒息预防: ①意识不清者禁漱口,用血管钳夹紧棉球,每次只用1个棉球,防止棉球遗漏在病人口腔内。 ②棉球湿度适当,以不滴水为标准。 ③有活动性假牙者应先取下。 处理: ①呼救报告医生。 ②取出异物(用手,血管钳,吸引器等)。 ③给病人取头低脚高位,拍背。开放气道,给氧,必要时人工呼吸。 2)黏膜损伤

预防: ①夹棉球方法正确,不能用钳子直接接触黏膜. ②擦洗动作轻柔。 处理: ①损伤黏膜处出血者立即止血。 ②保护受损黏膜(用西瓜霜等)。 三、保护用具使用操作并发症预防及处理 1)床栏致伤及床栏损坏 预防: ①护士每班检查床档功能。 ②患者肢体与床档之间用软枕隔挡,以保护患者肢体。 ③教会照顾者正确使用床档,确保安全。 处理: ①报告护士长、医师。 ②按医嘱对患者碰伤肢体进行检查,必要时拍片,明确诊断。 ③立即报修。 2)约束带致伤及肢体淤血预防: ①注意约束松紧。 ②使用棉质软约束带,必要时垫衬垫。 ③密切观察约束部位的血液循环。 处理: ①报告护士长、医师。 ②立即松开约束带,有专人看护。 ③按医嘱对淤血、皮肤破损处进行换药。

临床护理技术操作常见并发症的预防和处理规范

临床护理技术操作常见并发症的预防和处理规范目录 1、测体温(口表)操作常见并发症预防及处理 2、保护用具使用操作常见并发症的预防及处理 3、口服给药操作常见并发症预防及处理 4、口腔护理操作常见并发症预防及处理 5、鼻饲护理操作常见并发症预防及处理 6、氧气吸入操作常见并发症预防及处理 7、导尿术操作常见并发症预防及处理 8、雾化吸入操作常见并发症预防及处理 9、胃肠减压操作常见并发症预防及处理 10、膀胱冲洗操作常见并发症预防及处理 11、大量不保留灌肠操作常见并发症预防及处理 12、心电监护操作常见并发症预防及处理 13、微量泵操作常见并发症预防及处理 14、输液泵操作常见并发症的预防及处理 15、吸痰法操作常见并发症预防及处理 16、皮内注射法操作并发症预防及处理 17、皮下注射法操作并发症预防及处理 18、肌肉注射法操作并发症预防及处理 19、静脉注射法操作常见并发症预防及处理 20、静脉输液操作常见并发症预防及处理

21、静脉留置针常见并发症的预防及处理 22、抽血法操作常见并发症预防及处理 23、静脉输血操作常见并发症预防及处理 24、胰岛素注射操作常见并发症的预防及处理 1、测体温(口表)操作常见并发症预防及处理 体温表破损 1、预防 (1) 护士测体温前,检查体温表的质量。 (2) 患者神志清者,叮嘱患者不要说话、勿咬破。 (3) 患者神志不清者,测腋温或肛温,有人看管。 2、处理 (1) 检查患者是否吞入水银及口腔黏膜完好程度。 (2) 立即报告护士长。 (3) 嘱患者漱口、吐出。 (4) 如有吞入,立即给患者吞服蛋清、牛奶等,食粗纤维蔬菜。 2、保护用具使用操作常见并发症的预防及处理措施 (一)、床档碰伤肢体、床档断裂 1、预防 (1) 护士每班检查床档功能。 (2) 患者肢体与床档之间用软枕隔挡,以保护患者肢体。 (3) 教会患者家属正确使用床档,确保安全。 2、处理 (1) 报告护士长、医师。 (2) 按医嘱对患者碰伤肢体进行检查,必要时拍片,明确诊断。 (3) 立即报修。 (二)、约束带过紧,肢体淤血,皮肤破损 1、预防 (1) 密切观察约束部位的血液循环。 (2) 使用约束带,必须垫衬垫。 (3) 注意约束松紧。 2、处理 (1) 报告护士长、医师。 (2) 立即松开约束带,有专人看护。 (3) 按医嘱对淤血、皮肤破损处进行换药。

