scholarly journals Cytoskeleton and Membrane Organization at Axon Branches

2021 ◽  
Vol 9 ◽  
Author(s):  
Satish Bodakuntla ◽  
Hana Nedozralova ◽  
Nirakar Basnet ◽  
Naoko Mizuno
Keyword(s):  
Neural Network ◽  
Energy Production ◽  
Network Formation ◽  
Local System ◽  
Membrane Remodeling ◽  
Axon Branching ◽  
Cellular Functions ◽  
Branch Formation ◽  
High Degree ◽  
Critical Process

Axon branching is a critical process ensuring a high degree of interconnectivity for neural network formation. As branching occurs at sites distant from the soma, it is necessary that axons have a local system to dynamically control and regulate axonal growth. This machinery depends on the orchestration of cellular functions such as cytoskeleton, subcellular transport, energy production, protein- and membrane synthesis that are adapted for branch formation. Compared to the axon shaft, branching sites show a distinct and dynamic arrangement of cytoskeleton components, endoplasmic reticulum and mitochondria. This review discusses the regulation of axon branching in the context of cytoskeleton and membrane remodeling.

STUDIES IN PROSPECTIVE PROCESS VALIDATION OF ALLOPURINOL USP AS ACTIVE PHARMACEUTICAL INGREDIENT

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (10) ◽  
pp. 42-50
Author(s):  
Hemant S. Kandle ◽  
Sangram S. Patil ◽  
Sujata S. Sawant ◽  
Ganesh B. Vambhurkar ◽  
Asha M. Jagtap ◽  
...  
Keyword(s):  
Active Pharmaceutical Ingredient ◽  
Process Parameter ◽  
Regulatory Requirements ◽  
Validation Data ◽  
Process Validation ◽  
Critical Process Parameter ◽  
Quality Control Testing ◽  
Quality Product ◽  
High Degree ◽  
Critical Process

Allopurinol USP batches of same size, method, equipment and validation criterion were taken. The critical process parameter involved were reaction, drying, milling, sifting, milling, and blending stages were validation. Quality cannot be assured by daily quality control testing because of the limitations of statistical samples, and the limited facilities of finished product testing. Validation checks the accuracy and reliability of process. Aim of this work was to study prospective process validation of allopurinol USP designed to meet the current regulatory requirements and prove with assurance that the product meets the predetermined specifications and quality attributes. The critical process parameter was identified with the help of process capability and evaluated by challenging its in house and compendial specification. Three initial process validations batches APL/008, APL/009 and APL/010 were identified and evaluated as per validation master plan. The outcome indicated that this process validation data provides high degree of assurance so that manufacturing process produces a quality product.

scholarly journals A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

2019 ◽  
Vol 9 (9) ◽  
pp. 1844 ◽  
Author(s):  
Jesús Ferrero Bermejo ◽  
Juan F. Gómez Fernández ◽  
Fernando Olivencia Polo ◽  
Adolfo Crespo Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Energy Sources ◽  
Solar Pv ◽  
Ann Models ◽  
Artificial Neural

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

scholarly journals Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1500 ◽  
Author(s):  
Halit Apaydin ◽  
Hajar Feizi ◽  
Mohammad Taghi Sattari ◽  
Muslume Sevba Colak ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Energy Production ◽  
Short Term Memory ◽  
Efficiency Coefficient ◽  
Dam Reservoir ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.

Artificial Intelligence in Aviation Industries

2020 ◽  
pp. 1-35
Author(s):  
Tetiana Shmelova ◽  
Arnold Sterenharz ◽  
Serge Dolgikh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Unmanned Aircraft ◽  
Innovative Technology ◽  
Sociotechnical System ◽  
Air Traffic Controllers ◽  
High Degree ◽  
Difficult Situations

This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).

scholarly journals Environmental and Nutritional “Stressors” and Oligodendrocyte Dysfunction: Role of Mitochondrial and Endoplasmatic Reticulum Impairment

Biomedicines ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 553
Author(s):  
Jessica Maiuolo ◽  
Micaela Gliozzi ◽  
Vincenzo Musolino ◽  
Cristina Carresi ◽  
Saverio Nucera ◽  
...  
Keyword(s):  
Endoplasmic Reticulum ◽  
Energy Production ◽  
Lipid Synthesis ◽  
Main Function ◽  
Cellular Functions ◽  
Mitochondrial Oxidative Stress ◽  
The Central Nervous System ◽  
Altered Regulation ◽  
The Brain

