Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow

We review modeling of astrocyte ion dynamics with a specific focus on the implications of so-called spatial potassium buffering, where excess potassium in the extracellular space (ECS) is transported away to prevent pathological neural spiking. The recently introduced Kirchoff-Nernst-Planck (KNP) scheme for modeling ion dynamics in astrocytes (and brain tissue in general) is outlined and used to study such spatial buffering. We next describe how the ion dynamics of astrocytes may regulate microscopic liquid flow by osmotic effects and how such microscopic flow can be linked to whole-brain macroscopic flow. We thus include the key elements in a putative multiscale theory with astrocytes linking neural activity on a microscopic scale to macroscopic fluid flow.Comment: 27 pages, 7 figure

In vitro inhibitory activities of selected Australian medicinal plant extracts against protein glycation, angiotensin converting enzyme (ACE) and digestive enzymes linked to type II diabetes

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background There is a need to develop potential new therapies for the management of diabetes and hypertension. Australian medicinal plants collected from the Kuuku I’yu (Northern Kaanju) homelands, Cape York Peninsula, Queensland, Australia were investigated to determine their therapeutic potential. Extracts were tested for inhibition of protein glycation and key enzymes relevant to the management of hyperglycaemia and hypertension. The inhibitory activities were further correlated with the antioxidant activities. Methods Extracts of five selected plant species were investigated: Petalostigma pubescens, Petalostigma banksii, Memecylon pauciflorum, Millettia pinnata and Grewia mesomischa. Enzyme inhibitory activity of the plant extracts was assessed against α-amylase, α-glucosidase and angiotensin converting enzyme (ACE). Antiglycation activity was determined using glucose-induced protein glycation models and formation of protein-bound fluorescent advanced glycation endproducts (AGEs). Antioxidant activity was determined by measuring the scavenging effect of plant extracts against 1, 1-diphenyl-2-picryl hydrazyl (DPPH) and using the ferric reducing anti-oxidant potential assay (FRAP). Total phenolic and flavonoid contents were also determined. Results Extracts of the leaves of Petalostigma banksii and P. pubescens showed the strongest inhibition of α-amylase with IC50 values of 166.50 ± 5.50 μg/mL and 160.20 ± 27.92 μg/mL, respectively. The P. pubescens leaf extract was also the strongest inhibitor of α-glucosidase with an IC50 of 167.83 ± 23.82 μg/mL. Testing for the antiglycation potential of the extracts, measured as inhibition of formation of protein-bound fluorescent AGEs, showed that P. banksii root and fruit extracts had IC50 values of 34.49 ± 4.31 μg/mL and 47.72 ± 1.65 μg/mL, respectively, which were significantly lower (p < 0.05) than other extracts. The inhibitory effect on α-amylase, α-glucosidase and the antiglycation potential of the extracts did not correlate with the total phenolic, total flavonoid, FRAP or DPPH. For ACE inhibition, IC50 values ranged between 266.27 ± 6.91 to 695.17 ± 15.38 μg/mL. Conclusions The tested Australian medicinal plant extracts inhibit glucose-induced fluorescent AGEs, α-amylase, α-glucosidase and ACE with extracts of Petalostigma species showing the most promising activity. These medicinal plants could potentially be further developed as therapeutic agents in the treatment of hyperglycaemia and hypertension

AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids

Electricity theft (ET) is an utmost problem for power utilities because it threatens public safety, disturbs the normal working of grid infrastructure and increases revenue losses. In the literature, many machine learning (ML), deep learning (DL) and statistical based models are introduced to detect ET. However, these models do not give optimal results due to the following reasons: curse of dimensionality, class imbalance problem, inappropriate hyper-parameter tuning of ML and DL models, etc. Keeping the aforementioned concerns in view, we introduce a hybrid DL model for the efficient detection of electricity thieves in smart grids. AlexNet is utilized to handle the curse of dimensionality issue while the final classification of energy thieves and normal consumers is performed through adaptive boosting (AdaBoost). Moreover, class imbalance problem is resolved using an undersampling technique, named as near miss. Furthermore, hyper-parameters of AdaBoost and AlexNet are tuned using artificial bee colony optimization algorithm. The real smart meters' dataset is used to assess the efficacy of the hybrid model. The substantial amount of simulations proves that the hybrid model obtains the highest classification results as compared to its counterparts. Our proposed model obtains 88%, 86%, 84%, 85%, 78% and 91% accuracy, precision, recall, F1-score, Matthew correlation coefficient and area under the curve receiver operating characteristics, respectively

