Find research datasets worth reusing

FIGURES 68–74. Pupa. 68–70 in Descriptions of the developmental stages of Cafius nauticus (Fairmaire) (Coleoptera: Staphylinidae: Staphylininae), with comments on its biology

FIGURES 68–74. Pupa. 68–70. Habitus of pupa in dorsal (68), ventral (69) and lateral (70) aspect; 71–72. Microstructure of setiform projections on anterior margin of pronotum; 73–74. Microstructure of setiform projections on abdomen. Abbreviations: VP= ventral prolongations, FSP= functional spiracles, ASP= atrophied spiracles.

FIGURES 60–67. Abdominal segment IX, X and urogomphus. 60–62, 66–67 in Descriptions of the developmental stages of Cafius nauticus (Fairmaire) (Coleoptera: Staphylinidae: Staphylininae), with comments on its biology

FIGURES 60–67. Abdominal segment IX, X and urogomphus. 60–62, 66–67. First larval instar; 60. Abdominal segment IX and X, dorsal aspect; 61. Abdominal segment IX and X, ventral aspect; 62, 66. Urogomphus; 67. Microstructure of abdominal tergite X; 63–65. Mature larva instar; 63. Abdominal segment IX and X, dorsal aspect; 64. Abdominal segment IX and X, ventral aspect; 65. Urogomphus. Abbreviations: 1–30=setae, a–c=pores.

FIGURES 41–51. Thorax. 41–50 in Descriptions of the developmental stages of Cafius nauticus (Fairmaire) (Coleoptera: Staphylinidae: Staphylininae), with comments on its biology

FIGURES 41–51. Thorax. 41–50. First larval instar; 51. Third larval instar; 41, 51. Thorax in dorsal aspect; 42. Thorax in ventral aspect; 43–50. Seta on pronotum and abdominal tergite. Abbreviations: 1–27=setae, a–f=pores, CS=cervicosternum, ES=episternum, SP=spiracle.

FIGURES 33–36. Labium. 33–35 in Descriptions of the developmental stages of Cafius nauticus (Fairmaire) (Coleoptera: Staphylinidae: Staphylininae), with comments on its biology

FIGURES 33–36. Labium. 33–35. First larval instar; 36. Third larval instar; 33, 36. General appearance; 34. Apex portion of ligula; 35. Hypopharynx. Abbreviations: 1–2=setae, a–c=pores, LG=ligula, PMNT=prementum.

FIGURES 81–86 in Descriptions of the developmental stages of Cafius nauticus (Fairmaire) (Coleoptera: Staphylinidae: Staphylininae), with comments on its biology

FIGURES 81–86. Habits and habitats of Cafius nauticus. 81. Habitat of Cafius nauticus (Photo by Mr. Yu-Jie Cai from Guangdong, Shenzhen, Qiniang Mt., 26 May 2021); 82. Decaying algae on beach (Photo by Mr. Yu-Jie Cai from Guangdong, Shenzhen, Qiniang Mt., 26 May 2021); 83. Adult fly, larvae of which are potential prey for Cafius nauticus (Photo by Mr. Yu-Jie Cai from Guangdong, Shenzhen, Qiniang Mt., 23 Sep 2021); 84. Cafius nauticus, under a stone on beach (Photo by Mr. Yu-Jie Cai from Guangdong, Shenzhen, Qiniang Mt., 23 Sep 2021); 85. Mating behavior of Cafius nauticus (Photo by Mr. Yu-Jie Cai in laboratory, 25 Jun 2021); 86. Aggregative feeding behavior of Cafius nauticus (Photo by Mr. Yu-Jie Cai in laboratory, 5 Jul 2021).

