Effect of Non-Coding RNA on Post-Transcriptional Gene Silencing of Alzheimer Disease

A large amount of hidden biological information is contained in the human genome, which is not expressed or revealed in the form of proteins; the usual end product form of gene expression. Instead, most of such information is in the form of non-coding RNAs (ncRNAs). ncRNAs correspond to genes that are transcribed, but do not get translated into proteins. This part of the genome was, till recently, considered as ‘junk’. The term ‘junk’ implied lack of any discernible function of these RNA. More than 98% of the human genomic size encompasses these non-coding RNAs. But, recent research has evidently brought out the indispensible contribution of non-coding RNA in controlling and regulating gene expression. ncRNA such as siRNAs and microRNAs have been reported to greatly help in causing post-transcriptional gene silencing (PTGS) in cells through RNA interference (RNAi) pathway. In this work, we have investigated the possibility of using siRNAs and microRNAs to aid in gene silencing of early onset Alzheimer’s disease genes. 
Alzheimer’s disease specific mutations and their corresponding positions in mRNA have been identified for six genes; Presenilin-1, Presenilin-2, APP (amyloid beta precursor protein), APBB3, BACE-1 and PSENEN. 

Small interfering RNAs (siRNAs) that can cause PTGS through RNA interference pathway have been designed. RNA analysis has been done to verify complementarity of antisense siRNA sequence with target mRNA sequence. Interaction studies have been done computationally between these antisense siRNA strands and seven Argonaute proteins. From the interaction studies, only one of the seven Argonaute proteins; 1Q8K, was found to have interaction with the siRNAs indicating the importance and uniqueness of this particular protein in RISC (RNA induced silencing complex). 

The interaction studies have been carried out for the microRNAs also. Out of the 700 mature human microRNAs collected, 394 microRNAs have been identified to show partial complementarity with their target sequence on PSEN-1 mRNA. Of these 394, five microRNAs have shown partial complementarity to early onset Alzheimer’s disease specific mutations in PSEN-1 mRNA. Interaction studies have been done between these microRNAs and Argonaute proteins. Thus, design, characterization and analysis of ncRNAs that contribute to post transcriptional gene silencing of Alzheimer’s disease have been achieved.
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IDEAS project - Scaling-up innovations to improve maternal and newborn health - Uttar Pradesh case study resources

The IDEAS project sought to improve the health and survival of mothers and babies through generating evidence to inform policy and practice in Ethiopia, northeast Nigeria and Uttar Pradesh, India. This data collection contains interview field notes and supporting information produced as part of a case study to document and assess the process by which the State Government of Uttar Pradesh introduced and scaled-up mSehat, a mobile phone application used by community health workers (Accredited Social Health Activists (ASHAs)) to create and maintain electronic health records

TRANSFORMATIVE IMPACT OF TECHNOLOGY ON THE HEALTHCARE INDUSTRY: A SWOT ANALYSIS

The healthcare industry has witnessed a remarkable transformation driven by advancements in technology. This research paper aims to probe into the multifaceted impact of technology on the healthcare sector. By analyzing various technological innovations, such as artificial intelligence, telemedicine, electronic health records, remote healthcare, retail clinics and wearable devices, this study explores how technology has revolutionized healthcare delivery, improved patient outcomes, enhanced operational efficiency, and influenced healthcare professionals' roles. This paper has conducted a comprehensive SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis to examine the internal and external factors influencing the integration of technology in healthcare. The research evaluates the strengths and weaknesses of technology adoption, explores the opportunities for further advancements, and identifies the potential threats and challenges faced by the healthcare industry. By employing the SWOT framework, this paper offers valuable insights to stakeholders for strategic decision-making and effective utilization of technology in healthcare. The SWOT paragon provides significant insights to stakeholders for strategic decision-making and optimal technology utilization in healthcare

Modal Parameter Estimation of Tall Structures using HHT with Improved EMD

In this paper, identification of the natural frequencies, damping ratios and mode shapes of the structures using the measured ambient responses are proposed using time-frequency analysis. The impulse responses are obtained from the measured acceleration time history data through cross-correlations. Empirical Mode Decomposition (EMD) is employed on these generated impulse responses to obtain Intrinsic Mode Functions (IMFs). Finally, modal identification of the structure is carried out by performing Hilbert Transform (HT) on these generated IMFs. To avoid the problem of mode mixing during EMD of the signal, an improved version with intermittency criteria along with treatment to end effects during sifting is proposed in this paper. Experimentally measured data of Guangzhou New TV (GNTV) Tower is used to test and verify the proposed algorithm. The studies indicate that the proposed HHT based algorithm can be applied quite effectively for the modal identification of practical engineering structures

Beach Morphological Characteristics and Coastal Processes Along Dakshina Kannada Coast, West Coast of India

