Klout as an Example Application of Topics-oriented NLP APIsTyler Singletary
Klout in its iterations is a prime example of leveraging large scale NLP data science with topical assignment. Klout makes this available through its website, http://klout.com, and also through its developer API, http://developers.klout.com
We believe open annotation is a unique new capability that has the potential to radically transform the way we engage with scientific content across the web. Not only annotations are central in the realization of the web of documents by facilitating the formalization and discovery of relations across papers. Most importantly, annotations, we argue, are central in the empowerment of communities of practices by making content based conversations possible. In addition, the activity arising from such content based exploration allows for the definition of a novel alternative metric. As the annotation is specific to a part of the text, it allows for granular analysis of the paper; such metric tells us not just the number of tweets or LIKEs for a given document. It also allows us to identify the topics that are arising interest and how are these being discussed. The contribution is therefore twofold; on the one hand NanoTweets are a type of community based annotation, on the other, hand, NanoTweets are also delivering a granular metric rooted within the content of the document -simplifying content based business intelligence.
Emotional Social Signals for Search RankingIsmail BADACHE
A large amount of social feedback expressed by social signals (e.g. like, +1, rating) are assigned to web resources. These signals are often exploited as additional sources of evidence in search engines. Our objective in this paper is to study the impact of the new social signals, called Facebook reactions (love, haha, angry, wow, sad) in the retrieval. These reactions allow users to express more nuanced emotions compared to classic signals (e.g. like, share). First, we analyze these reactions and show how users use these signals to interact with posts. Second, we evaluate the impact of each such reaction in the retrieval, by comparing them to both the textual model without social features and the first classical signal (like-based model). These social features are modeled as document prior and are integrated into a language model. We conducted a series of experiments on IMDb dataset. Our findings reveal that incorporating social features is a promising approach for improving the retrieval ranking performance.
Fresh and Diverse Social Signals: Any Impacts on Search?Ismail BADACHE
In this paper, we extensively study the impact of social signals (users' actions) obtained from several social networks on search ranking task. Social signals associated with web resources (documents) can be considered as an additional information that can play a vital role to estimate a priori importance of these resources. Particularly, we are interested in the freshness of signals and their diversity. We hypothesize that the moment (the date) when the user actions occur and the diversity of actions may impact the search performance. We propose to model these heterogeneous social features as document prior. We evaluate the effectiveness of our approach by carrying out extensive experiments on two different INEX datasets, namely SBS and IMDb, enriched with several social signals collected from social networks. Our experimental results consistently demonstrate the interest of integrating fresh and diverse signals in the retrieval process.
In this talk we shall introduce the main ideas of TruSIS (Trust in Social Internetworking System), a Marie Curie Fellowhsip financed by European Union and hosted at VU University, Department of Computer Science, Business and Web group. The goal of TruSIS is to study the baheviour of users who affiliate to multiple social networking sites and
are active in them (e.g., users may publish personal profiles on sites like MySpace and post videos on sites like YouTube). We briefly called this scenario as SIS (Social Internetworking system).
As a first research contribution, we implemented a crawler to gather data about users and link their profiles on multiple social networking websites. To this purpose we used Google Social Graph API, a powerful API released by Google in 2008. We obtained a sample of about 1.3 millions of user accounts and 36 millions of connections between them.
Parameters from social network theory (like average clustering coefficient, network modularity and so on) were used to study the structural properties of the gathered sample and how these properties depend on user behavious.
A second contribution is about the computation of distance between two users in a SIS on the basis of their social ties. We used a popular parameter from Social Network Theory known as Katz coeffcient and
provide a computationally afficient approach to computing Katz coefficient which relies on the usage of a popular tool from linear algebra known as Sherman- Morrison formula.
Finally, we shall describe our work on extending the notion of trust from single social networks to a SIS. We describe the main research challenges tied to the definition of trust and how they relate to Semantic Web technologies.
