961 resultados para yolk color


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海水经济鱼类的养殖在我国已经形成第四次海水养殖浪潮,经济效益显著,有力地推动了我国海水养殖的产业结构调整和可持续发展。然而在海水养殖发展过程中也存在着诸多问题,尤其是早期发育阶段的高死亡率,严重制约了我国海水养殖产业的稳定和健康发展。 海水鱼类养殖的关键为高质量,高存活率苗种的生产和培育,由于鱼类种类繁多,生物多样性丰富,对应实际的繁育技术,尤其是新品种的开发,必须要做出相应的调整。这就要求我们必须对每一种鱼类早期发育有所了解,并将形态和组织上的数据用于指导生产。 本文通过显微观察和组织学研究,主要描述和研究了我国北方三种重要的海水经济鱼类(条斑星鲽、杂交鲆、条石鲷)的早期发育生物学,并结合实际生产进一步阐明关键期的产生原因,机理以及采用相应的对策。具体结果如下: 1.条斑星鲽:作为冷温性鲆鲽鱼类,条斑星鲽早期发育过程的特征主要有: ① 条斑星鲽受精卵无油球,卵子呈半浮性;不同步卵裂现象提前,发生在第三次卵裂;卵裂期裂球大小差异大。孵化过程较长,在水温8 ± 0.3℃,盐度33的条件下,经9 d孵化。条斑星鲽胚胎发育的不同时期对温度的敏感性不同,其中原肠期对温度比较敏感。 ②在8-10℃,盐度33的条件下,8-9 dph开口摄食。且开口时,其吻前端出现有一点状黑褐色素,构成了条斑星鲽仔鱼“开口期”的重要标志。卵黄囊于消失。在后期仔鱼末期,背鳍和臀鳍上形成特有的黑褐色条斑带。 ③杯状细胞首先出现在咽腔后部和食道前段,胃腺和幽门盲囊出现于29 dph,变态期始于30dph。在条斑星鲽早期发育过程中,观察到其直肠粘膜层细胞质出现大量嗜伊红颗粒,为仔鱼肠道上皮吸收的蛋白质。 ④首先淋巴化的免疫器官是头肾,然后是胸腺和脾脏,这与大部分硬骨鱼类不同。条斑星鲽除头肾和脾脏外,胸腺实质也形成MMCs。其中以脾脏形成MMCs最为丰富,形态多样。 2. 杂交鲆:为同属的牙鲆和夏鲆间的远缘杂交种,其发育过程的特点为: ① 在温度为15.4~16.0℃,杂交鲆胚胎从受精到孵化所需的时间为76 h左右,胚孔关闭前期,胚胎先出现视囊及克氏囊,而后形成体节。孵出前胚体在卵膜内环绕不到1周。 ② 孵化后消失。杂交鲆群体变态间隔长(34-60 dph),且变态高峰期出现的冠状幼鳍不明显(与母本牙鲆相比),数量为7-8根。 ③组织学观察发现,其消化系统中胃腺出现较晚,且胃腺发育过程缓慢(与母本牙鲆相比)。甲状腺滤泡增生不明显,颜色较浅,数量较少。杂交鲆在早期发育过程中,并没有出现鳔原基。 3. 条石鲷作为岩礁性的暖水性鱼类,早期发育过程也较为特殊,包括外形以及内部的器官结构。主要特点有: ① 受精卵:受精卵卵黄上具有龟裂结构,为鱼卵的分类特征之一。 ② 初孵仔鱼:初孵仔鱼背鳍膜上的黑色素,从体背面向背鳍膜边缘移动,到3dph仔鱼基本消失,此为本种仔鱼发育所特有的特点。 ③ 后期仔鱼和稚鱼:肠道肌肉层加厚明显,仔稚鱼胃肠排空率急剧上升,死亡率增加,通过改善常规的投饵方式部分解决了这个死亡高峰的问题。在幼鱼初期,牙齿融合为骨喙,为石鲷科鱼类的特征。 ④胸腺上皮分泌细胞:类似的现象同样在虹鳟鱼中发现,但是虹鳟鱼胸腺上皮分泌细胞不如条石鲷的丰富,同样也不如条石鲷的排列整齐,而是零星分布在胸腺上皮与咽腔接触的表面。除了正常的造血器官—脾脏和头肾外,肝脏、胰腺和鳔等多种组织等也出现MMCs,此现象在硬骨鱼类不多见,一般发生在软骨鱼类。