FIGURES 1–12. Cafius nauticus. 1–3 in Descriptions of the developmental stages of Cafius nauticus (Fairmaire) (Coleoptera: Staphylinidae: Staphylininae), with comments on its biology

FIGURES 1–12. Cafius nauticus. 1–3. Egg; 1–2. Surface microstructure; 3. General appearance; 4–6. Habitus of first larval instar in dorsal (4), ventral (5) and lateral (6) aspect; 7–9. Habitus of second larval instar in dorsal (7), ventral (8) and lateral (9) aspect; 10–12. Habitus of third larva instar in dorsal (10), ventral (11) and lateral (12) aspect.

opennotspecifiedFeb 2016View details →

Fig. 2 in The phylogeny, evolutionary developmental biology, and paleobiology of the Deuterostomia: 25 years of new techniques, new discoveries, and new ideas

Fig. 2 The phylogeny and divergence times of the major deuterostome taxa. Shown is the consensus tree discussed in the text with each node labeled according to its relative support (see key). Most nodes are supported by abundant types of independent data (see text), but a few (e.g., Asterozoa), although supported by phylogenomics, are only weakly

dryad32/100

Data from: Loggerhead sea turtle embryos (Caretta caretta) regulate expression of stress-response and developmental genes when exposed to a biologically realistic heat stress

Open the record for dataset details and reuse information.

publicMar 2017View details →

dryad32/100

Data from: Developmental biology of the early Cambrian cnidarian Olivooides

Open the record for dataset details and reuse information.

publicJan 2017View details →

opennotspecifiedDec 2004View details →

Fig. 4 in Developmental Biology and Phenology of a Korean Native Dung Beetle, Copris ochus (Motschulsky) (Coleoptera: Scarabaeidae)

Fig. 4. Incidence of brood balls of C. ochus at different depths below ground.

opennotspecifiedDec 2004View details →

Fig. 3 in Developmental Biology and Phenology of a Korean Native Dung Beetle, Copris ochus (Motschulsky) (Coleoptera: Scarabaeidae)

Fig. 3. (a) Brood ball and egg of C. ochus; (b) 3rd instar larvae; (c) dorsal view of male of

opennotspecifiedDec 2004View details →

Fig. 1 in Developmental Biology and Phenology of a Korean Native Dung Beetle, Copris ochus (Motschulsky) (Coleoptera: Scarabaeidae)

Fig. 1. Average soil temperature at depths of 30 cm and 50 cm on Je-Ju island. (1995–2000).

