Signal transducers and activators of transcription 3 (STAT-3) is a transcription factor that regulates the gene expression of several target genes. These factors are activated by the binding of cytokines and growth factors with STAT-3 specific receptors on cell membrane. Few years ago, STAT-3 was considered an acute phase response element having several cellular functions such as inflammation, cell survival, invasion, metastasis and proliferation, genetic alteration, and angiogenesis. STAT-3 is activated by several types of inflammatory cytokines, carcinogens, viruses, growth factors, and oncogenes. Thus, the STAT3 pathway is a potential target for cancer therapeutics. Abnormal STAT-3 activity in tumor development and cellular transformation can be targeted by several genomic and pharmacological methodologies. An extensive review of the literature has been conducted to emphasize the role of STAT-3 as a unique cancer drug target. This review article discusses in detail the wide range of STAT-3 inhibitors that show antitumor effects both in vitro and in vivo. Thus, targeting constitutive STAT-3 signaling is a remarkable therapeutic methodology for tumor progression. Finally, current limitations, trials and future perspectives of STAT-3 inhibitors are also critically discussed.
this paper presents a review of "How AI, cognitive science and DM are combined to develop intelligent agents", and how the paradigm first shifted from AI to Data mining and then towards combination of data mining and artificial intelligence. The paper will also provide a state-of-the-art account of the cognitive architectures. It also gives a detailed comparative study of all the architectures discussed in the paper. All the survey of data mining and cognitive architecture is done w.r.t Multi agent systems. Therefore, paper will also provide a bird eye view of MAS! ABMS.
Neuroproteomics, as a sub-discipline of proteomics, has enlightened the pathway for the study of different complicated diseases and brain disorders. Since four decades, various analytical and quantitative techniques have been used to cure problems related to brain and memory. Brain has a complex structure with various cells and cell types, the expressing proteins and suppressing factors too. Drug addiction is one of the main health concerns as it causes physiological changes in brain and affects its different parts. Some of these drugs like cocaine, marijuana, nicotine and alcohol not only affect memory and brain cells but also lead to expression and suppression of unwanted and beneficial proteins respectively. A variety of techniques involving separation techniques, quantification techniques and analytical techniques are used along with the combination of bioinformatics and magical tools for analyzing different aspects of brain parts especially proteome of the brain cells. Moreover, different animal models preferably those resembling human beings are routinely used in neuroproteomics to study the effects of different drugs on the brain proteome. Different experiments have already been performed by the researchers on drug abuse that helped massively in estimating not only the effects of drug addiction on the brain of highly complex organisms (human beings) but also to propose different therapeutics.
Weather data have two classes' synoptic data and climatic data. Real time data (Synoptic data) is used in applications like forecast modelling and aviation; climatic data is recorded over certain period of time. Formerly weather was forecasted manually by observing sky conditions and current weather conditions with manual calculations. Weather conditions are chaotic so there prediction must be precise and accurate. More sophisticated techniques have been developed which are far more efficient and accurate than manual calculations. Data mining is among one of such technologies. And it has a broader scope of applicability. One of such domains is Meteorology where data mining can enhance the productivity of its analysts extensively by transforming their huge, unmanageable data into valuable information knowledge. Meteorological changes of a region can cause economic and ecological damage and can harm human lives. Accurate prediction is therefore a key factor for controlling such occurrences. Various weather events e.g. Temperature, Humidity, Wind direction, Wind Speed, and Rain etc. are being predicted using various data mining techniques .The prime objective of this paper is to review research in data mining techniques applied in the field of weather prediction. A comparative study of various data mining techniques in weather forecasting is followed by a discussion on conventional preprocessing, challenges associated and issues of model evaluation and building methods. Therefore, this paper provides a roadmap to researchers for knowledge.
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