146 research outputs found
R from Zero to Hero (Arabic)
No full textThis is a course designed by Batool Almarzouq and delivered in JeelAIDM. All Materials are licensed under CC-BY license. CC-BY license means you can re-use, modify and build upon the materials with attribution to the source. The course is delivered over six weeks, with two sessions each week, each lasting two hours.
Week
Session
1
Introduction to R and Open Science
1
Project Management
2
R Markdown
2
GitHub in RStudio
3
Tidydata
3
Tidyverse
4
ggplot2 Part 1
4
ggplot2 Part 2
5
YAML in R Markdown
5
Blogging in R
6
Reproducibility with renv
6
Create your first R package!
The Slides are accompanied by live coding in this GitHub repository associated.The author acknowledges JeelAIDM for making the materials ope
Knowledge, Attitude and Awareness of Hepatitis B Vaccination in Patients Visiting Jinnah Postgraduate Medical Center, Karachi
No full textNot Availabl
Mathematical Multiscale Modeling and Stability Analysis of Cancer Genesis
No full textCancer, a complex and multifaceted disease, continues to challenge the boundaries of biomedical research. In this dissertation, we explore the complexity of cancer genesis, employing multiscale modeling, abstract mathematical concepts such as stability analysis, and numerical simulations as powerful tools to decipher its underlying mechanisms. Through a series of comprehensive studies, we mainly investigate the cell cycle dynamics, the delicate balance between quiescence and proliferation, the impact of mutations, and the co-evolution of healthy and cancer stem cell lineages. The introductory chapter provides a comprehensive overview of cancer and the critical importance of understanding its underlying mechanisms. Additionally, it establishes the foundation by elucidating key definitions and presenting various modeling perspectives to address the cancer genesis. Next, cell cycle dynamics have been explored, revealing the temporal oscillatory dynamics that govern the progression of cells through the cell cycle.
The first half of the thesis investigates the cell cycle dynamics and evolution of cancer stem cell lineages by incorporating feedback regulation mechanisms. Thereby, the pivotal role of feedback loops in driving the expansion of cancer stem cells has been thoroughly studied, offering new perspectives on cancer progression. Furthermore, the mathematical rigor of the model has been addressed by deriving wellposedness conditions, thereby strengthening the reliability of our findings and conclusions. Then, expanding our modeling scope, we explore the interplay between quiescent and proliferating cell populations, shedding light on the importance of their equilibrium in cancer biology. The models developed in this context offer potential avenues for targeted cancer therapies, addressing perspective cell populations critical for cancer progression. The second half of the thesis focuses on multiscale modeling of proliferating and quiescent cell populations incorporating cell cycle dynamics and the extension thereof with mutation acquisition. Following rigorous mathematical analysis, the wellposedness of the proposed modeling frameworks have been studied along with steady-state solutions and stability criteria.
In a nutshell, this thesis represents a significant stride in our understanding of cancer genesis, providing a comprehensive view of the complex interplay between cell cycle dynamics, quiescence, proliferation, mutation acquisition, and cancer stem cells. The journey towards conquering cancer is far from over. However, this research provides valuable insights and directions for future investigation, bringing us closer to the ultimate goal of mitigating the impact of this formidable disease
Erratum: Cloaking using anisotropic multilayer circular cylinder (AIP Advances (2020) 10 (095312) DOI: 10.1063/5.0012769)
No full textCo-author Mehwish Nisar should have had an additional affiliation noted in the byline of our original manuscript.1 The correct affiliations for this manuscript are as listed above
Women leadership and their experience of internal identity asymmetry at workplace
No full textIndividuals at the workplace have a lasting interest in how others perceive them and a core desire for others to assert and verify their salient work-related identities. Internal identity asymmetry is encountered when an individual feels misidentified; when they think their work-related identities are not recognized by their peers. This article based on previous literature about women leadership and their experience of Internal Identity at the workplace. Although there is no concrete theory to explain this concept accordingly in this article, we attempt to investigate the concept of internal identity asymmetry with related theories combined. Subsequently, we addressed how women get misidentified and deduce the consequences of experiences of Internal Identity Asymmetry at the workplace. The current study is a conceptual paper and therefore, contributes freshness to this existing literature by integrating the concept of internal Identity asymmetry and women leadership thus, the model can be empirically tested in future research
Stability analysis of a multiscale model of cell cycle dynamics coupled with quiescent and proliferating cell populations.
