IOT Devices to Cater Home Automation through AI Search Engine- A Review

As we advances in technology, Day by day we are getting acquainted with new results to simply our daily routine. Through Artificial Intelligence, Three big giants in technology namely Google, Apple and Amazon trying their level best to introduce different forms of user experience through talk-back answering assistant. This AI machine can do a lot of work even getting your appointment to barber shop, setting up the ambience of home lighting, temperature and shop the exact product online without any physical existence. A small portion of such system can be developed through an IOT device which is aimed in this paper. The various home appliances which are connected to wireless network can be monitored and controlled by AI system such as Alexa by Amazon, Google Assistant by Google, Siri from Apple and Bixby by Samsung. We will proceed with one of them and with Arduino we will search it out to get our own home automation system

A Survey on Classification of Photo Aesthetics Based on Emotion

Recognition of human facial expression and calculating exact emotion by computer vision is an interesting and challenging problem. Emotion in natural scenery images plays vital role in the way humans perceive an image. Based on the various emotions like happiness, sadness, fear, anger of any human being the images that are examined by that person can propose that if the person is in happy mood then he/she would C the same images in different ways but still can be possible to build a universal classification for various emotions. The paper proposes the various techniques of recognizing emotion on the basis of how humans perceive an image, also aims to classify the aesthetics of the photographic images and determine wallpaper (Scene or non-scene images) according to human emotions

Biomarkers for Cancer: A Detail Review

When aberrant cells multiply uncontrolled, transcend their normal borders, invade nearby tissues, or spread to other organs, a wide spectrum of illnesses collectively referred to as "cancer" can arise in practically every organ or tissue of the body. The second-leading cause of death globally in 2018, cancer was expected to be responsible for 9.6 million deaths, or one in every six fatalities. A cancer biomarker is a characteristic that can be used to gauge a patient's likelihood of developing cancer or its outcome. Various biomarkers can be used at molecular and cellular level. It is crucial that biomarkers undergo thorough review, including analytical validation, clinical validation, and appraisal of clinical value, prior to being included into normal clinical treatment because of the crucial role they play at all stages of disease. We discuss important steps in the creation of biomarkers in this review, including how to prevent introducing bias and standards to adhere to when presenting the findings of biomarker research

Identification of sugar-containing natural products that interact with i-motif DNA

There are thousands of compounds shown to interact with G-quadruplex DNA, yet very few which target i-motif (iM) DNA. Previous work showed that tobramycin can interact with iM- DNA, indicating the potential for sugar-molecules to target these structures. Computational approaches indicated that the sugar-containing natural products baicalin and geniposidic acid had potential to target iM-DNA. We assessed the DNA interacting properties of these compounds using FRET-based DNA melting and a fluorescence-based displacement assay using iM-DNA structures from the human telomere and the insulin linked polymorphic region (ILPR), as well as complementary G-quadruplex and double stranded DNA. Both baicalin and geniposidic acid show promise as iM-interacting compounds with potential for use in experiments into the structure and function of i-motif forming DNA sequences and present starting points for further synthetic development of these as probes for iM-DNA

Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent Advances

Molecular docking is a widely used and effective structure-based computational strategy for predicting dynamics between ligands and receptors. Until now the docking software were developed for the protein-ligand interactions and very few docking tools were developed exclusively for the docking of small molecules on the nucleic acid structures like the DNA and RNA. The progress in algorithms and the need for deeper understanding of ligand-nucleic acid interactions more focused, and specialized tools are being developed to explore this hindered area of drug discovery. This chapter is focused on and discus in details about various tools available for docking with nucleic acids and how the rejuvenation of machine learning methods is making its impact on the development of these docking programs

Generation of Phenothiazine with Potent Anti-TLK1 Activity for Prostate Cancer Therapy

Through in vitro kinase assays and docking studies, we report the synthesis and biological evaluation of a phenothiazine analog J54 with potent TLK1 inhibitory activity for prostate cancer (PCa) therapy. Most PCa deaths result from progressive failure in standard androgen deprivation therapy (ADT), leading to metastatic castration-resistant PCa. Treatments that can suppress the conversion to mCRPC have high potential to be rapidly implemented in the clinics. ADT results in increased expression of TLK1B, a key kinase upstream of NEK1 and ATR and mediating the DNA damage response that typically results in temporary cell-cycle arrest of androgen-responsive PCa cells, whereas its abrogation leads to apoptosis. We studied J54 as a potent inhibitor of this axis and as a mediator of apoptosis in vitro and in LNCaP xenografts, which has potential for clinical investigation in combination with ADT. J54 has low affinity for the dopamine receptor in modeling and competition studies and weak detrimental behavioral effects in mice and C. elegans

Pharmacoinformatics-based identification of transmembrane protease serine-2 inhibitors from Morus Alba as SARS-CoV-2 cell entry inhibitors

Transmembrane protease serine-2 (TMPRSS2) is a cell-surface protein expressed by epithelial cells of specific tissues including those in the aerodigestive tract. It helps the entry of novel coronavirus (n-CoV) or Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the host cell. Successful inhibition of the TMPRSS2 can be one of the crucial strategies to stop the SARS-CoV-2 infection. In the present study, a set of bioactive molecules from Morus alba Linn. were screened against the TMPRSS2 through two widely used molecular docking engines such as Autodock vina and Glide. Molecules having a higher binding affinity toward the TMPRSS2 compared to Camostat and Ambroxol were considered for in-silico pharmacokinetic analyses. Based on acceptable pharmacokinetic parameters and drug-likeness, finally, five molecules were found to be important for the TMPRSS2 inhibition. A number of bonding interactions in terms of hydrogen bond and hydrophobic interactions were observed between the proposed molecules and ligand-interacting amino acids of the TMPRSS2. The dynamic behavior and stability of best-docked complex between TRMPRSS2 and proposed molecules were assessed through molecular dynamics (MD) simulation. Several parameters from MD simulation have suggested the stability between the protein and ligands. Binding free energy of each molecule calculated through MM-GBSA approach from the MD simulation trajectory suggested strong affection toward the TMPRSS2. Hence, proposed molecules might be crucial chemical components for the TMPRSS2 inhibition