Modernized Wildlife Surveillance and Behaviour Detection using a Novel Machine Learning Algorithm

In a natural ecosystem, understanding the difficulties of the wildlife surveillance is helpful to protect and manage animals also gain knowledge around animals count, behaviour and location. Moreover, camera trap images allow the picture of wildlife as unobtrusively, inexpensively and high volume it can identify animals, and behaviour but  it has the issues of high expensive, time consuming, error, and low accuracy. So, in this research work, designed a novel wildlife surveillance framework using DCNN for accurate prediction of animals and enhance the performance of detection accuracy. The executed research work is implemented in the python tool and 2700 sample input frame datasets are tested and trained to the system. Furthermore, analyze whether animals are present or not using background subtraction and features extracted is performed to extract the significant features. Finally, classification is executed to predict the animal using the fitness of seagull. Additionally, attained results of the developed framework are compared with other state-of-the-art techniques in terms of detection accuracy, sensitivity, F-measure and error

Network-based integration of multi-omics data for prioritizing cancer genes

Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions, and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression.; We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS' competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types.; NetICS is available at https://github.com/cbg-ethz/netics.; [email protected].; Supplementary data are available at Bioinformatics online

Simian Virus 40 Small T Antigen Activates AMPK and Triggers Autophagy To Protect Cancer Cells from Nutrient Deprivation ▿

As tumors grow larger, they often experience an insufficient supply of oxygen and nutrients. Hence, cancer cells must develop mechanisms to overcome these stresses. Using an in vitro transformation model where the presence of the simian virus 40 (SV40) small T (ST) antigen has been shown to be critical for tumorigenic transformation, we investigated whether the ST antigen has a role to play in regulating the energy homeostasis of cancer cells. We find that cells expressing the SV40 ST antigen (+ST cells) are more resistant to glucose deprivation-induced cell death than cells lacking the SV40 ST antigen (−ST cells). Mechanistically, we find that the ST antigen mediates this effect by activating a nutrient-sensing kinase, AMP-activated protein kinase (AMPK). The basal level of active, phosphorylated AMPK was higher in +ST cells than in −ST cells, and these levels increased further in response to glucose deprivation. Additionally, inhibition of AMPK in +ST cells increased the rate of cell death, while activation of AMPK in −ST cells decreased the rate of cell death, under conditions of glucose deprivation. We further show that AMPK mediates its effects, at least in part, by inhibiting mTOR (mammalian target of rapamycin), thereby shutting down protein translation. Finally, we show that +ST cells exhibit a higher percentage of autophagy than −ST cells upon glucose deprivation. Thus, we demonstrate a novel role for the SV40 ST antigen in cancers, where it functions to maintain energy homeostasis during glucose deprivation by activating AMPK, inhibiting mTOR, and inducing autophagy as an alternate energy source

Artificial Intelligence based Forecasting Techniques for the Covid-19 pandemic

The burial of bodies became a trend in the cause of ongoing pending (Novel Coronavirus), more than50 a million people all over the globe are adversely affected, hence the analysis and forecasting techniques are necessary to regain the human livelihood. The enlargement of technologies such as Artificial Intelligence, Machine Learning, Deep Learning, are en route into all the living aspects. Hence by using AI, ML, DL, Advanced technologies and existing models ARIMA, PROPHET, SVM, RNN, Faster Mask R-CNN, RESNET-50, and other techniques such as logarithmic scaling and exponential smoothing so on, the spread of VIRUS, the effect of countries economic growth, confirmed cases, fatality rate, recoveries are predicted to overcome the life threat due to SARS. Such that different predictive techniques are used to forecast. The advancement in the past algorithms to acquire accurate results are been introduced and described. © 2022 IEEE

AMP-activated protein kinase promotes epithelial-mesenchymal transition in cancer cells through Twist1 upregulation

The developmental programme of epithelial-mesenchymal transition (EMT), involving loss of epithelial and acquisition of mesenchymal properties, plays an important role in the invasion-metastasis cascade of cancer cells. In the present study, we show that activation of AMP-activated protein kinase (AMPK) using A769662 led to a concomitant induction of EMT in multiple cancer cell types, as observed by enhanced expression of mesenchymal markers, decrease in epithelial markers, and increase in migration and invasion. In contrast, inhibition or depletion of AMPK led to a reversal of EMT. Importantly, AMPK activity was found to be necessary for the induction of EMT by physiological cues such as hypoxia and TGF beta treatment. Furthermore, AMPK activation increased the expression and nuclear localization of Twist1, an EMT transcription factor. Depletion of Twist1 impaired AMPK-induced EMT phenotypes, suggesting that AMPK might mediate its effects on EMT, at least in part, through Twist1 upregulation. Inhibition or depletion of AMPK also attenuated metastasis. Thus, our data underscore a central role for AMPK in the induction of EMT and in metastasis, suggesting that strategies targeting AMPK might provide novel approaches to curb cancer spread