WXZ196系列微机消谐装置说明书

1彳既^述 (1) 2^型号说明 ........................................... 1 ............................. .. 3使用条件 ............................................. 2................................. 4 术参 (2) 5装置特点 ............................................. 3................................. 6工作原理 ............................................. 4................................. 6 1 PT 产生铁磁谐振的原^因''"'''?. ' 1 1■ i I4... ... ............ (i) 6.2铁磁谐振产生的条件...................................... 4........................... 6.3磁谐振消除原理....................................... 4 ........................... 6.4^动作判............................................ 5................................. 7使用说明............................................ 7 ................................. 7.1修改时钟.......................................... 8................................. 7.2系统配置.......................................... 8................................. 7.2.1蜂鸣器开关........................................ 8 .............................. 7.2.2打印机开关........................................ 8 .............................. 7.2.3装置通讯地址...................................... 9 .............................. 7.2.4通讯波特率........................................ 9 .............................. 7.3 系统自检.......................................... 9................................. 7.3.1指示灯检查........................................ 9.............................. 7.3.2打印机检查........................................ 9 .............................. 7.3.3继电器检查........................................ 9 .............................. 734 查看米样^^值■■ ■ ■■■ ■ ■ ■■■■ ■ ■■■ ■ ■ ■■ ■■ ■ ■■■ ■ ■■ ■■ ■ ■■■ ■ ■ ■■ ■■ ■ ■■■■■ ■■ ■■ T0?.................... . 7.4 故障报告.......................................... 10 ................................

消谐装置作用及工作原理

PT二次消谐装置说明书 一、概述 在电力系统中,由于电压互感器的 非线性电感与线路对地电容的匹配而引起铁 磁谐振过电压,直接威胁电力系统的安全运 行,严重时会引起电压互感器(PT)的爆炸, 造成事故。传统的解决办法是在电压互感器 开口三角两端并接一个电阻,从理论上讲对 频率越低的铁磁谐振阻值应取得越小,但太 小的电阻并在PT开口三角上会影响其正常 运行,严重时会造成PT烧毁。另外因为铁磁 谐振的频率往往不是单一的,所以这种方法 就难于消除所有频率的谐振。 针对上述情况,国内一些厂家先后研制了一些分频消谐装置。这些装置的原理均是采用模拟选频的原理,功能单一,只对单一频率的谐振有效。由于电网中谐振往往是多种频率同时存在,所以其适应性较差,模拟电路实现的选频与微机选频相比其选频效果也差,有时电网的过渡过程等也会造成误动。 PWX-50系列微机消谐装置将微机技术用于电网消谐,利用计算机快速、准确的数据处理能力实现快速傅里叶分析,其选频准确。通过对PT开口三角电压的采集,对电网谐振时的各种频率成份能快速分析,准确地辨别出:①单相接地;②过渡过程;③电网谐振。如果是谐振,计算机发出指令使消谐电路投入,实现快速消谐。经实际运行证明本装置对各种高频、低频、工频谐振均判断准确,动作迅速,较完善地解决了电力系统中电网的消谐问题,并能记录存储谐振的次数及谐振频率,可广泛适用于发电厂、变电站及钢铁、煤炭、石油化工等大型厂矿企业的电力系统。 二、装置用途: PWX-50 系列微机消谐装置将微机技术用于电网消谐,利用计算机快速、准确的数据处理能力实现快速傅里叶分析,其选频准确。通过对 PT 开口三角电压的采集,对电网谐振时的各种频率成份能快速分析,准确地辨别出:①单相接地;②过渡过程;③电网谐振。如果是谐振,计算机发出指令使消谐电路投入,实现快速消谐。经实际运行证明本装置对各种高频、低频、工频谐振均判断准确,动作迅速,较完善地解决了电力系统中电网的消谐问题,并能记录存储谐振的次数及谐振频率,可广泛适用于发电厂、变电站及钢铁、煤炭、石油化工等大型厂矿企业的电力系统。

