vendredi 4 septembre 2015

quel type d'intelligent êtes vous ?

http://quiztim.com/

§ Qu'est-ce que la géothermie ?


1- Le domaine continental et sa dynamique

En classe de première S, l'attention s'est portée principalement sur les domaines océaniques. On aborde ici les continents. Il s'agit de dégager les caractéristiques de la lithosphère continentale et d'en comprendre l'évolution à partir de données de terrain. La compréhension de la dynamique de la lithosphère devient ainsi plus complète.
© geothermie Lochwiller.odt

§ Qu'est-ce que la géothermie ?

§ Comment l'idée tectonique est-elle née ?


A.Wegener song : http://youtu.be/T1-cES1Ekto

1- La tectonique des plaques et l'histoire d'un modèle

§ Comment l'idée tectonique est-elle née ?

Introduction : La naissance d'une théorie au début du siècle

Activité 1/ Difficultés d’acceptation de l'idée de mobilité / Diapo

Construire une chronologie (date, noms, découverte) :
D/ Histoire_tectonique.odp
histoire de la tectonique des plaques à partir de

jeudi 3 septembre 2015

§ Qu'est-ce que la vie ?


Theme 1 – La Terre dans l'Univers, la vie et l'évolution du vivant : une planète habitée

§ Qu'est-ce que la vie ?

1.1 – Biodiversité & évolution

«la caractéristique la plus frappante de la Terre est sa vie, la caractéristique la plus frappante de la vie est sa diversité » David Tilman, Nature. 2000.

Activité 1/ Sortie dans le parc pour évaluer de la biodiversité / quadra

L'objectif de la sortie étant d'évaluer la biodiversité du parc, évitez de rester groupés en troupeau sur le même carré de pelouse et de prélever les mêmes échantillons que votre voisin(e) !
  1. Notez les caractéristiques du milieu : prairie, forêt, …
  2. Limitez la zone à analyser, à l'aide d'une ficelle et d'un mètre, un carré de 1m de côté
  3. Identifiez le Genre de chaque être vivant que vous trouvez sur 1m² (livres et fiches d'identifications à disposition au laboratoire) (n'oubliez pas les végétaux!)
clefs d'identification :
Pour le 5 novembre : herbier

A2/ Réalisation d'un herbier

Réaliser un herbier de 4 plantes herbacées avec détermination du nom commun + scientifique, date, lieu et milieu de récolte, notation en plus sur qualité du séchage et l'état de l'échantillon
exemples d'herbiers :
clefs de détermination :

1troduction

présentations
  • présentations
  • emploi du temps :
    • 2E :
    • 1S : 2 h cours + 1 h tp
    • TS : 2 h cours + 1,5 h tp
    • TS Spé : + 2 h  tp
  • autonomie
    • en tp => comptes-rendus
    • en cours => prise de notes
    • à la maison => devoirs du soir
  • prise de notes : un exercice cérébral difficile
    • écouter avec attention ≠ entendre avec distraction
    • sélectionner ce qu'il faut noter
    • écrire et placer les mots intelligemment
    • ne pas perdre le fil
    • ne jamais faire de phrases
    • ut abrév°
    • garder la mise en forme (sur/souligner, couleurs) pour le travail du soir
  • constitution du cahier ou classeur SVT :
    • cours : titres, mots clefs, bilans, schémas
    • comptes rendus de tp perso ou demandé
    • exos du manuel
    • devoirs,
    • fiches techniques
  • évaluation :
    • devoir / bac-blanc /20
    • compte-rendu de TP /10
    • contrôle continu / cours précédent / 5 sem
    • test QCM /10
  • devoirs du soir :
    • revoir ses notes avec le blog + manuel
    • préparer les devoirs (exercices du manuel)
    • faire des fiches de révision avec notes + manuel + blog
  • manuels : plan : intro-cours-bilans-exos-fiches techniques
  • plan :
Thématiques \ objectifs :
culture scientifique
esprit critique & citoyen
études supérieures
Terre, vie, évolution
Monde :
  • état
  • fonctionnement
  • histoire
explication
argumentation
observation
Recherche,
enseignement
environnement
problèmes humanité
responsabilités
  • individuelles
  • collectives
agronomie,
architecture,
gestion des ressources naturelles
humain et santé
organisme
  • fonctionnement
  • capacités
  • limites
responsabilités
  • individuelles
  • familiales
  • sociales
médecine,
odontologie,
diététique,
épidémiologie
 