Klout as an Example Application of Topics-oriented NLP APIsTyler Singletary
Klout in its iterations is a prime example of leveraging large scale NLP data science with topical assignment. Klout makes this available through its website, http://klout.com, and also through its developer API, http://developers.klout.com
We believe open annotation is a unique new capability that has the potential to radically transform the way we engage with scientific content across the web. Not only annotations are central in the realization of the web of documents by facilitating the formalization and discovery of relations across papers. Most importantly, annotations, we argue, are central in the empowerment of communities of practices by making content based conversations possible. In addition, the activity arising from such content based exploration allows for the definition of a novel alternative metric. As the annotation is specific to a part of the text, it allows for granular analysis of the paper; such metric tells us not just the number of tweets or LIKEs for a given document. It also allows us to identify the topics that are arising interest and how are these being discussed. The contribution is therefore twofold; on the one hand NanoTweets are a type of community based annotation, on the other, hand, NanoTweets are also delivering a granular metric rooted within the content of the document -simplifying content based business intelligence.
Emotional Social Signals for Search RankingIsmail BADACHE
A large amount of social feedback expressed by social signals (e.g. like, +1, rating) are assigned to web resources. These signals are often exploited as additional sources of evidence in search engines. Our objective in this paper is to study the impact of the new social signals, called Facebook reactions (love, haha, angry, wow, sad) in the retrieval. These reactions allow users to express more nuanced emotions compared to classic signals (e.g. like, share). First, we analyze these reactions and show how users use these signals to interact with posts. Second, we evaluate the impact of each such reaction in the retrieval, by comparing them to both the textual model without social features and the first classical signal (like-based model). These social features are modeled as document prior and are integrated into a language model. We conducted a series of experiments on IMDb dataset. Our findings reveal that incorporating social features is a promising approach for improving the retrieval ranking performance.
Fresh and Diverse Social Signals: Any Impacts on Search?Ismail BADACHE
In this paper, we extensively study the impact of social signals (users' actions) obtained from several social networks on search ranking task. Social signals associated with web resources (documents) can be considered as an additional information that can play a vital role to estimate a priori importance of these resources. Particularly, we are interested in the freshness of signals and their diversity. We hypothesize that the moment (the date) when the user actions occur and the diversity of actions may impact the search performance. We propose to model these heterogeneous social features as document prior. We evaluate the effectiveness of our approach by carrying out extensive experiments on two different INEX datasets, namely SBS and IMDb, enriched with several social signals collected from social networks. Our experimental results consistently demonstrate the interest of integrating fresh and diverse signals in the retrieval process.
In this talk we shall introduce the main ideas of TruSIS (Trust in Social Internetworking System), a Marie Curie Fellowhsip financed by European Union and hosted at VU University, Department of Computer Science, Business and Web group. The goal of TruSIS is to study the baheviour of users who affiliate to multiple social networking sites and
are active in them (e.g., users may publish personal profiles on sites like MySpace and post videos on sites like YouTube). We briefly called this scenario as SIS (Social Internetworking system).
As a first research contribution, we implemented a crawler to gather data about users and link their profiles on multiple social networking websites. To this purpose we used Google Social Graph API, a powerful API released by Google in 2008. We obtained a sample of about 1.3 millions of user accounts and 36 millions of connections between them.
Parameters from social network theory (like average clustering coefficient, network modularity and so on) were used to study the structural properties of the gathered sample and how these properties depend on user behavious.
A second contribution is about the computation of distance between two users in a SIS on the basis of their social ties. We used a popular parameter from Social Network Theory known as Katz coeffcient and
provide a computationally afficient approach to computing Katz coefficient which relies on the usage of a popular tool from linear algebra known as Sherman- Morrison formula.
Finally, we shall describe our work on extending the notion of trust from single social networks to a SIS. We describe the main research challenges tied to the definition of trust and how they relate to Semantic Web technologies.