No full textIn this paper, we perform a mathematical analysis of our proposed nonlinear, multiscale mathematical model of physiologically structured quiescent and proliferating cell populations at the macroscale and cell-cycle proteins at the microscale. Cell cycle dynamics (microscale) are driven by growth factors derived from the total cell population of quiescent and proliferating cells. Cell-cycle protein concentrations, on the other hand, determine the rates of transition between the two subpopulations. Our model demonstrates the underlying impact of cell cycle dynamics on the evolution of cell population in a tissue. We study the model's well-posedness, derive steady-state solutions, and find sufficient conditions for the stability of steady-state solutions using semigroup and spectral theory. Finally, we performed numerical simulations to see how the parameters affect the model's nonlinear dynamics
Evolution of cancer stem cell lineage involving feedback regulation.
No full textTumor emergence and progression is a complex phenomenon that assumes special molecular and cellular interactions. The hierarchical structuring and communication via feedback signaling of different cell types, which are categorized as the stem, progenitor, and differentiated cells in dependence of their maturity level, plays an important role. Under healthy conditions, these cells build a dynamical system that is responsible for facilitating the homeostatic regulation of the tissue. Generally, in this hierarchical setting, stem and progenitor cells are yet likely to undergo a mutation, when a cell divides into two daughter cells. This may lead to the development of abnormal characteristics, i.e. mutation in the cell, yielding an unrestrained number of cells. Therefore, the regulation of a stem cell's proliferation and differentiation rate is crucial for maintaining the balance in the overall cell population. In this paper, a maturity based mathematical model with feedback regulation is formulated for healthy and mutated cell lineages. It is given in the form of coupled ordinary and partial differential equations. The focus is laid on the dynamical effects resulting from acquiring a mutation in the hierarchical structure of stem, progenitor and fully differentiated cells. Additionally, the effects of nonlinear feedback regulation from mature cells into both stem and progenitor cell populations have been inspected. The steady-state solutions of the model are derived analytically. Numerical simulations and results based on a finite volume scheme underpin various expected behavioral patterns of the homeostatic regulation and cancer evolution. For instance, it has been found that the mutated cells can experience significant growth even with a single somatic mutation, but under homeostatic regulation acquire a steady-state and thus, ensuing healthy cell population to either a steady-state or a lower cell concentration. Furthermore, the model behavior has been validated with different experimentally measured tumor values from the literature
Deep feature engineering using full-text publications
No full textWe have observed a rapid proliferation in scientific literature and advancements in web technologies has shifted information dissemination to digital libraries [1]. In general, the research conducted by scientific community is articulated through scholarly publications pertaining high quality algorithms along other algorithmic specific metadata such as achieved results, deployed datasets and runtime complexity. According to estimation, approximately 900 algorithms are published in top core conferences during the years 2005-2009 [2]. With this significant increase in algorithms reported in these conferences, more efficient search systems with advance searching capabilities must be designed to search for an algorithm and its supported metadata such as evaluation results like precision, recall etc., particular dataset on which an algorithm executed or the time complexity achieved by that algorithm from full body text of an article. Such advanced search systems could support researchers and software engineers looking for cutting edge algorithmic solutions. Recently, state designed to search for an algorithm from full text articles [3-5].
In this work, we designed an advanced search engine for full text publications that leverages the deep learning techniques to classify algorithmic specific metadata and further to improve searching capabilities for a search system
Evolution of microscale proteins from the cell-cycle.
No full textCyclin shows a complete activation and degradation within a full cycle. The concentration of transcription factor is elevated since Retinoblastoma protein is inactivated with the rise in Cyclin complex. Similarly, protein elevates near the end of the cell-cycle to help in the degradation of the Cyclin’ complex.</p
Interpreting Between the Lines: Unveiling the Vituperative and Manipulative Linguistic Expressions Used in Pakistani Political Discourses
No full textPolitical discourses always serve the agenda of political figures through the use of manipulative and vituperative language. Manipulative language is cunning or scheming language, having a certain agenda, while Vituperative language is bitter and abusive and hurts someone’s feelings either directly or indirectly. Today’s society is breaking away from normative language and is choosing the manipulative language that most of the public uses in daily conversations. This article delves into the complexities of the layers of political language by thoroughly studying the words’ approach at different levels. The correctness of language is marred by the grossness spread among the masses. The study is based on the analysis of the political speeches delivered by Imran Khan and Nawaz Sharif during different periods in the course of their tenure to check the validity of their language choice. This research will be beneficial to unmask their vituperative and machiavellian language, penetrating the roots of society and invigorating the masses to be vigilant and critical. Their victory speeches, speeches after their government was toppled, the speech by Imran Khan at the prominent event of Amar bil Maroof, and the homecoming speech of Nawaz Sharif are analyzed. Moreover, Fairclough’s 3D model is applied to these speeches to interpret them at different levels. Collectively, the article’s approach is to cover the broader perspective of language use or misuse
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