两种大眼蟹线粒体16S rRNA基因序列分析

测定了宽身大眼蟹和日本大眼蟹线粒体16SrRNA基因部分片段的序列,其长度均为517bp。二者的核苷酸序列A,T,G,C的含量相似:宽身大眼蟹分别为33.3%,35.6%,19.9%,11.2%;日本大眼蟹分别为35.2%,35.4%,18.4%,11.0%。不包括6处插入/缺失位点,两序列间有63个变异位点,核苷酸差异率为12.26%,其中转换32个、颠换31个,转换与颠换比约为1.0。对国内外大眼蟹的24条长度为415bp的16SrRNA基因同源序列进行分析,A+T的平均含量为71.7%,明显高于G+C的平均含量,且存有变异位点171个,简约信息位点137个。中国和日本的日本大眼蟹之间的核苷酸差异率为4.42%,表明二者已有明显的遗传分化;中国的日本大眼蟹与日本的万岁大眼蟹之间的核苷酸差异率仅为0.21%,表明二者有可能为同一物种。上述结果得到了系统发生树拓扑结构的支持。     

基于深度卷积嵌入式聚类（DCEC）的海洋环境特征提取对渔情预报模型的改进研究

In order to improve the forecasting ability of the fishery forecast model for the longline bigeye tuna (Thunnus obesus), we proposed a marine environment feature extraction method based on deep convolutional embedded clustering (DCEC), combined with generalized additive model (GAM) for forecasting the longline bigeye tuna fishing grounds in the Southwest Indian Ocean. We used the MODIS-Aqua and MODIS-Terra sea surface temperature (SST) three-level inversion image data (in days) from January to December in 2018 at 0.041 6°×0.041 6° to construct a DCEC model, determined the optimal number of clusters based on the Davies-Bouldi index (DBI), and extracted the category feature value (F_M) of each month’s sea surface temperature (SST); we used monthly 1°×1° bigeye tuna longline fishery data from January to December in 2018 generated from the Indian Ocean Tuna Commission (IOTC), and calculated the catch per unit effort (CPUE); we matched the monthly category feature value F_M and the monthly average value of Chl a concentration with the CPUE data to construct an improved GAM; we matched the monthly average SST, the monthly average Chl a concentration and CPUE data to build a basic GAM; we used the joint hypothesis test (F test) to verify the influence of model explanatory variables; we used akaike information criterion (AIC), mean square error (MSE), and draw the frequency distribution diagrams and box diagrams of measured and predicted values, etc., to analysis the improvement effect of the improved GAM compared to the basic GAM. The results showed that: (1) the category feature value (F_M) extracted based on the DCEC model could better reflect the temporal and spatial dynamic characteristics of SST in the Southwest Indian Ocean, and was related with the climatic conditions, monsoon conditions, and hydrological characteristics in the Southwest Indian Ocean; (2) the factor interpretation of F_M was higher than that of the monthly average SST in GAM, which means F_M had more significant impact on the CPUE of bigeye tuna. The high catch rate was concentrated in the areas where the F_M category was 2, 10, 24 with intersections between the warm and cold currents; (3) the AIC of the improved GAM was reduced by 9.17% than that of the basic GAM and MSE of the improved GAM was reduced by 26.7% than that of the basic GAM; the frequency distribution of the CPUE logarithmic value predicted by the improved GAM was closer to the normal distribution, and the high frequency distribution interval was closer to that of the measured value; the scatter plot showed that the CPUE predicted by the improved GAM had a significant correlation with the measured CPUE, with r equaled to 0.60. This study proves the effectiveness of the DCEC model in extracting marine environmental features, and can provide a reference for the further study on the bigeye tuna fishery forecast.     

眼斑拟石首鱼的生化组成及预测模式

测定了养殖眼斑拟石首鱼肌肉及鱼体中的蛋白质、脂类、灰分和水分等生化组成。分析了肌肉蛋白质中17种常见氨基酸的含量。建立了以鱼体体重、体长、含水量与蛋白含量、脂肪含量、灰份、比能值的回归关系的预测模型。回归结果表明，眼斑拟石首鱼的鱼体组分与鱼体重、含水量间呈显著线性相关，而与全长则呈乘幂函数，基本呈立方关系，比能值与体重无相关性，其相关系数蛋白质＞水份＞灰分＞脂肪＞比能值。     

Typhoon Winnie （1997） with a Very Large Eye： High Resolution Numerical Simulation

Typhoon Winnie (1997) was one of the hurricanes that had extremely large eyewall ever recorded with a diameter of eyewall reaching 370 km. Using the Penn State University/National Center for Atmospheric Research mesoscale model MM5 with 3-km grid horizontal spacing on the finest nested mesh, Winnie was successfully simulated in terms of track, intensity, eye and concentric eyewalls. The dynamic and thermal structures of concentric eyewalls were studied based on the model output. It was found that the concentric eyewalls and their surrounding wind fields were asymmetric in observation as well as in simulation. Winnie's outer eyewall was associated with a maximum wind ring, a warm moist ring, and a high vorticity ring. The inner eyewall was associated with a secondary maximum wind ring and a warm moist ring. Upward motion dominated the whole layer of inner eyewall and the area above 2-km altitude of the outer eyewall. Downward motion was found inside the eye and the moat. Radial inflow happened in the boundary layer of the outer eyewall and the moat, but radial outflow dominated the middle and upper levels of the outer eyewall.     