Invited talk at Session on Semantic Knowledge for Commodity Computing, at Microsoft Research Faculty Summit 2011, July 19-20, 2011, Redmond, WA. http://research.microsoft.com/en-us/events/fs2011/default.aspx
Associated video at: https://youtu.be/HKqpuLiMXRs
"Mass Surveillance" through Distant ReadingShalin Hai-Jew
Distant reading refers to the uses of computers to “read” texts by counting words, identifying themes and subthemes (through topic modeling), extracting sentiment, applying psychological analysis to the author(s), and otherwise finding latent or hidden insights. This work is based on research on “mass surveillance” based on five text sets: academic, mainstream journalism, microblogging, Wikipedia articles, and leaked government data. The purpose was to capture some insights about the collective social discussions occurring around this issue in an indirect way. This presentation uses a variety of data visualizations (article network graphs, word trees, dendrograms, treemaps, cluster diagrams, line graphs, bar charts, pie charts, and others) to show how machines read and the types of summary data they enable (at computational speeds, at machine scale, and in a reproducible way). Also, some computational linguistic analysis tools enable the creation of custom dictionaries for unique types of applied research. The tools used in this presentation include NVivo 11 Plus and LIWC2015.
Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A compreh...Amit Sheth
Amit Sheth, "Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness", Keynote at
From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Falls Church, VA, February 17, 2009. http://semanticommunity.wik.is/
Hashtag Conversations,Eventgraphs, and User Ego Neighborhoods: Extracting So...Shalin Hai-Jew
This introduces methods for extracting and analyzing social network data from Twitter for hashtag conversations (and emergent events), event graphs, search networks, and user ego neighborhoods (using NodeXL). There will be direct demonstrations and discussions of how to analyze social network graphs. This information may be extended with human- and / or machine-based sentiment analysis.
Pavan Kapanipathi's talk at IBM's Frontiers of Cloud Computing and Big Data Workshop 2014. http://researcher.ibm.com/researcher/view_group_subpage.php?id=5565
Due to the increased adoption of social web, users, specifically Twitter users are facing information overload. Unless a user is willing to restrict the sources (eg number of followings), important information relevant to users' interests often go unnoticed. The reasons include (1) the postings may be at a time the user is not looking for; (2) the user unaware and hence not following the information source; (3) and the information arrives at a rate at which the user cannot consume. Furthermore, some information that are temporally relevant, discovered late might be of no use.
My research addresses these challenges by
(1) Generating user profiles of interests from Twitter using Wikipedia. The interests gleaned from users' Twitter data can be leveraged by personalization and recommendation systems in order to reduce information overload/Volume for users.
(2) Filtering twitter data relevant to dynamically evolving entities. Including Volume, this addresses the velocity challenge in delivering relevant information in real-time. The approach is deployed on Twitris to crawl for dynamic event-relevant tweets for analysis. The prominent aspect of the approaches is the use of crowd-sourced knowledge-base such as Wikipedia.
Invited talk at Session on Semantic Knowledge for Commodity Computing, at Microsoft Research Faculty Summit 2011, July 19-20, 2011, Redmond, WA. http://research.microsoft.com/en-us/events/fs2011/default.aspx
Associated video at: https://youtu.be/HKqpuLiMXRs
"Mass Surveillance" through Distant ReadingShalin Hai-Jew
Distant reading refers to the uses of computers to “read” texts by counting words, identifying themes and subthemes (through topic modeling), extracting sentiment, applying psychological analysis to the author(s), and otherwise finding latent or hidden insights. This work is based on research on “mass surveillance” based on five text sets: academic, mainstream journalism, microblogging, Wikipedia articles, and leaked government data. The purpose was to capture some insights about the collective social discussions occurring around this issue in an indirect way. This presentation uses a variety of data visualizations (article network graphs, word trees, dendrograms, treemaps, cluster diagrams, line graphs, bar charts, pie charts, and others) to show how machines read and the types of summary data they enable (at computational speeds, at machine scale, and in a reproducible way). Also, some computational linguistic analysis tools enable the creation of custom dictionaries for unique types of applied research. The tools used in this presentation include NVivo 11 Plus and LIWC2015.
Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A compreh...Amit Sheth
Amit Sheth, "Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness", Keynote at
From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Falls Church, VA, February 17, 2009. http://semanticommunity.wik.is/
Hashtag Conversations,Eventgraphs, and User Ego Neighborhoods: Extracting So...Shalin Hai-Jew
This introduces methods for extracting and analyzing social network data from Twitter for hashtag conversations (and emergent events), event graphs, search networks, and user ego neighborhoods (using NodeXL). There will be direct demonstrations and discussions of how to analyze social network graphs. This information may be extended with human- and / or machine-based sentiment analysis.
Pavan Kapanipathi's talk at IBM's Frontiers of Cloud Computing and Big Data Workshop 2014. http://researcher.ibm.com/researcher/view_group_subpage.php?id=5565
Due to the increased adoption of social web, users, specifically Twitter users are facing information overload. Unless a user is willing to restrict the sources (eg number of followings), important information relevant to users' interests often go unnoticed. The reasons include (1) the postings may be at a time the user is not looking for; (2) the user unaware and hence not following the information source; (3) and the information arrives at a rate at which the user cannot consume. Furthermore, some information that are temporally relevant, discovered late might be of no use.
My research addresses these challenges by
(1) Generating user profiles of interests from Twitter using Wikipedia. The interests gleaned from users' Twitter data can be leveraged by personalization and recommendation systems in order to reduce information overload/Volume for users.
(2) Filtering twitter data relevant to dynamically evolving entities. Including Volume, this addresses the velocity challenge in delivering relevant information in real-time. The approach is deployed on Twitris to crawl for dynamic event-relevant tweets for analysis. The prominent aspect of the approaches is the use of crowd-sourced knowledge-base such as Wikipedia.
Building a Biomedical Knowledge Garden Benjamin Good
Describes the tribulations of building a large biomedical knowledge graph. Provides a comparison between the UMLS and Wikidata in terms of content and structure. Concludes with the idea of anchoring the knowledge graph in Wikidata items and properties.
ECIR23: A Streaming Approach to Neural Team Formation TrainingHossein Fani
Predicting future successful teams of experts who can effectively collaborate is challenging due to the experts’ temporality of skill sets, levels of expertise, and collaboration ties, which is overlooked by prior work. Specifically, state-of-the-art neural-based methods learn vector representations of experts and skills in a static latent space, falling short of incorporating the possible drift and variability of experts’ skills and collaboration ties in time. In this paper, we propose (1) a streaming-based training strategy for neural models to capture the evolution of experts’ skills and collaboration ties over time and (2) to consume time information as an additional signal to the model for predicting future successful teams. We empirically benchmark our proposed method against state-of-the-art neural team formation methods and a strong temporal recommender system on datasets from varying domains with distinct distributions of skills and experts in teams. The results demonstrate neural models that utilize our proposed training strategy excel at efficacy in terms of classification and information retrieval metrics. The codebase is available at https://github.com/fani-lab/OpeNTF/tree/ecir24.
SEKE15: An ontology for describing security eventsHossein Fani
Mining security events helps with better precautionary planning for community safety. However, incident records are expressed in diverse and application dependent formats which impedes common comprehension for automatic knowledge extraction and reasoning. In this paper, we present Security Incident Ontology, SIO, a novel light-weight domain ontology for security incidents. We use Timeline to annotate the temporal facts of incidents and adopt Event to represent any security issues from indecent behavior to assault to more adverse crime which raises the security alarm in a community. It will present a unique way to the security incident detectors, a police officer, Robocops, or intelligent CCTV cameras, to report security events. We use SIO in populating security incident notifications of Integrated Risk Management (IRM) at Ryerson University to evaluate its competency, for Ryerson University campus has both business and housing area in the vicinity and encompass not only high rate, but also a wide variety of different security issues. SIO is developed in OWL 2 with Protégé.