Network-based integration of multi-omics data for prioritizing cancer genes

Abstract Motivation Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS' competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types. Availability and implementation NetICS is available at https://github.com/cbg-ethz/netics. Supplementary information Supplementary data are available at Bioinformatics online

Identification of a novel AMPK-PEA15 axis in the anoikis-resistant growth of mammary cells

Introduction: Matrix detachment triggers anoikis, a form of apoptosis, in most normal epithelial cells, while acquisition of anoikis resistance is a prime requisite for solid tumor growth. Of note, recent studies have revealed that a small population of normal human mammary epithelial cells (HMECs) survive in suspension and generate multicellular spheroids termed `mammospheres'. Therefore, understanding how normal HMECs overcome anoikis may provide insights into breast cancer initiation and progression. Methods: Primary breast tissue-derived normal HMECs were grown as adherent monolayers or mammospheres. The status of AMP-activated protein kinase (AMPK) and PEA15 signaling was investigated by immunoblotting. Pharmacological agents and an RNA interference (RNAi) approach were employed to gauge their roles in mammosphere formation. Immunoprecipitation and in vitro kinase assays were undertaken to evaluate interactions between AMPK and PEA15. In vitro sphere formation and tumor xenograft assays were performed to understand their roles in tumorigenicity. Results: In this study, we show that mammosphere formation by normal HMECs is accompanied with an increase in AMPK activity. Inhibition or knockdown of AMPK impaired mammosphere formation. Concomitant with AMPK activation, we detected increased Ser(116) phosphorylation of PEA15, which promotes its anti-apoptotic functions. Inhibition or knockdown of AMPK impaired PEA15 Ser(116) phosphorylation and increased apoptosis. Knockdown of PEA15, or overexpression of the nonphosphorylatable S116A mutant of PEA15, also abrogated mammosphere formation. We further demonstrate that AMPK directly interacts with and phosphorylates PEA15 at Ser(116) residue, thus identifying PEA15 as a novel AMPK substrate. Together, these data revealed that AMPK activation facilitates mammosphere formation by inhibition of apoptosis, at least in part, through Ser(116) phosphorylation of PEA15. Since anoikis resistance plays a critical role in solid tumor growth, we investigated the relevance of these findings in the context of breast cancer. Significantly, we show that the AMPK-PEA15 axis plays an important role in the anchorage-independent growth of breast cancer cells both in vitro and in vivo. Conclusions: Our study identifies a novel AMPK-PEA15 signaling axis in the anchorage-independent growth of both normal and cancerous mammary epithelial cells, suggesting that breast cancer cells may employ mechanisms of anoikis resistance already inherent within a subset of normal HMECs. Thus, targeting the AMPK-PEA15 axis might prevent breast cancer dissemination and metastasis

Phosphoprotein enriched in diabetes (PED/PEA15) promotes migration in hepatocellular carcinoma and confers resistance to sorafenib

Hepatocellular carcinoma (HCC) is the third-leading cause of cancer-related death with limited treatment options and frequent resistance to sorafenib, the only drug currently approved for first-line therapy. Therefore, better understanding of HCC tumor biology and its resistance to treatment is urgently needed. Here, we analyzed the role of phosphoprotein enriched in diabetes (PED) in HCC. PED has been shown to regulate cell proliferation, apoptosis and migration in several types of cancer. However, its function in HCC has not been addressed yet. Our study revealed that both transcript and protein levels of PED were significantly high in HCC compared with non-tumoral tissue. Clinico-pathological correlation revealed that PEDhighHCCs showed an enrichment of gene signatures associated with metastasis and poor prognosis. Further, we observed that PED overexpression elevated the migration potential and PED silencing the decreased migration potential in liver cancer cell lines without effecting cell proliferation. Interestingly, we found that PED expression was regulated by a hepatocyte specific nuclear factor, HNF4α. A reduction of HNF4α induced an increase in PED expression and consequently, promoted cell migration in vitro. Finally, PED reduced the antitumoral effect of sorafenib by inhibiting caspase-3/7 activity. In conclusion, our data suggest that PED has a prominent role in HCC biology. It acts particularly on promoting cell migration and confers resistance to sorafenib treatment. PED may be a novel target for HCC therapy and serve as a predictive marker for treatment response against sorafenib