programme
2nde
1ère
TS
spécialité
Thème 1 : évolution
terre & vivant
nature du vivant
biodiversité
évolution
génétique
tectonique
évolution & génétique
plantes angiospermes
dynamique continentale
Énergie cellulaire
Thème 2 :
ressources
énergie
sol
géologiques
agricoles
géothermie
plantes sélectionnées
Climats
Thème 3 :
santé
humain
coeur-poumon
régulation PA
muscle
sexualité
maladies
vision
immunologie
neurophysiologie
Glycémie et diabète


compétences : une combinaison de connaissances, capacités et attitudes
Manipuler et expérimenter.
Pratiquer une démarche scientifique : observer, questionner, formuler une hypothèse, expérimenter, raisonner avec rigueur, modéliser
Recenser, extraire et organiser des informations.
Relier phénomènes naturels et langage mathématique.
Relier sciences et techniques.
Développer sens de l’observation, curiosité, esprit critique.
Développer un esprit critique face aux ressources documentaires.
Comprendre la nature provisoire, en devenir, du savoir scientifique.
Comprendre qu’un effet peut avoir plusieurs causes.
Exprimer et exploiter des résultats, à l’écrit, à l’oral, en utilisant les technologies de l’information et de la communication.
Communiquer dans un langage scientifiquement approprié : oral, écrit, graphique, numérique.
S'Intéresser aux progrès scientifiques et techniques. & à la vie publique et les grands enjeux de la société.
Prendre Conscience de sa responsabilité face à l’environnement, la santé, le monde vivant. & de l’existence d’implications éthiques de la science.
Maîtriser son corps.
Respecter les règles de sécurité.
Choisir un parcours de formation.



dimanche 30 août 2015

Fig S1. Mycorrhizal Metabolome
Complete metabolism network KEGG map01100 has been highlighted to indicate expressed elements of the mycorrhizal metabolome. Nodes in this figure are metabolic compounds. Edges are enzymatic transformations. Highlighted edges indicate that an EC activity has been detected in mycorrhizal transcriptome. An edge highlighted with red indicates an expressed EC activity unique to the Laccaria transcriptomes. Green highlighted edges are unique to the aspen transcriptome. Nodes highlighted in yellow are sugar compounds likely transported by the set of statistically enriched sugar porters in Laccaria. Nodes highlighted in blue are amino acids, likely transporter by the set of statistically enriched amino acid transporters in both Laccaria and aspen. Nodes highlighted in pink are compounds that are likely to be metabolites directly shared between Laccaria and aspen metabolism during mycorrhizal interaction. Clicking on the name of a KEGG sub-network will display that subnetwork with EC activities present in the mycorrhizal metabolome highlighted in grey, EC activities that are uniquely expressed in aspen highlighted in green, and EC activities that are uniquely expressed in Laccaria highlighted in red.
Select species, Laccaria or aspen, by clicking "Laccaira KEGG Metabolism" or "Aspen KEGG Metabolism" below will set subsystems to link to species-specific gene and protein sequences when available.