ECIR20: Temporal Latent Space Modeling for Community PredictionHossein Fani
We propose a temporal latent space model for user community prediction in social networks, whose goal is to predict future emerging user communities based on past history of users’ topics of interest. Our model assumes that each user lies within an unobserved latent space, and similar users in the latent space representation are more likely to be members of the same user community. The model allows each user to adjust its location in the latent space as her topics of interest evolve over time. Empirically, we demonstrate that our model, when evaluated on a Twitter dataset, outperforms existing approaches under two application scenarios, namely news recommendation and user prediction on a host of metrics such as mrr, ndcg as well as precision and f-measure.
CIKM17: temporally like-minded user community identification through neural ...Hossein Fani
We propose a neural embedding approach to identify temporally
like-minded user communities, i.e., those communities of users who have similar temporal alignment in their topics of interest. Like-minded user communities in social networks are usually identified by either considering explicit structural connections between users (link analysis), users’ topics of interest expressed in their posted contents (content analysis), or in tandem. In such communities, however, the users’ rich temporal behavior towards topics of interest is overlooked. Only few recent research efforts consider the time dimension and define like-minded user communities as groups of users who share not only similar topical interests but also similar temporal behavior. Temporal like-minded user communities find application in areas such as recommender systems where relevant items are recommended to the users at the right time. In this paper, we tackle the problem of identifying temporally like-minded user communities by leveraging unsupervised feature learning (embeddings). Specifically, we learn a mapping from the user space to a low-dimensional vector space of features that incorporate both topics of interest and their temporal nature. We demonstrate the efficacy of our proposed approach on a Twitter dataset in the context of three applications: news recommendation, user prediction and community selection, where our work is able to outperform the state-of-the-art on important information retrieval metrics.
CIKM AnalytiCup 2017: Bagging Model for Product Title Quality with NoiseHossein Fani
To stand out from the crowd, sellers employ creative, sometimes disruptive titles for their products in online stores to improve their search relevancy or attract the attention of customers. As a part of the CIKM AnalytiCup 2017, the challenge is to build a product title quality model that can automatically grade the clarity and the conciseness of a product title. Our proposed “Bagging Model for Product Title Quality with Noise” could leave others behind in performance and become the winner of the CIKM Cup 2017 competition.
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.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
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.
(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.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
2. TREND
DETECTION, TRACKING & TRANSITION
in Social Networks
1. Definition & General Idea
2. Web Samples in Trend Hunting
3. Detection Approches
4. Architecture: TwitterMonitor
5. Detection: MemeTracker
6. Classification: ExoEndo
SemioNet: Semantic Social Network Analysis
3. REFERENCES
Mathioudakis, Michael, and Nick Koudas. "Twittermonitor: trend
detection over the twitter stream." Proceedings of the 2010 ACM
SIGMOD International Conference on Management of data. ACM,
2010.
Leskovec, Jure, Lars Backstrom, and Jon Kleinberg. "Meme-tracking
and the dynamics of the news cycle." Proceedings of the 15th ACM
SIGKDD international conference on Knowledge discovery and
data mining. ACM, 2009.
Naaman, Mor, Hila Becker, and Luis Gravano. "Hip and trendy:
Characterizing emerging trends on Twitter." Journal of the American
Society for Information Science and Technology 62.5 (2011): 902-
918.
Becker, Hila, Mor Naaman, and Luis Gravano. "Beyond Trending
Topics: Real-World Event Identification on Twitter." ICWSM 11 (2011):
438-441.
