Mute and deaf people find it very difficult to share their thoughts and feelings to normal people. American Sign Language is a hand sign language which is capable of carrying a complete conversation between two persons by the movement of both the hands. Although ASL is a complete language, it is grammatically different from the English language. There are several researches on the prediction of sign language based on the static image. But there is very little research that has been done on the video. Convolutional Neural Networks (CNN) and K-nearest neighbors (KNN) are majorly used algorithms for prediction. This article has summarized several research works that have been done in the field of hand sign language recognition. This study has also compared several research works and listed the corresponding advantages and disadvantages.
Distinct from normal differentiated tissues, cancer cells reprogram nutrient uptake and utilization to accommodate their elevated demands for biosynthesis and energy production. A hallmark of these types of reprogramming is the increased utilization of, and dependency on glutamine, a nonessential amino acid, for cancer cell growth and survival. It is well-accepted that glutamine is a versatile biosynthetic substrate in cancer cells beyond its role as a proteinogenic amino acid. In addition, accumulating evidence suggests that glutamine metabolism is regulated by many factors, including tumor origin, oncogene/tumor suppressor status, epigenetic alternations and tumor microenvironment. However, despite the emerging understanding of why cancer cells depend on glutamine for growth and survival, the contribution of glutamine metabolism to tumor progression under physiological conditions is still under investigation, partially because the level of glutamine in the tumor environment is often found low. Since targeting glutamine acquisition and utilization has been proposed to be a new therapeutic strategy in cancer, it is central to understand how tumor cells respond and adapt to glutamine starvation for optimized therapeutic intervention. In this review, we first summarize the diverse usage of glutamine to support cancer cell growth and survival, and then focus our discussion on the influence of other nutrients on cancer cell adaptation to glutamine starvation as well as its implication in cancer therapy.
ChatGPT is making waves in the technology space and has sparked debate among various researchers about its impact on students, industries, the labor market, etc. While there are various articles and research papers published on ChatGPT and available on SCOPUS, there is a need for better understanding to comprehend how scholars perceive this new technological disruption. With each passing day, we are seeing more and more research articles being published. While some research has been critical of ChatGPT, some have highlighted its positive impacts only, and few have analysed both the pros and cons of this new technology. We have analysed 100 research papers and case studies from SCOPUS to look at ChatGPT from the lens of a researcher and tried to narrow down to the areas, which according to researchers will be highly impacted. This has been done by reviewing all the articles and categorizing them based on the researcher’s point of view about the impacted sectors and fields. Keywords: ChatGPT, Artificial Intelligence, Natural Language Processing, Large Language Models
Abstract Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, affecting the lymphoid cells of both B and T lineages. Most ALL cells are auxotrophic for asparagine, a nonessential amino acid for protein synthesis, due to the low expression of asparagine synthetase (ASNS), a rate limiting enzyme for de novo biosynthesis of asparagine. As a result, standard ALL treatment takes advantage of this vulnerability by giving patients L-asparaginase, a bacterial enzyme that depletes the circulating asparagine. However, previous work from our lab and others have shown that some ALL cells become resistance to L-asparaginase treatment through the induction of ASNS expression. Mechanistically, amino acid starvation actives the general control nonderepressible 2 (GCN2) kinase, leading to the accumulation of ATF4 transcriptional factor. ATF4, in turn, is recruited to the promoter of the ASNS gene to activate its transcription. However, the role of GCN2 kinase in the process of leukemogenesis under nutrient limiting environment has not been established. In this study, our goal is to use a mouse model of T-ALL driven by mutant KRas to determine the role of GCN2 in leukemogenesis and the therapeutic response to L-asparaginase treatment. Citation Format: Ji Zhang, Rodney Claude, Sankalp Srivastava, Sandeep Batra, Minghua Zhong, Utpal Dave, Ronald C. Wek. Exploring the role of adaptive amino acid responses in Ras-driven leukemia progression and therapy [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr A012.