常见护理技术操作并发症预防及处理

一、口腔护理技术操作并发症 (一)口腔损伤 1.发生原因 (1)在口腔擦洗过程中,由于护理人员动作粗暴,裸露得止血钳尖端碰伤口腔黏膜及牙龈,特别就是肿瘤患者放疗期,口腔有感染及凝血功能差得患者,容易引起口腔黏膜及牙龈得损伤。(2)为昏迷患者进行口腔护理时,使用开口器协助张口方法不正确或力量不当,造成患者口腔、牙龈或口腔黏膜损伤。 (3)漱口液温度或浓度不当,造成口腔黏膜灼伤。 2.临床表现 (1)口腔黏膜充血、出血、水肿、炎症反应、溃疡形成,患者主诉口腔疼痛,颌下可触及淋巴结肿大。 3.预防及处理 (1)为患者进行口腔护理时,动作要轻柔,避免止血钳得尖端直接触及患者口腔黏膜。 (2)对凝血功能机制差、有出血倾向得患者,擦洗过程中特别要注意防止碰上黏膜及牙龈。(3)对需要使用开口器协助张口得患者,应将开口器包上纱布从臼齿处放入,以防损伤患者口腔黏膜或牙齿,牙关紧闭者不可使用暴力使其开口。 (4)根据口腔具体情况选择温度、浓度适宜得漱口液。 (5)在口腔护理过程中,要注意观察口腔黏膜情况。如发生口腔黏膜损伤,应用朵贝尔氏液、呋喃西林液或0、1%-0、2%双氧水含漱;如有口腔溃疡疼痛时,溃疡面用西瓜霜或锡类散吹敷,必要时可用利多卡因喷雾止痛或洗必泰漱口液直接喷于溃疡面,每日3~4次抗感染。 (二)吸入性肺炎 1.发生原因 多发生于意识障碍得患者,口腔护理得清洁液、口腔内分泌物及呕吐物误入气道,就是吸入性肺炎得主要原因。 2.临床表现 (1)发热、咳嗽、咳痰、气促及胸痛等,叩诊呈浊音,听诊肺部有湿性罗音。 (2)胸部X片可见斑片状阴影。 (3)实验室检查有白细胞增多。 3.预防及处理 (1)为患者进行口腔护理时,辅助患者采取仰卧位,切记将头偏向一侧,防止漱口液流入呼吸道。 (2)口腔护理所用棉球要拧干水分,不可过湿;神志不清患者不可漱口,以防误吸。 (3)已出现肺炎得患者,根据病情选择得抗生素积极进行抗感染治疗,并结合相应得临床表现采取对症处理。 (三)窒息1.发生原因 (1)医护人员为神志不清或吞咽功能障碍得患者进行口腔护理时,由于粗心大意,将棉球遗留在口腔,导致窒息。 (2)有义齿得患者,操作前末将义齿取出,操作时义齿脱落,造成窒息。 (3)为兴奋、躁动,行为紊乱得患者进行口腔护理时,因患者不配合造成擦洗棉球松脱,掉入气管,导致窒息。 2.临床表现 口护过程中患者突发吸气性呼吸困难,面色发绀,端坐呼吸,三凹症阳性,严重者出现面色苍白,四肢厥冷,尿便失禁,抽搐,昏迷,甚至呼吸停止。 3.预防及处理 (1)严格按照口腔护理得操作规范进行操作,一次只能夹取一个棉球,防止棉球遗漏在口腔。(2)认真检查牙齿情况。操作前瞧牙齿有无松动,义齿有无松动,如有活动性义齿,应操作前取下。 (3)对于兴奋、躁动、行为紊乱得患者尽量在其较安静得情况下进行口腔护理。 (4)患者出现窒息后应立即进行处理,迅速清除吸入异物,恢复有效通气;如异物已进入气管或支气管,患者出现严重得呼吸障碍,立即用大号穿刺针行环甲膜穿刺,以改善通气,争取时间

10kV消谐装置(教育教程)

10kV消谐装置原理介绍 2014年9月3日要掌握的知识点: 1、铁磁谐振产生的原因及危害。 2、PT产生了铁磁谐振时,谐波分量主要有哪些? 3、一般消谐装置“接地”、“过电压”、“谐振”的动作判据分别是什么?其动作出口分别是什么? 4、观音岩电厂所选用消谐装置的厂家、型号、端子接线。 5、谐振模拟实验时,为什么要在试验回路中串接灯泡或电阻? 一、什么叫铁磁谐振?(答案来自网络整理,仅供参考) 答:电力系统中有大量的电感电容储能元件,他们组成了许多串联或并联振荡回路,在正常的稳定状态下运行时,不可能产生严重的振荡,但当系统发生故障或由于某种原因电网参数发生了变化引起冲击扰动时,就很可能发生谐振,引起持续时间很长的过电压;电压互感器(PT)一类的电感元件在正常工作电压下,通常铁心磁通密度不高,铁心并不饱和,如在过电压下铁心饱和了,电感会迅速降低,从而与系统中的电容产生谐振,这时的谐振称作铁磁谐振。铁磁谐振不仅可在基频(50Hz)下发生,也可在高频(150Hz)、低频(17Hz、25Hz)下发生。二、PT产生铁磁谐振产生的原因以及危害?(答案来自网络整理,仅供参考) 答:正常运行时,PT开口三角的电压(3U0)理论上是0V,在实际中一般也不超过10V,系统发生单相接地故障时,3U0将迅速升高到30V,有时更高,达到120V时,形成过电压,当系统带电时,由于三相不同期等原因(存在有如瞬时接地故障等的现象,对系统造成冲击扰动),会在电压互感器中产生很大的谐波电流,导致互感器内部铁芯饱和,使二次侧的波形发生畸变,当畸变足够大时,就形成了铁磁谐振。另外也有因磁滞损耗和涡流损耗而形成谐振的情况。 在分频谐振(低于50Hz,比如16.667Hz、25Hz)时,一般过电压并不高,但是PT的电流大,易使电压互感器(PT)过热而爆炸;基波和倍频谐振(比如50Hz、150Hz)时,一般电流不大,但是过电压很高,常使设备绝缘损坏,引起电压互感器(PT)烧毁,造成恶性事故。

各种注射常见并发症预防和处理

各种注射常见并发症预防和处理

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