Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

Sensitivity Analysis of Calibration Methods and Factors Effecting the Statistical Nature of Radiation Measurement

The scintillator detectors are recalibrated against the datasheet given by the manufacturer. Optimal and mutual dependent values of (a) high voltage at PMT (Photomultiplier Tube), (b) amplifier gain, (c) average time to count the radiation particles (set by operator), and (d) number of instances/sample number are estimated. Total 5: two versions of Central Limit Theorem (CLT), (3) industry preferred Pulse Width Saturation, (4) calibration based on MPPC coupled Gamma-ray detector, and (5) gross method are used. It is shown that the CLT method is the most optimal method to calibrate the detector and its respective electronics couple. An inverse modeling-based Computerized Tomography method is used for verification. It is shown that statistically averaging results are more accurate and precise data than mode and median if the data is not skewed and a random number of samples are used during the calibration process. It is also shown that the average time to count the radiation particle is the most important parameter affecting the optimal calibration setting for precision and accurate measurements of gamma radiation.

     

Exploring Molecular Contacts of MUC1 at CIN85 Binding Interface to Address Future Drug Design Efforts

The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.     

Tuning of shear thickening behavior and elastic strength of polyvinylidene fluoride via doping of ZnO-graphene

ZnO rice like nonarchitects are grafted on the graphene carbon core via a rapid microwave synthesis route. The prepared grafted systems are characterized via XRD, SEM, RAMAN, and XPS to examined the structural and morphological parameters. Zinc oxide grafted graphene sheets (ZnO-G) are further doped in β-phase of polyvinylidene fluoride (PVDF) to prepare the polymer nanocomposites (PNCs) via mixed solvent approach (THF/DMF). β-phase confirmation of PVDF PNCs is done by FTIR studies. It is observed that ZnO-G filler enhances the β-phase content in the PNCs. Non-doped PVDF and PNCs are further studied for rheological behavior under the shear rate of 1–100 s⁻¹. Doping of ZnO-G dopant to the PVDF matrix changes its discontinuous shear thickening (DST) behavior to continues shear thickening behavior (CST). Hydrocluster formation and their interaction with the dopant could be the reason for this striking DST to CST behavioral change. Strain amplitude sweep (10⁻³% -10%) oscillatory test reveals that the PNCs shows extended linear viscoelastic region with high elastic modulus and lower viscous modulus. Effective shear thickening behavior and strong elastic strength of these PNCs present their candidature for various fields including mechanical and soft body armor applications.     

Performance-based modelling of long-term deterioration to support rehabilitation and investment decisions in drinking water distribution systems

Managing the urban drinking water system in the long term in order to maintain system performance can be challenging due to the difficulty of modelling future deterioration of the networks. This paper establishes a methodology for cohort survival models where historical (empirical) data on decommissioning ages of pipes are used to calibrate survival functions of pipe cohorts according to service level targets. The benefit of the approach is that remaining useful life of pipes, future renewal rates and investment needs can be governed by a required level of service in the network. A case study shows how the methodology can be applied to a cohort of drinking water pipes to create a ‘calibration curve’, which is a survival function calibrated with empirical data.     

Extracellular Vesicles in CNS Developmental Disorders

The central nervous system (CNS) is the most complex structure in the body, consisting of multiple cell types with distinct morphology and function. Development of the neuronal circuit and its function rely on a continuous crosstalk between neurons and non-neural cells. It has been widely accepted that extracellular vesicles (EVs), mainly exosomes, are effective entities responsible for intercellular CNS communication. They contain membrane and cytoplasmic proteins, lipids, non-coding RNAs, microRNAs and mRNAs. Their cargo modulates gene and protein expression in recipient cells. Several lines of evidence indicate that EVs play a role in modifying signal transduction with subsequent physiological changes in neurogenesis, gliogenesis, synaptogenesis and network circuit formation and activity, as well as synaptic pruning and myelination. Several studies demonstrate that neural and non-neural EVs play an important role in physiological and pathological neurodevelopment. The present review discusses the role of EVs in various neurodevelopmental disorders and the prospects of using EVs as disease biomarkers and therapeutics.     

Isomeric Carborane Neuromuscular Blocking Agents

We synthesized a family of neuromuscular blocking agents (NMB) based on decamethonium, but containing a carborane cluster in the methylene chain between the two quaternary ammonium groups. The carborane cluster isomers o-NMB, m-NMB, and p-NMB were tested in animals for neuromuscular block and compared with agents used clinically: rocuronium and decamethonium. All three isomers caused reversible muscle weakness in mice as determined by grip strength and inverted screen tests, with a potency rank of p-NMB > rocuronium > decamethonium > m-NMB > o-NMB. The mechanism of action of the compounds was determined by using the in vitro rat phrenic nerve hemi-diaphragm preparation and electrophysiologic measurements in cells. Neostigmine reversed hemi-diaphragm weakness caused by the three isomers and rocuronium, but not succinylcholine. In electrophysiologic recordings of currents through acetylcholine receptor channels, the carborane compounds did not activate channel activity but did inhibit channel activation by acetylcholine. These results demonstrate that the carborane neuromuscular blocking agents are non-depolarizers in contrast to the depolarizing action of the parent compound.     

The influence of brewing method on bioactive compounds residues in spent coffee grounds of different roasting degree and geographical origin

The content of bioactive compounds in spent coffee grounds (SGC) was studied. SGC were obtained from Coffea arabica beans of different roasting degrees (light and dark) and different geographical origins (Nicaragua, Columbia and Mexico) processed using four brewing methods (mocha, filtered, drip and infusion). The highest caffeine and chlorogenic acid contents were determined in filtered spent coffee extracts. All extracts of light roasted spent coffee grounds showed lower browning index levels in comparison to that from dark roasted spent coffee grounds. Generally, the highest content of total polyphenolic compounds and highest antioxidant capacity were determined in extracts prepared in drip. In conclusion, the results obtained in this study indicate that the spent coffee grounds produced of domestic levels, especially those obtained from filter coffeemaker, could be considered as a good source of natural antioxidants.