Laccaria KEGG Metabolism

Global KEGG Metabolism

Aspen KEGG Metabolism

http://www.bio.anl.gov/molecular_and_systems_biology/MycorMetabolome/MycorrhizalMetabolome_Lac.html

International Consortium Builds ‘Google Map’ of Human Metabolism

Recon 2 is the most comprehensive virtual reconstruction of the human metabolic network to date, assemble by an international consortium of researchers, who liken it to a ‘Google map’ of metabolism. Image Credit: Anna Dröfn Daníelsdóttir, Freyr Jóhannsson, Soffía Jónsdóttir, Sindri Jarlsson, Jón Pétur Gunnarsson & Ronan M. T. Fleming from the University of Iceland.
Building on earlier pioneering work by researchers at the University of California, San Diego, an international consortium of university researchers has produced the most comprehensive virtual reconstruction of human metabolism to date. Scientists could use the model, known as Recon 2, to identify causes of and new treatments for diseases like cancer, diabetes and even psychiatric and neurodegenerative disorders. Each person’s metabolism, which represents the conversion of food sources into energy and the assembly of molecules, is determined by genetics, environment and nutrition.
The researchers presented Recon 2 in a paper published online March 3 in the journal Nature Biotechnology.
Doctors have long recognized the importance of metabolic imbalances as an underlying cause of disease, but scientists have been ramping up their research on the connection as a result of compelling evidence enabled by the Human Genome Project and advances in systems biology, which leverages the power of high-powered computing to build vast interactive databases of biological information.
“Recon 2 allows biomedical researchers to study the human metabolic network with more precision than was ever previously possible. This is essential to understanding where and how specific metabolic pathways go off track to create disease,” said Bernhard Palsson, Galletti Professor of Bioengineering at UC San Diego Jacobs School of Engineering.
“It’s like having the coordinates of all the cars in town, but no street map. Without this tool, we don’t know why people are moving the way they are,” said Palsson.
He likened Recon 2 to Google mapping for its ability to merge complex details into a single, interactive map. For example, researchers looking at how metabolism sets the stage for cancerous tumor growth could zoom in on the “map” for finely detailed images of individual metabolic reactions or zoom out to look at patterns and relationships among pathways or different sectors of metabolism. This is not unlike how you can get a street view of a single house or zoom out to see how the house fits into the whole neighborhood, city, state, country and globe.  And just as Google maps brings together a broad set of data – such as images, addresses, streets and traffic flow – into an easily navigated tool, Recon 2 pulls together a vast compendium of data from published literature and existing models of metabolic processes.
Researchers liken Recon 2 to Google mapping for its ability to merge complex details into a single, interactive map. For example, researchers looking at how metabolism sets the stage for cancerous tumor growth could zoom in on the “map” for finely detailed images of individual metabolic reactions or zoom out to look at patterns and relationships among pathways or different sectors of metabolism. Image Credit: Anna Dröfn Daníelsdóttir, Freyr Jóhannsson, Soffía Jónsdóttir, Sindri Jarlsson, Jón Pétur Gunnarsson & Ronan M. T. Fleming from the University of Iceland.
As a multi-scale representation of the human metabolic network, Recon 2 provides essential context for data being reviewed by researchers. Palsson and other scientists in the field have already successfully demonstrated the utility of such models in simple organisms such as yeast and E.coli. As a result, they have been able to engineer these organisms in the lab to improve the efficiency of ethanol production and predict drug resistance in bacteria.  
One of the most promising applications for the network reconstruction is the ability to identify specific gene expressions and their metabolic pathways for targeted drug delivery. Large gene expression databases are available for human cells that have been treated with molecules extracted from existing drugs as well as drugs that are in development. Recon 2 allows researchers to use this existing gene expression data and knowledge of the entire metabolic network to figure how certain drugs would affect specific metabolic pathways found to create the conditions for cancerous cell growth, for example. They could then conduct virtual experiments to see whether the drug can fix the metabolic imbalance causing the disease.