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

A Systematic Survey on the Research of AI-predictive Models for Wastewater Treatment Processes

Context: To increase the efficiency of wastewater treatment, modeling and optimization of pollutant removal processes are the best solutions. The relationship between input and output parameters in wastewater treatment processes (WWTP) is a complicated one, and it is difficult for designing models using statistics. Artificial Intelligence (AI) models are generally more flexible when compared with statistical models while modeling complex datasets with nonlinearity and missing data. Objective: Studies on WWTP of AI-based are increasing day by day. Therefore, it is crucial to systematically review the AI techniques available which are implemented for WWTP. Such kind of review helps for classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, can sort out the best AI technique to design predictive models for WWTP. Method: With the help of the most relevant digital libraries, the total number of papers collected is 1222 which are based on AI modeling on WWTP. Then the filtration of the papers is mainly based on the inclusion and exclusion criteria. Also, to identify new relevant papers, snowballing is the other technique applied. Results: Finally selected 76 primary papers to reach the result were published between 2004 and 2020. Conclusion: ANN with MLP approach on BP algorithm become a supervised neural network called BPNN is the most used AI modeling for WWTP and around 40% of the experimental research done with BPNN. Then there are some limitations on AI modeling of WWTP using photoreforming which is the current study of WWTP represents a promising path for generating renewable and sustainable energy resources like chemicals and fuels

A review on predictive models designed from artificial intelligence techniques in the wastewater treatment process

Modeling and optimization of pollutant removal processes are the best solutions to increase the efficiency of wastewater treatment. The relationship between input and output parameters in wastewater treatment processes (WWTP) are complicated. Artificial intelligence (AI) models are generally more flexible when compared with statistical models while modeling complex datasets with nonlinearity and missing data. Studies on AI-based WWTP are increasing day by day. Therefore, it is crucial to review the AI techniques available which are implemented for WWTP. Such a review helps classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, it can sort out the best AI technique to design predictive models for WWTPs

A survey on artificial intelligence techniques for various wastewater treatment processes

Pollutant removal percentage is a key parameter for every WWTPs, and it is crucial to predict pollutant removal efficiency. The efficiency of pollutant removal processes can be increased with the help of modeling and its optimization. Statistical models are not practical enough for wastewater treatments due to complicated relationship among input and output parameters. AI models are generally more flexible while modeling complex datasets with missing data and nonlinearities. Many AI techniques are available, and the aim is to sort out the best AI technique to design predictive models for WWTPs. Deep Learning and Ensemble are the main techniques reviewed in this work. The Ensemble Learning models showing the most successful performance among other techniques by generally showed their accuracy and efficiency

Ergonomic Risk Assessment and Fatigue Analysis During Manual Lifting Tasks in Farming Activities

Introduction: Farming is a physically demanding occupation that puts farmers at risk of musculoskeletal disorders, particularly when frequently performing activities like heavy lifting, which strains the lower back muscles. The present study aimed to assess the ergonomic risk and fatigue during manual lifting tasks pertaining to farming activities. Methods: A study was performed on 20 farmers to analyze the ergonomic risks associated with load lifting through the estimation of the Recommended Weight Limit and Lifting Index using the revised NIOSH lifting equation. The low back compression forces of the participants were estimated using the 3DSSPP software. Surface electromyography was employed to analyze the onset of muscle fatigue during the lifting activity. Results: The results of the study showed a 111.12% increase in the recommended weight limit, a 52.77% reduction in lifting index, and a 28.15% reduction in the low back compression forces for the redesigned lifting technique. The average low-back compression force for the redesigned technique was observed to be well below the back compression design limit of 770 lb. A reduction in the slope of the RMS voltage regression line by 60% and a reduction of 50.23% in the peak spectral power of the sEMG signal, accompanied by a shift in the peak spectral power towards higher frequency region indicated delayed onset of fatigue for the redesigned technique. Conclusion: The outcomes of the study indicated that the ergonomic redesign of the lifting task could significantly reduce the lifting index and alleviate the spinal compression forces well within the back-compression design limit. The redesign was also found to delay the onset of fatigue in the erector spinae muscles

A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset

It is understood that water is the most valuable natural resource and as like wastewater treatment plants are necessary base to control the environmental balance where they are installed. To ensure good quality effluents, the dynamic and complicated wastewater treatment procedure must be handled efficiently. A global interest has been prompted in conservation, reuse, and alternative water sources due to growing treats over water supply scarcity. Water utilities are searching for more efficient ways to maintain their resources globally. The development of machine learning techniques is starting to offer real opportunities to operate water treatment systems in more efficient manners. This paperwork shows research as well as its development work implemented to predict the performance of petrochemical wastewater treatment. The data were used from a reputed chemical plant and the predictive models were developed by implementation of Backpropagation Neural Network using sample datasets with the parameters of wastewater dataset