Social factors like class, ethnicity, gender, age, and education influence language variation. Labov's study found class affected pronunciation patterns, with upper classes using standard variants and lower classes using non-standard variants. Ethnic groups develop language varieties through substrates and adstrates. Gender influences language choice, with men using more direct and non-standard forms while women use more standard forms. Younger generations adopt new slang that differs from older generations. Education level also impacts language, with more educated speakers using standard dialects. These social dimensions are core to understanding sociolinguistic variation.
Creole and Pidgin Languages. General CharacteristicsMarina Malaki
This PPT presents Pidgin and Creole Languages, its general characteristics, as well as some peculiar features, varieties and examples. Hope you'd like it! Enjoy!
Critical Language Awareness commonly described CLA is a prerequisite technique to Critical Discourse Analysis. CLA is primarily an understanding that makes us competent socially, politically, ideologically and among various discourses and contexts of different linguistic variations.
Creole and Pidgin Languages. General CharacteristicsMarina Malaki
This PPT presents Pidgin and Creole Languages, its general characteristics, as well as some peculiar features, varieties and examples. Hope you'd like it! Enjoy!
Critical Language Awareness commonly described CLA is a prerequisite technique to Critical Discourse Analysis. CLA is primarily an understanding that makes us competent socially, politically, ideologically and among various discourses and contexts of different linguistic variations.
The results of our group discussion on sociolinguistics. We take this material from several book references. We uploaded this presentation with the aim that we can learn together especially sociolinguistics. We hope that readers can understand the contents of the material. There are many mistakes please forgive us. Thank you.
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.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's entangled aventures in wonderlandRichard 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.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
1. GHAZI UNIVERSITY,
DERA GHAZI KHAN
Sociolinguistics
Topic: Social Factors Governing Language Variation.
Submitted to : Sir Maqsood Alam Rizvi
Submitted by : Zara Naseer
Roll no: 11
Department of English
M.Phil Linguistics
Semester 2nd
2. Introduction:
Variation is a characteristic of language: there is more than one way of
saying the same thing. Speakers may vary pronunciation (accent), word
choice (lexicon), or morphology and syntax (sometimes called "grammar").
Language variation is a core concept in sociolinguistics, to the extent that it
requires reference to social factors. Language varies from one place to
another, from one social group to another and from one situation to another.
It is universal characteristic of human language that speakers of the same
language who live in different parts of a continuous territory do not speak in
the same way, the speech of each locality differs in some features from the
speech of each neighboring locality.
And the thing that social forces contribute to linguistic change is also clear.
Younger people alter their speech so as to differentiate themselves from their
elders. Islanders want to sound different from mainlanders. Middle-class
speakers attempt to sound different from the working class, and vice versa.
Men and women tend to speak the way that their sex is “supposed to” speak.
In the end, sometimes consciously and Sometimes unconsciously, people
speak like the people they want to think of themselves as being; linguistic
differentiation is a matter of the presentation of self in everyday life.
3. Social Factors Governing Language
Variation:
Class, ethnicity, and gender, age, and education
are main social factors that play a role in language
variation. Class is the structure of relationships
between groups where people are classified based
on their education, occupation, and income.
Ethnicity refers to a group of people that share
cultural characteristics and gender deals with the
traits associated with men and women. This
division among groups in each factor contributes to
the differences of their use of the English
language.
4. Class:
Labov’s study addresses and depicts how class, ethnicity, and gender
influences language variation. One example of how class affects
language variation is evident in the New York City study by Labov.
He displays the social classes in four classes: the lower working
class, the upper working class, the lower middle class, and the upper
middle class. He also displays the styles of speech in three styles,
which are casual, careful conversation, and reading. According to the
data, the upper middle class speakers almost always use the
standard ing variant and the lower working class speakers almost
always use the non-standard in variant. Each class prefers the use of
one pronunciation over the other regardless, of the style of speech.
However, the lower working class shifted from using in in casual
speech to using ing in the reading style.
The middle class uses more formal or “elaborate” code whereas the
work class uses public or “restricted” codes.
5. Ethnic Groups:
Ethnic groups affect language variation, because they
usually have to learn the language that is prominent in an
area. Although they view language as a part of their
identity, they have to compromise their languages and
substitute it with another, or combine both languages.
Ethnic groups learn the dominant language in an area when
the majority of the people speak that language. Their
variety of the dominant language is called the "substrate,"
because it shows the differences between it and their
language.
Immigrants that arrive in a new location quickly learn the
dominant language. Their language is called the "adstrate,"
and it affects the way they speak the dominant language.
The adstrate and substrate could create a variety of the
dominant language, and would differ from the normal
version of the language.
6. Gender:
Gender affects language variation by influencing the
language choice between men and women. A man and a
woman’s speech differ from one another in matters of
degree. Men’s language can be direct, non-standard, and
aggressive. Women’s language can be less harsh,
emotional, and standard. Many languages have alternative
forms that are used only by men or by women. In some
cases, the men and women speak different dialects, or they
don't speak the same language to each other. Trudgill
concludes that women consistently use forms which more
closely approach those of the standard variety or the
prestige accent than those used by men. In other words,
female speakers of English use linguistic forms which are
considered to be better than male forms. It also makes it
difficult for each gender to fully understand the opposite
sex.
7. Speaker’s age/Generation
Gap:
Age can determine how English learners express themselves and you as a
teacher can easily see the difference in language variation if you teach a
class of children as opposed to a class of adult learners. . As Ali leaves behind
his teenage years and becomes an adult, he slowly leaves behind the old
ways of expression he used to have both for oral and written discourse. For
example, as Ali joins college, he gets used to more formal ways of
expressions with people. When he greets people he no longer feels
comfortable saying “Hey.. what's up?”. Instead he prefers “Hi good morning”.
This is not just because Ali is in college. This is also because he is mature
now and he spontaneously feels more comfortable using more formal
language.
We often come across the term generation gap. There are obvious differences
between the younger and older generations and these quite naturally are
reflected in speech. We often hear older people complain about the strange
language forms that youngsters pick up and use. These different forms are
usually no more than transient expression taken from pop culture which last
only while they are in vogue. These is also a difference in word frequency,
different generation tend to use certain words and expressions more than
others.
8. Education:
Education is another social factor which plays
an important role in language variation.
Within each dialect area, one notices linguistic
variation according to education and social
standing. The uneducated speech is most
easily identified with the regional dialect,
while educated speech tends to transcend
regional limitations. Assiri(2008) claimed that
speakers’ level of education is affecting the
choice speakers make between using standard
and non standard language.
9. Conclusion:
To conclude we can say that, variation in language is
an important topic in sociolinguistics, because it
refers to social factors in society and how each
factor plays a role in language varieties. Languages
vary between ethnic groups, social situations, and
specific locations. From Labov’s study, people can
determine that variation is a characteristic of
language that can be influenced by class, ethnicity,
gender, and education. People notice these
variations by interacting with people from different
ethnic backgrounds and people with different social
standings. According to his research, Labov realized
that there are many ways of speaking, and each
way of speaking is influenced by social factors in a
society.