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Shrimps Litopenaeus vannamei with initial body weight of 2.108 +/- 0.036 g were sampled for specific growth rates (SGR) and body color measurements for 50 days under different light sources (incandescent lamp, IL; cool-white fluorescent lamp, FL; metal halide lamp, MHL; and control without lamp) and different illumination methods (illumination only in day, IOD, and illumination day and night, IDN). Body color of L. vannamei was measured according to the free astaxanthin concentration (FAC) of shrimp. The SGR, food intake (FI), feed conversion efficiency (FCE) and FAC of shrimps showed significant differences among the experimental treatment groups (P < 0.05). Maximum and minimum SGR occurred under IOD by MHL and IDN by FL, respectively (difference 56.34%). The FI of shrimp for the control group did not rank lowest among treatments, confirming that shrimp primarily use scent, not vision, to search for food. FI and FCE of shrimps were both the lowest among treatment groups under IDN by FL and growth was slow, thus FL is not a preferred light source for shrimp culture. Under IOD by MHL, shrimps had the highest FCE and the third highest FI among treatment groups ensuring rapid growth. FAC of shrimp were about 3.31 +/- 0.20 mg/kg. When under IOD by MHL and IDN by FL, FAC was significantly higher than the other treatments (P < 0.05). To summarize, when illuminated by MHL, L. vannamei had not only vivid body color due to high astaxanthin concentration but also rapid growth. Therefore, MHL is an appropriate indoor light source for shrimp super-intensive culture. SGR of shrimp was in significantly negative correlation to FAC of shrimp (P < 0.05). Thus, when FAC increased, SGR did not always follow, suggesting that the purpose of astaxanthin accumulation was not for growth promotion but for protection against intense light. (c) 2005 Elsevier B.V. All rights reserved.

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Amplified fragment length polymorphisms (AFLP) were used to study the inheritance of shell color in Argopecten irradians. Two scallops, one with orange and the other with white shells, were used as parents to produce four F-1 families by selfing and outcrossing. Eighty-eight progeny, 37 orange and 51 white, were randomly selected from one of the families for segregation and mapping analysis with AFLP and microsatellite markers. Twenty-five AFLP primer pairs were screened, yielding 1138 fragments, among which 148 (13.0%) were polymorphic in two parents and segregated in progeny. Six AFLP markers showed significant (P < 0.05) association with shell color. All six loci were mapped to one linkage group. One of the markers, F1f335, is completely linked to the gene for orange shell, which we designated as Orange1, without any recombination in the progeny we sampled. The marker was amplified in the orange parent and all orange progeny, but absent in the white parent and all the white progeny. The close linkage between F1f335 and Orange1 was validated using bulk segregation analysis in two natural populations, and all our data indicate that F1f335 is specific for the shell color gene, Orange1. The genomic mapping of a shell color gene in bay scallop improves our understanding of shell color inheritance and may contribute to the breeding of molluscs with desired shell colors.

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\0\05{\0\0\0\0\0\0\0\0 a uniform wall illuminated by a spot light often gives a strong impression of the illuminant color. How can it be possible to know if it is a white wall illuminated by yellow light or a yellow wall illuminated by white light? If the wall is a Lambertian reflector, it would not be possible to tell the difference. However, in the real world, some amount of specular reflection is almost always present. In this memo, it is shown that the computation is possible in most practical cases.

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IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 5, pp. 1338-1343, 2003.

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26 hojas : fotografías a color.

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47 hojas : ilustraciones, fotografías a color, muestras.

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13 hojas.

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11 hojas : ilustraciones, fotografías a color.

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27 hojas : ilustraciones, fotografías

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A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24% is observed in 17 out of 21 test sequences. In all but one case the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.