Taxonomy is the branch of biology that classifies organisms into a hierarchical system of groups based on similarities. There are three domains - Bacteria, Archaea, and Eukarya. Within Eukarya are kingdoms including Plantae, Fungi, and Animalia. Taxonomy groups species into increasingly specific taxonomic ranks like domain, kingdom, phylum, class, order, family, genus, and species. The scientific name for a species includes its genus and specific epithet.
The Classification Of Living Organisms Ed205guest89f8df
This is the powerpoint I created for students to be able to interact with the icons on the project, to gain more knowledge about the classification of living organisms.
The Classification Of Living Organisms Ed205guest89f8df
This is the powerpoint I created for students to be able to interact with the icons on the project, to gain more knowledge about the classification of living organisms.
Taxonomy (or systematics) is basically concerned with the classification of organisms. Living organisms are placed in groups on the basis of similarities and differences at the organismic, cellular, and molecular levels.
Taxonomy (or systematics) is basically concerned with the classification of organisms. Living organisms are placed in groups on the basis of similarities and differences at the organismic, cellular, and molecular levels.
Taxonomyof Educational Objectives:
Cognitive Domain by Blooms which was revised by Anderson.
Affective Domain by Krathwol
Psychomotor Domain by Simpson and Harlow
In this chapter of Effective HR, Theories of Learning is explained. This chapter covers topics like understanding what learning is, the classification of learning capabilities and to understand the various theories of learning. This presentation on Effective HR is an initiative by Welingkar’s Distance Learning Division.
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Guided notes covering material from Topic 5.3 of the updated IB Biology syllabus for 2016 exams. Notes sequence and prompts are based on the Oxford IB Biology textbook by Allott and Mindorff.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
5. Taxonomy:
Hierarchical
Organization:
Domain
Kingdom
Phylum
Class
Order
Family
Genus
species
Species:
Panthera
pardus
Genus: Panthera
Family: Felidae
Order: Carnivora
Class: Mammalia
Phylum: Chordata
Kingdom: Animalia
Bacteria Domain: Eukarya Archaea
6. Fig. 1-14
Species Genus Family Order Class Phylum Kingdom Domain
Ursus americanus
(American black bear)
Ursus
Ursidae
Carnivora
Mammalia
Chordata
Animalia
Eukarya
7. Three Domain System
Land plants
Cellular slime moldsAmoebas
Fungi
EUKARYA
Euglena
Trypanosomes
Green algae
Forams
Red algae
Dinoflagellates
Ciliates
Diatoms
Animals
Leishmania
Green nonsulfur bacteria
Sulfolobus
Thermophiles
Halophiles
Methanobacterium
ARCHAEA
COMMON
ANCESTOR
OF ALL
LIFE
(Mitochondrion)
Spirochetes
Chlamydia
BACTERIA
Green
sulfur bacteria
Cyanobacteria
(Plastids, including
chloroplasts)
13. • Domain Bacteria are what you generally think
of when you think of bacteria - such as E. coli
and salmonella. The prefix 'eu' means 'true,' so
these are true bacteria.
15. • Domain Archaebacteria are known as ancient
bacteria. The prefix 'archae' means 'ancient,'
making this one easy to remember. They are
prokaryotic and unicellular.
24. ACTIVITY IN YOUR NOTEBOOK
• Describe in your own words the concept of
taxonomy.
• Describe hierarchical organization in
Taxonomy.
• Name the three principal domains in nature,
and give an example of each of them.
• Name the three kingdoms that belong to the
Eucarya domain.
• Explain the differences between the Eucaryotic
cell and prokaryotic cell.