Oligodendrocytes are myelinating cells of the central nervous system which are generated by progenitor oligodendrocytes as a result of maturation processes. The main function of mature oligodendrocytes is to produce myelin, a lipid-rich multi-lamellar membrane that wraps tightly around neuronal axons, insulating them and facilitating nerve conduction through saltatory propagation. The myelination process requires the consumption a large amount of energy and a high metabolic turnover. Mitochondria are essential organelles which regulate many cellular functions, including energy production through oxidative phosphorylation. Any mitochondrial dysfunction impacts cellular metabolism and negatively affects the health of the organism. If the functioning of the mitochondria is unbalanced, the myelination process is impaired. When myelination has finished, oligodendrocyte will have synthesized about 40% of the total lipids present in the brain. Since lipid synthesis occurs in the cellular endoplasmic reticulum, the dysfunction of this organelle can lead to partial or deficient myelination, triggering numerous neurodegenerative diseases. In this review, the induced malfunction of oligodendrocytes by harmful exogenous stimuli has been outlined. In particular, the effects of alcohol consumption and heavy metal intake are discussed. Furthermore, the response of the oligodendrocyte to excessive mitochondrial oxidative stress and to the altered regulation of the functioning of the endoplasmic reticulum will be explored.

scholarly journals A Novel Point Mutation Promotes Growth Phase-Dependent Daptomycin Tolerance in Staphylococcus aureus

2015 ◽  
Vol 59 (9) ◽  
pp. 5366-5376 ◽  
Author(s):  
Lukas Mechler ◽  
Alexander Herbig ◽  
Kerstin Paprotka ◽  
Martin Fraunholz ◽  
Kay Nieselt ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Point Mutation ◽  
Growth Phase ◽  
Drug Tolerance ◽  
Stationary Growth Phase ◽  
Cellular Functions ◽  
Upstream Gene ◽  
Content Type ◽  
High Degree

ABSTRACTRecalcitrance of genetically susceptible bacteria to antibiotic killing is a hallmark of bacterial drug tolerance. This phenomenon is prevalent in biofilms, persisters, and also planktonic cells and is associated with chronic or relapsing infections with pathogens such asStaphylococcus aureus. Here we report thein vitroevolution of anS. aureusstrain that exhibits a high degree of nonsusceptibility to daptomycin as a result of cyclic challenges with bactericidal concentrations of the drug. This phenotype was attributed to stationary growth phase-dependent drug tolerance and was clearly distinguished from resistance. The underlying genetic basis was revealed to be an adaptive point mutation in the putative inorganic phosphate (Pi) transporter genepitA. Drug tolerance caused by this allele, termedpitA6, was abrogated when the upstream genepitRwas inactivated. Enhanced tolerance toward daptomycin, as well as the acyldepsipeptide antibiotic ADEP4 and various combinations of other drugs, was accompanied by elevated intracellular concentrations of Piand polyphosphate, which may reversibly interfere with critical cellular functions. The evolved strain displayed increased rates of survival within human endothelial cells, demonstrating the correlation of intracellular persistence and drug tolerance. These findings will be useful for further investigations ofS. aureusdrug tolerance, toward the development of additional antipersister compounds and strategies.

scholarly journals Exchangeable Chaperone Modules Contribute to Specification of Type I and Type II Hsp40 Cellular Function

2004 ◽  
Vol 15 (2) ◽  
pp. 761-773 ◽  
Author(s):  
Chun-Yang Fan ◽  
Soojin Lee ◽  
Hong-Yu Ren ◽  
Douglas M. Cyr
Keyword(s):  
In Vivo Studies ◽  
Cell Physiology ◽  
Type I ◽  
Type Ii ◽  
Cellular Functions ◽  
Chaperone Function ◽  
Sequence Identity ◽  
Carboxyl Terminal ◽  
High Degree

Hsp40 family members regulate Hsp70s ability to bind nonnative polypeptides and thereby play an essential role in cell physiology. Type I and type II Hsp40s, such as yeast Ydj1 and Sis1, form chaperone pairs with cytosolic Hsp70 Ssa1 that fold proteins with different efficiencies and carry out specific cellular functions. The mechanism by which Ydj1 and Sis1 specify Hsp70 functions is not clear. Ydj1 and Sis1 share a high degree of sequence identity in their amino and carboxyl terminal ends, but each contains a structurally unique and centrally located protein module that is implicated in chaperone function. To test whether the chaperone modules of Ydj1 and Sis1 function in the specification of Hsp70 action, we constructed a set of chimeric Hsp40s in which the chaperone domains of Ydj1 and Sis1 were swapped to form YSY and SYS. Purified SYS and YSY exhibited protein-folding activity and substrate specificity that mimicked that of Ydj1 and Sis1, respectively. In in vivo studies, YSY exhibited a gain of function and, unlike Ydj1, could complement the lethal phenotype of sis1Δ and facilitate maintenance of the prion [RNQ+]. Ydj1 and Sis1 contain exchangeable chaperone modules that assist in specification of Hsp70 function.

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