4. Trend Analysis
The Science of Studying
Changes in Social Patterns,
Including Fashion, Technology
& Consumer Behavior
Horizontal Analysis
The General Movement
over TIME of a
Statistically Detectable
Change
Fundamentally, a Method
for Understanding HOW &
WHY Things have Changed
– or will Change – over TIME
6. APPROCH
Text Mining
Topic Ident. & Clust.
"Kilroy was here" was a
piece of graffiti that
became popular in the
1940s, and existed under
various names in
different countries,
illustrating how a meme
can be modified through
replication
Memes
(/ˈmiːm/) is "an idea, behavior, or
style that spreads from person to person
within a culture.“ … through writing,
speech, gestures, rituals, or other
imitable phenomena with a mimicked
theme. … cultural analogues to genes in that
they self-replicate, mutate, and
respond to selective pressures.
7. GroupBurst: Assesses Co-occurrences
One-pass
of Bursty
Real-time
Keyword in Recent Tweets
Adjustable against spam
Theoretically sound!
Adjustable against SPURIOUS Bursts. Coincidental Burst of Keyword over a short period of time
Context Extraction Algorithms (PCA,
SVD) & Grapevine’s Entity Extractor
to Add more
271 Million Monthly Active Users
500 Million Tweets (140 ch) Per Day
78% Active Users on Mobile
77% Accounts Outside U.S.
Supports 35+ languages
8. MemeTracking
News Cycle
Tracking News Evolution
Quotes & Memes
Integral Part of Journalistic Practice
Travel Relatively Intact with Mutational Variants
Clustering by Graph
9. Item: Each News Article/Blog Post
Phrase: A Quoted String Occurs in Items
MemeTracking …
10. Phrase Graph
DAG
|P| < |Q|
“senseless killing”
“enough of senseless
killing”
“Hear our voice. We have had enough of this
senseless killing”
Directed Edit Distance(P, Q) < δ
Word Consecutive Overlap(P, Q) > k
P Q
푊푃,푄 ∝
1
퐷푖푟푒푐푡푒푑 퐸푑푖푡 퐷푖푠푡푎푛푐푒(푃,푄)
∝ 푇표푡푎푙 푁푢푚푏푒푟 표푓 푄 푖푛 퐶표푟푝푢푠
MemeTracking …
11. Phrase Clusters
Directed Acyclic Graph (DAG) Partitioning
Given a Weighted DAG, Delete a Set of Edges of
Min Total Weight So That Each of the Resulting
Components is Single-Rooted.
NP-hard
Heuristic
1.Start from the Roots
2.Down the DAG & greedily Assigns each Node to the Cluster to
which it has the most Edges
MemeTracking …
13. Result
Volume Distribution
Dataset
3 Months Aug 1 to Oct 31 2008
~ 1M Docs per Day from 1.65 Million
Sites!
47M Phrases, 22M Distinct
9H Clustering Process Time
35, 800 Non-trivial Clusters (at least two phrases)
MemeTracking …
15. Other Findings
Time lag between the news media and blogs
푓 푛푗 훿 푡 − 푡푗
푛푗 = Number of Item Previously Written for Cluster j
푡 = 푡ℎ푒 푐푢푟푟푒푛푡 푡푖푚푒
푡푗 = 푡ℎ푒 푡푖푚푒 푤ℎ푒푛 푗 푤푎푠 푓푖푟푠푡 푝푟표푑푢푐푒푑
푅푒푐푒푛푐푦 → 훿 푖푠 푚표푛표푡표푛푖푐푎푙푙푦 푑푒푐푟푒푎푠푖푛푔 푖푛 푡 − 푡푗
퐼푚푖푡푎푡푖표푛 → 푓 푖푠 푚표푛표푡표푛푖푐푎푙푙푦 푖푛푐푟푒푎푠푖푛푔 푖푛 푛푗, 푓(0) > 0
푡 → 0−: 푎 = 0.076 푡 → 0+: 푎 = 0.092
푡 → 0−: 푏 = 1.77 푡 → 0+: 푏 = 2.15
Quotes migrating from blogs to news media: 3.5%
Each Cluster
Modeling the news trend
Imitation≠Recency
MemeTracking …
16.