In an endeavour to tackle global inequality through digitalization, this study concentrates on utilizing the capabilities of Generative Artificial Intelligence (Generative AI) to empower Indigenous communities. The aim of this research is to investigate how Generative AI can mitigate socio-economic disparities by safeguarding indigenous knowledge and promoting social justice, all while being conscious of the historical biases faced by these communities. By employing innovative research tools that leverage Generative AI, the researchers delve into its applications within Indigenous contexts in India. Their findings underscore the potential of Generative AI in advancing cultural preservation, strengthening social cohesion, and establishing sustainable economic opportunities. This research sheds light on a transformative path toward digital empowerment and social justice for marginalized Indigenous communities
Objective - Indigenous communities face various challenges, including marginalization, loss of cultural heritage, language endangerment, health disparities, and economic inequities. Digitalization, empowered by Artificial Intelligence (AI), offers transformative solutions for preserving and revitalizing indigenous knowledge systems and improving the quality of life for these communities. Methodology/Technique – This review critically examines the impact of digitalization and AI on indigenous populations, focusing on culture, language, health, and economic status. It evaluates both the positive outcomes and the potential biases introduced by AI technologies. Finding – By exploring the application of Generative AI, this review extends existing studies and demonstrates its capability to mitigate biases and enrich our understanding of Indigenous cultures. The review identifies the dual narrative present in existing research, the beneficial effects of digitalization and AI, and the potential for bias. Novelty – This study uniquely focuses on the dual narrative of AI impacts, particularly the potential for Generative AI to mitigate biases, offering new insights into the intersection of digitalization and Indigenous knowledge systems. Type of Paper: Review JEL Classification: O33, I15, Z13, L86 Keywords: indigenous communities, artificial intelligence, deep learning, large language, models, digitalization, decolonial AI, ethical artificial intelligence. Reference to this paper should be referred to as follows: Srivastava, S; Upadhyay, P. (2024). Digital Empowerment for Indigenous Communities Using Generative Artificial Intelligence, GATR-Global J. Bus. Soc. Sci. Review, 12(2), 74–82. https://doi.org/10.35609/gjbssr.2024.12.2(3)
Tumor cells adapt to nutrient-limited environments by inducing gene expression that ensures adequate nutrients to sustain metabolic demands. For example, during amino acid limitations, ATF4 in the amino acid response induces expression of asparagine synthetase (ASNS), which provides for asparagine biosynthesis. Acute lymphoblastic leukemia (ALL) cells are sensitive to asparagine depletion, and administration of the asparagine depletion enzyme l-asparaginase is an important therapy option. ASNS expression can counterbalance l-asparaginase treatment by mitigating nutrient stress. Therefore, understanding the mechanisms regulating ASNS expression is important to define the adaptive processes underlying tumor progression and treatment. Here we show that DNA hypermethylation at the ASNS promoter prevents its transcriptional expression following asparagine depletion. Insufficient expression of ASNS leads to asparagine deficiency, which facilitates ATF4-independent induction of CCAAT-enhancer-binding protein homologous protein (CHOP), which triggers apoptosis. We conclude that chromatin accessibility is critical for ATF4 activity at the ASNS promoter, which can switch ALL cells from an ATF4-dependent adaptive response to ATF4-independent apoptosis during asparagine depletion. This work may also help explain why ALL cells are most sensitive to l-asparaginase treatment compared with other cancers. Tumor cells adapt to nutrient-limited environments by inducing gene expression that ensures adequate nutrients to sustain metabolic demands. For example, during amino acid limitations, ATF4 in the amino acid response induces expression of asparagine synthetase (ASNS), which provides for asparagine biosynthesis. Acute lymphoblastic leukemia (ALL) cells are sensitive to asparagine depletion, and administration of the asparagine depletion enzyme l-asparaginase is an important therapy option. ASNS expression can counterbalance l-asparaginase treatment by mitigating nutrient stress. Therefore, understanding the mechanisms regulating ASNS expression is important to define the adaptive processes underlying tumor progression and treatment. Here we show that DNA hypermethylation at the ASNS promoter prevents its transcriptional expression following asparagine depletion. Insufficient expression of ASNS leads to asparagine deficiency, which facilitates ATF4-independent induction of CCAAT-enhancer-binding protein homologous protein (CHOP), which triggers apoptosis. We conclude that chromatin accessibility is critical for ATF4 activity at the ASNS promoter, which can switch ALL cells from an ATF4-dependent adaptive response to ATF4-independent apoptosis during asparagine depletion. This work may also help explain why ALL cells are most sensitive to l-asparaginase treatment compared with other cancers.
Abstract Chromosomal translocation is the most common form of chromosomal abnormality and is often associated with congenital genetic disorders, infertility and cancers. The lack of cellular and animal models for chromosomal translocations, however, has hampered our ability to understand the underlying disease mechanisms and to develop new therapies. Here, we show that site-specific chromosomal translocations can be generated in mouse embryonic stem cells (mESCs) via CRISPR/Cas9. Mouse ESCs carrying translocated chromosomes can be isolated and expanded to establish stable cell lines. Furthermore, chimeric mice can be generated by injecting these mESCs into host blastocysts. The establishment of ESC-based cellular and animal models of chromosomal translocation by CRISPR/Cas9 provides a powerful platform for understanding the effect of chromosomal translocation and for the development of new therapeutic strategies.
This study presents a deep learning-based approach to improve the prediction of coronary artery disease (CAD) using X-ray angiography images. The primary objective is to achieve accurate and automated CAD identification by employing a convolutional neural network (CNN) model. The methodology involves preprocessing the dataset through normalization and augmentation techniques and utilizes a U-Net architecture for precise detection of coronary stenosis. To ensure robustness and generalizability, hyperparameter tuning and dropout regularisation are applied during model training. The proposed model achieves high performance, with an average Dice coefficient of 0.57 and a Jaccard Index of 0.47 on a held-out test set, indicating its effectiveness in segmenting coronary artery stenosis. These findings support the integration of deep learning methods into clinical workflows for enhanced CAD diagnosis and early intervention.
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