opencc-by-4.0Feb 2023View details →

Plant developmental biology: how ROMI's tools help advance research in biology

The following video describes how phyllotaxis research in the science of plant development biology informs the development of ROMI's digital observational tools. Funded by EU Grant 773875. Videos are available in: <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> Video script: (VERNOUX) What i've been interested in for many years now is how does the the precise positioning of the flower at the the shoot tip, there's a structure that we call the meristem, where there are stem cells, that have the ability to generate new cells all the time. And this gives the property to this tissue to make the flowers. But the tissue doesn't make the flower at random position, it positions them very precisely accordingly to the other ones that are existing, and so then the problem is very similar to what I was describing before. You know this problem of emergence is that we think that the way this works, it gets positioned at a certain place because there are signals that exchange between all the flowers that exist, and the rest that will lead they will canalise the system, I mean into positioning the flower at this position, and actually also with a certain frequency. The type of phyllotaxis that Arabidopsis has is a spiral phyllotaxis, meaning that the flowers are produced one-by-one, and then distributed along the stem so that they produce a spiral. Like that the spiral can be perfect, meaning that it's really I mean follow perfectly the spiral, but you can now have also errors that could be small deviations from what you expect. But because the actual the angle between successive flowers is quite well controlled. So if you measure most plants like this the the that other spiral of your taxes the angle in between flowers will be close around the 37. So you can have small deviation of of the this angle but you can have also if you want mistakes in the sequence of angles. and so when you have this kind of observation one thing you could imagine doing, is taking this, I mean using this to actually go back to the genetic origin of why you don't have the same robustness and so then the big biological question very fundamental is really can I find genes that control the robustness of the architecture of an organism? In the the frame of ROMI this is really typically something we can do if if we have a phenotyping station and pipeline for analysis. It actually works well because and if it's high throughput because then what what you can do if you want to make it simpler you can take this these plants from two different origins you cross them and then in the progeny you can analyse them so that to know what is the how is the phyllotaxis how robust it is okay and so it's actually not the plants themselves you go for their progeny and then you look in the populations and then you can go and and map the origin of the genetic origin of the control. But for this you need to look at a lot of plants. (BESNARD) So here basically we will enter the culture chambers and so in those cultural chambers we are culturing uh four kinds of plants. So as I told you I am interested in phyllotaxis which is the angle between two successive organs and this is not random, and the plants built each new organs with a very precise fashion with a lot of regularities. But sometimes they make some errors in this, which are very indicative of how the plants build this. And also you can have mutants, then you invalidate the function of one gene and then you change the phenotype again and then, by this chain in phenotype you can try to go back to the function of the gene. But for this you need to measure the angles so to precisely measure it, so you need not to measure it only on two organs you need a lot of plants, a lot of organs, so it's a lot of tedious measurements. So what I’m doing now is i'm doing it by hand so I take one plant I put it here down here here at the top just to maintain it, I’m stretching it a bit, and here I have a protractor and with this protractor I move it along from organ to organ and I just measure the angles between successive organs. So this is really built for Arabidopsis Thaliana, for example but if you would have another plant for example this tool would not be appropriate. So you can see that it's very tedious, like for one plant it takes me more than 20 minutes, and then if I want to have like the length between the internals I need to go to another tool to measure it. So it's never ending yeah but that's all we did now so we would really be happy to have like a tool which is able to to grasp the 3d architecture in all directions. So that we have an object where we can turn around virtually which comes from the true plant. And then we can make accurate measurements of the angles of the length. and then i'm not like a specialist in one plant because if i have another plant like a Chenopodium that would not fit in this device because this is too tiny it's only built for Arabidopsis but then with my robot you can turn around and it can accept a lot of plants with a lot of different architecture. So this is like a prehistoric tool and with ROMI we hope to to move to the to the 20th century at last. The researcher in this natural environment! So what we can see here is six plants, and there have been born in the same environment, they are very close together and yet some of them are a bit bigger. And how do you explain this? Because a gene is different or because this slot here is too humid, or not enough? But at least there is something different in the phenotype at least, and the information about what your plant should look like is very important. And so again if I were to have to measure all the plants here, like a farmer might have to do for all the crops in the field, this would take a lot of time. And so if you could have a Robot that would say have a quantified measure, like ‘tardation’. Then If it is like eighty percent of my plants, not five percent, then I can postpone my harvest. (LEGRAND) So Fabrice as a Biologist of the project is interested in organ arrangements around the stem. And you can see here that we have the main stem and we have a lateral organs arranged around the stem. So that as you understand that if you try and measure this by hand, this is firstly not reproducible, and second subject to the research capacity to really look into the proper detail, or to take as much information as possible. Whereas if we do numerical reconstruction of the plant, you can apply a succession of different algorithms. Ones that would be to compute the angle to the successive organs around the plant. But if five years later you are interested in another internode length you can keep or take back this 3D reconstruction that you have made and then apply another algorithm that will quantify this internode length. So by transforming this biological data into numerical data, you can keep on working with the same data almost forever. That’s quite cool right? (VERNOUX) This kind of knowledge that we are generating is really fundamental. I think the kind of question that we are addressing is really close to the essence of life, i mean how do you construct and organism? It is true for plants, it is true for humans, it is true for animals, is that you start from one cell and in this all animals and plants are the same. And then you consider your way to construct a very complex structure from that, and so how can we go from something so simple to something so complex. So its a dream to understand something like this because if you can understand something like this you can understand at least a part of how life is constructed to maybe how we evolved on earth to how we diversify the shapes and etcetera etcetera.

openGEO-OpenApr 2013View details →

Gene Expression Analysis of Zebrafish Melanocytes, Iridophores, and Retinal Pigmented Epithelium Reveals Indicators of Biological Function and Developmental Origin

GEO Series GSE46387. Danio rerio. 35 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenDec 2022View details →

Mapping of clonal lineages across developmental stages in human neural differentiation (Biological Replicates #2 and #3)

GEO Series GSE221592. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenMar 2023View details →

Testing biological actions of medicinal plants from Northern Vietnam on zebrafish embryos and larvae: developmental, behavioral, and putative therapeutical effects.

GEO Series GSE207770. Danio rerio. 8 samples. Type: Expression profiling by high throughput sequencing.