17. Characterizing Trends
“trends in trend data.” Meta Trend
Taxonomy of the trends
Key Distinguishing Features of Trends
Not only the Textual Content
Social Network Structure
Ties
Geographic
Action Retweet, Reply, Mention, Hashtag
18. Trends
Exogenous
Broadcast-media
Broadcast of local media
“fight” (boxing event)
“Ravens” (football game)
Broadcast of global/national media
“Kanye”(KanyeWest acts up at the MTVVideo MusicAwards)
“Lost Finale” (series finale of Lost).
Global News
Breaking
“earthquake” (Chile earthquake)
“Tsunami” (HawaiiTsunamiwarning)
“Beyoncé”(Beyoncé cancels Malaysia concert).
Nonbreaking
“HCR” (health care reform)
“Tiger” (Tiger Woods apologizes)
“iPad” (toward thelaunch of Apple’s popular device).
National Holidays & Memorial Days
“Halloween,” “Valentine’s.”
Local Participatory & Physical
Planned
“marathon,”
“superbowl” (Super Bowl viewing parties)
“patrick’s” (St. Patrick’s Day Parade).
Unplanned
“rainy,” “snow.”
Endogenous
Memes
#in2010 (in December 2009, users imagine their near future)
“November” (users marking the beginning of the month on November 1)
Retweets
Fan Community Activities
“2pac” (the anniversary of the death of hip-hop artist Tupac Shakur).
Characterizing Trends …
19. Trends from twitter.com
Trends from Simple Trend Detector
Trends for Quality Analysis Supervised Categories
Trends for Computing Features
Tquantity
Ttwitter
Tterm freq.
Tquality
Characterizing Trends …
20. Content Features
•Average number of words/characters
•Proportion of messages with URLs, unique URLs, with hashtags ex/including trend terms
•Top unique hashtag?
•Similarity to centroid
Interaction Features
• Proportion of retweets, replies, mentions
Time-based Features
• Exponential fit head, tail
• Logarithmic fit head, tail
Participation Features
• Messages per author
• Proportion of messages from top author
• Proportion of messages from top 10% of authors
Social Network Features
•Level of reciprocity
•Maximal eigenvector centrality
•Maximal degree centrality
•Transitivity
•Density
•Average component size
Characterizing Trends …
21. Content features: Exo higher URLs, smaller hashtags
Exogenous
vs.
Endogenous
Trends
Interaction features: Exo fewer
retweets, similar number of replies
Time features: Exo different for the
head period before the trend peak
but will exhibit similar time features in
the tail period after the trend peak,
compared to endogenous trends.
Social network features: Exo fewer connections, less reciprocity
1.1
1.2
1.3
1.4
Characterizing Trends …
23. IDEA
Automatic Categorization of Trends
Photography Trend Selfie Image
Trust Trend Trustful Users, Trustful Twits
Untrendy People! Users Counteract the trends
Editor's Notes
Vertical Analysis: Financial Managers Set One Accounting Item as the Benchmark & Compare other Items with the Numerical Standard
In contrast with
Horizontal Analysis: Study of Performance Trends over Time
Short
Intermediate
Long
Past
Now
Future
Automatic trend detection over the twitter stream
distinctive phrases that travel relatively intact through on-line text; developing scalable algorithms for clustering textual variants of such phrases, we identify a broad class of memes that exhibit wide spread
mutation. As a result, a central computational challenge in this approach is to find robust ways of extracting and identifying all the mutational variants of each of these distinctive phrases, and to group them together.
Words as Tokens
This latter dependence is important, since we particularly wish to preserve edges (p, q) when the inclusion of p in q is supported by many occurrences of q.