基于GIS的印度洋大眼金枪鱼延绳钓钓获率与水温关系的研究

采用GIS定性分析和数值分析的方法，研究了印度洋大眼金枪鱼延绳钓钓获率与水温的关系。结果表明，大眼金枪鱼延绳钓高钓获率的出现与印度洋加权水温大面分布存在明显的相互关系，大眼金枪鱼的渔获适温在14～17℃间。建议测量200m水深处的水温作为海上生产时的参考。     

大眼鲷匹里虫病及匹里虫一新种的研究

从我国南海大眼鲷科(Prlacanthidae)的两种大眼鲷——长尾大眼鲷(Priacanthns tayenus)和短尾大眼鲷(P．macracanthus)上发现了一种匹里虫(新种)，定名为大眼鳎匹里虫(Pleistophora priacanthusis sp．nov．)。本文报道了感染鱼的征象、感染率和感染强度、感染部位和对寄主的影响、季节分布和性别差异的情况，并对大眼鲷匹里虫的孢囊、孢子、产孢体、新种的特征作了详细的描述。     

河蟹大眼幼体及Ⅰ期仔蟹变态成活率的研究

近年来从春节前后早繁的中华绒螯蟹 (Ｅｒｉｏｃｈｅｉｒｓｉｎｅｎｓｉｓ) (简称河蟹 )蟹苗到６月份的后期蟹苗 ,在大眼幼体培育成Ⅱ期仔蟹的过程中 ,均出现大批死亡现象。Ⅰ期仔蟹大量上滩死亡 ,给养殖者带来了巨大的损失 ,也给正常的养蟹生产带来了混乱 ,养殖大棚、池塘的利用率大大降低 ,养殖计划无法实施 ,经济损失巨大。国内对其研究有过一些报道 ,但均为单、双因子试验的结果［１ ,３］。作者在两年的研究过程中 ,比较了苗种质量、培育盐度、池水透明度、饵料品种４因子３水平下各因子对Ｍ～Ｃ２ 变态成活率的影响 ,借以找出影响Ｍ～…     

地球球观认知分析:以谷歌地球上的寻路任务为例

地理空间认知影响人类与空间相关的行为.本文提出了地球空间观(球观)的概念,以地球上不同区域找寻过程作为试验环境,研究被试者在数字地球球体(Google Earth)上如何进行定向、定位和目的地寻找,分析球观模式下对地球球体的空间认知过程.借助眼动仪记录的数据将被试者的读图过程可视化并且进行思维过程分析.结果发现,在地球球体上,结合了即时相对位置判断和方向定位的目的地搜寻过程是被试者球观建立过程的综合表现.     

台风眼壁的云结构与降水形成机制分析

使用带有详细微物理过程的ARPS模式,对台风韦帕（Wipha）进行三重嵌套细网格模拟,利用模式结果,对台风眼壁强降水中心的云结构和降水形成机制进行分析,结果表明：冰相微物理过程是启动和形成台风眼壁暴雨的主要降水形成机制。在9000～14000 m高空,云水在很低的温度下经均质核化产生冰晶,或经非均质核化形成云冰;冰晶通过凝华增长（psfi,贝吉龙过程）、雨水收集云冰产生雪（praci）和冰晶粘附雨水成雪（piacr）过程生长为雪;霰产生主要包括4个过程：冰晶接触雨水使其成霰（piacr）、雪撞冻云水使其成霰（psacr）、雨水收集云冰转化成霰（praci）或雨水冻结为霰（pgfr）;霰粒子通过收集云冰干增长（dgaci）,霰撞冻云滴增长（dgacw）等过程生长;霰融化（pgmlt）和雪融化（psmlt）成雨水后再通过碰并云水等暖云生长过程,最后形成雨水。霰过程的强弱在雨水形成机制中很重要。（29.5°N、121.8°E）和（28.3°N、120.4°E）强降水中心冰晶转化率没有太大差别,但是（29.5°N、121.8°E）强降水中心上空冰晶通过贝吉龙过程快速成长为雪和霰,霰粒子增长过程远远强于（28.3°N、120.4°E）强降水中心,低空又有较高的云水转化率,使降水粒子在暖云中继续快速生长,冷暖云过程的有利配置使（29.5°N、121.8°E）出现较强雨水转化率。