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications

The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture

Population‐based cohort study of outcomes following cholecystectomy for benign gallbladder diseases

Background The aim was to describe the management of benign gallbladder disease and identify characteristics associated with all‐cause 30‐day readmissions and complications in a prospective population‐based cohort. Methods Data were collected on consecutive patients undergoing cholecystectomy in acute UK and Irish hospitals between 1 March and 1 May 2014. Potential explanatory variables influencing all‐cause 30‐day readmissions and complications were analysed by means of multilevel, multivariable logistic regression modelling using a two‐level hierarchical structure with patients (level 1) nested within hospitals (level 2). Results Data were collected on 8909 patients undergoing cholecystectomy from 167 hospitals. Some 1451 cholecystectomies (16·3 per cent) were performed as an emergency, 4165 (46·8 per cent) as elective operations, and 3293 patients (37·0 per cent) had had at least one previous emergency admission, but had surgery on a delayed basis. The readmission and complication rates at 30 days were 7·1 per cent (633 of 8909) and 10·8 per cent (962 of 8909) respectively. Both readmissions and complications were independently associated with increasing ASA fitness grade, duration of surgery, and increasing numbers of emergency admissions with gallbladder disease before cholecystectomy. No identifiable hospital characteristics were linked to readmissions and complications. Conclusion Readmissions and complications following cholecystectomy are common and associated with patient and disease characteristics

A Secure and Efficient Energy Trading Model Using Blockchain for a 5G-Deployed Smart Community

A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs' charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain-based privacy-preserving energy trading system for 5G-deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation-based distributed matching algorithm for EVs and a Reward-based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short-term load forecasting model for EVs' charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity-based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs' component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers' component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs' charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy-preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug-free against the common vulnerabilities' attacks. The results show that the smart contracts are secure against both internal and external attacks

Know Yourself:An Adaptive Causal Network Model for Therapeutic Intervention for Regaining Cognitive Control

Part 6: Medical-Health SystemsInternational audienceLong term stress often causes depression and neuronal atrophies that in turn can lead to a variety of health problems. As a result of these cellular changes, also molecular changes occur. These changes, that include increase of glucocorticoids and decrease of the brain-derived neurotrophic factor, have the unfortunate effect that they decrease the cognitive abilities needed for the individual to solve the stressful situation. Such cognitive abilities like reappraisal and their adaptation mechanisms turn out to be substantially impaired while they are needed for regulation of the negative emotions. However, antidepressant treatments and some other therapies have proved to be quite effective for the strengthening of such cognitive abilities. This study introduces an adaptive causal network model for this phenomenon where a subject loses his or her cognitive abilities (negative metaplasticity) due to long-term stress and re-improve these cognitive abilities (positive metaplasticity) through mindfulness-based cognitive therapy (MBCT). Simulation results have been reported for demonstration of the phenomenon

Current trends in the surgical management of Dupuytren’s disease in Europe: an analysis of patient charts

Introduction: Dupuytren's disease (DD) causes progressive digital flexion contracture and is more common in men of European descent. Methods: Orthopaedic and plastic surgeons in 12 European countries (the Czech Republic, Denmark, Finland, France, Germany, Hungary, Italy, The Netherlands, Poland, Spain, Sweden and the UK) with >3 and <30 years experience reviewed the medical charts of five consecutive patients they had treated surgically for DD in 2008. Descriptive statistics are reported. Results: In total, 3,357 patient charts were reviewed. Mean (standard deviation) patient age was 61.9 (10.2) years; 81% were men. At the time of the procedure, 11% of patients were at Tubiana stage Ia (0-20° total flexion); 30%, stage Ib (21-45°); 34%, stage II (46-90°); 17%, stage III (91-135°); and 5%, stage IV (&135°). Percutaneous needle fasciotomy was performed in 10%, fasciotomy in 13%, fasciectomy in 69% and dermofasciectomy (DF) in 6% of patients. After surgery, fingers improved a mean of 1.9 Tubiana stages, and 54% of patients had no nodules or contracture. The rate of reported complications during the procedure was 4% overall (11% in patients undergoing DF). The most common postoperative complications reported were haematoma (8%), wound healing complications (6%) and pain (6%). No postoperative complications were reported in 77% of patients. Conclusions: In this European study of more than 3,000 patients with DD, most patients were diagnosed at Tubiana stage I or II, the majority received fasciectomy and more than half had no nodules or contracture remaining after surgery