Collections of Phrases Deemed to be Close Textual Variants of One Another
CCDF: Complementary Cumulative Distribution Function
If the quantity of interest is power-law distributed with exponent γ, p(x) ∝ x−γ, then when plotted on log-log axes the CCDF will be a straight line with slope −(γ + 1).
the tail is much heavier
This means that variants of popular phrases, like “lipstick on a pig,” are much more “stickier” than what would be expected from overall phrase volume distribution.
Popular phrases have many variants and each of them appears more frequently than an “average” phrase.
To put a “lipstick on a pig”(does not make it a lady) is a rhetorical expression used to convey the message that making superficial or cosmetic changes is a futile attempt to disguise the true nature of a product
اگر زري بپوشي، اگر اطلس بپوشي، همون کنگر فروشي
بزک
focus on the 1,000 threads with the largest total volumes (i.e. the largest number of mentions).
Thread volume in blogs reaches its peak typically 2.5 hours after the peak thread volume in the news sources. Thread volume in news sources increases slowly but decrease quickly, while in blogs the increase is rapid and decrease much slower.
reflect an ever-updating real-time live image of our society.
Exogenous Trends
• Broadcast-media events:
◦ Broadcast of local media events: “fight” (boxing event), “Ravens” (football game).
◦ Broadcast of global/national media events: “Kanye”(KanyeWest acts up at the MTVVideo MusicAwards),“Lost Finale” (series finale of Lost).
• Global news events:
◦ Breaking news events: “earthquake” (Chile earthquake),“Tsunami” (HawaiiTsunamiwarning), “Beyoncé”(Beyoncé cancels Malaysia concert).
◦ Nonbreaking news events: “HCR” (health care reform),“Tiger” (Tiger Woods apologizes), “iPad” (toward thelaunch of Apple’s popular device).
• National holidays and memorial days: “Halloween,” “Valentine’s.”
• Local participatory and physical events:
◦ Planned events: “marathon,” “superbowl” (Super Bowl viewing parties), “patrick’s” (St. Patrick’s Day Parade).
◦ Unplanned events: “rainy,” “snow.”
Endogenous Trends
• Memes: #in2010 (in December 2009, users imagine their near future), “November” (users marking the beginning of the month on November 1)
• Retweets (users “forwarding” en masse a single tweet from a popular user): “determination” (users retweeting LL Cool J’s post about said concept).
• Fan community activities: “2pac” (the anniversary of the death of hip-hop artist Tupac Shakur).
Breaking News vs. Other Exogenous Trends
H2.1: Interaction features of breaking events will be different than those of other exogenous trends, with more retweets (forwarding), but fewer replies (conversation).
H2.2: Time features of breaking events will be different for the head period, showing more rapid growth, and a better fit to the functions’ curve (i.e., less noise) compared to other exogenous trends.
H2.3: Social network features of breaking events will be different than those of other exogenous trends.
Local Events vs. Other Exogenous Trends
H3.1: Content features of local events will be different than those of other exogenous trends.
H3.2: Interaction features of local events will be different than those of other exogenous trends; in particular, local events will have more replies (conversation).
H3.3: Time features of local events will be different than those of other exogenous trends.
H3.4: Social network features of local events will be different than those of other exogenous trends; in particular, local events will have denser networks, more connectivity, and higher reciprocity.
Memes vs. Retweet Endogenous Trends
H4.1: Content features of memes will be different than those of retweet trends.
H4.2: Interaction features of memes will be different than those of retweet trends; in particular, retweet trends will have significantly more retweet (forwarding) messages (this hypothesis is included as a “sanity check” since the retweet trends are defined by having a large proportion of retweets).
H4.3: Time features of memes will be different than those of retweet trends.
H4.4: Participation features of memes will be different than those of retweet trends.
H4.5: Social network features of memes will be different than those of retweet trends; in particular, meme trends will have more connectivity and higher reciprocity than retweet trends.