Palsson’s Systems Biology Research Group at UC San Diego built the first virtual reconstruction of the human metabolism network, known asRecon 1, in 2007 with a six-person team.  It featured more than 3,300 known biochemical reactions documented in over 50 years of metabolic research. Recon 2, which contains more than 7,400 reactions, was built by bringing together researchers from dozens of institutions around the globe in a series of “jamboree” meetings to refine and consolidate the data used in the reconstruction. Palsson said this jamboree approach helped the group establish common standards to build a consensus reconstruction, simplify its usability for biomedical researchers, and increase its transparency. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org.
Recon 2 is already proving its utility, according to Ines Thiele, a professor at the University of Iceland and UC San Diego alumna, who led the Recon 2 effort. Thiele earned her Ph.D. in bioinformatics as a student of Palsson’s and was part of the original Recon 1 team. Several other UC San Diego alumni, and former Palsson students, participated in the consortium from their new institutions, including Neema Jamshidi (Ph.D., 2008, M.D., 2009), who is now interning at UCLA; Jason Papin (Ph.D., 2004), a professor at the University of Virginia; and Nathan Price (Ph.D., 2005), who is a professor at the Institute for Systems Biology in Seattle, Wash.
Thiele said Recon 2 has successfully predicted alterations in metabolism that are currently used to diagnose certain inherited metabolic diseases.
“The use of this foundational resource will undoubtedly lead to a myriad of exciting predictions that will accelerate the translation of basic experimental results into clinical applications,” said Thiele. “Ultimately, I envision it being used to personalize diagnosis and treatment to meet the needs of individual patients. In the future, this capability could enable doctors to develop virtual models of their patients’ individual metabolic networks and identify the most efficacious treatment for various diseases including diabetes, cancer and neurodegenerative diseases.”
As much as Recon 2 marks a significant improvement over Recon 1, there is still much work to be done, according to the research team. Thiele said Recon 2 accounts for almost 1,800 genes of an estimated 20,000 protein-coding genes in the human genome. “Clearly, further community effort t will be required to capture chemical interactions with and between the rest of the genome,” she said.  
The international consortium includes researchers from the following institutions: University of Vienna, Austria; Charité– Universitätsmedizin Berlin, Germany; Jacobs University Bremen, Germany; Center for Systems Biology, University of Iceland; Okinawa Institute Science and Technology, Japan; Luxembourg Centre for Systems Biomedicine; University of Amsterdam, the Netherlands; Netherlands Bioinformatics Centre, the Netherlands; Vrije Universiteit, Amsterdam, the Netherlands; Netherlands Consortium for Systems Biology; Institute of Cell Biophysics, Moscow region, Pushchino, Russia; Chalmers University of Technology, Sweden; University of Manchester, United Kingdom; Manchester Institute of Biotechnology, UK; Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK; European Bioinformatics Institute, UK; Babraham Institute, Cambridge, UK; University of Warwick, UK; University of Edinburgh, UK; University of Sheffield, UK; University of California, San Diego; University of Virginia; Virginia Tech; Centre for Advanced Discovery and Experimental Therapeutics (CADET), University of North Texas; Genome Designs, Inc., Walnut Creek, Calif.; California Institute of Technology; and Institute for Systems Biology, Seattle, Wash.http://ucsdnews.ucsd.edu/pressrelease/international_consortium_builds_google_map_of_human_metabolism
International consortium builds 'Google Map' of human metabolism
An international consortium of university researchers has produced the most comprehensive virtual reconstruction of human metabolism to date. Researchers liken Recon 2 to Google mapping for its ability to merge complex details into a single, interactive map. For example, researchers looking at how metabolism sets the stage for cancerous tumor growth could zoom in on the “map” for finely detailed images of individual metabolic reactions or zoom out to look at patterns and relationships among pathways or different sectors of metabolism. Credit: Anna Dröfn Daníelsdóttir, Freyr Jóhannsson, Soffía Jónsdóttir, Sindri Jarlsson, Jón Pétur Gunnarsson & Ronan M. T. Fleming from the University of Iceland.
http://medicalxpress.com/news/2013-03-international-consortium-google-human-metabolism.html#jCp

Deciphering the metabolism of Plasmodium falciparum

Deciphering the metabolism of Plasmodium falciparum
Scientists are exploring the key proteins and metabolic pathways in Plasmodium falciparum to develop new drugs to prevent malaria. Pink block arrows indicate the steps inhibited by antimalarial drugs within the mitochondrion, cytosol, and food vacuole, and grey block arrows highlight potential drug targets.