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- Pantazis, A., et al.
(författare)
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Tracking the motion of the KV1.2 voltage sensor reveals the molecular perturbations caused by a de novo mutation in a case of epilepsy
- 2020
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Ingår i: Journal of Physiology. - : Blackwell Publishing Ltd. - 0022-3751 .- 1469-7793.
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Tidskriftsartikel (refereegranskat)abstract
- Key points: KV1.2 channels, encoded by the KCNA2 gene, regulate neuronal excitability by conducting K+ upon depolarization. A new KCNA2 missense variant was discovered in a patient with epilepsy, causing amino acid substitution F302L at helix S4, in the KV1.2 voltage-sensing domain. Immunocytochemistry and flow cytometry showed that F302L does not impair KCNA2 subunit surface trafficking. Molecular dynamics simulations indicated that F302L alters the exposure of S4 residues to membrane lipids. Voltage clamp fluorometry revealed that the voltage-sensing domain of KV1.2-F302L channels is more sensitive to depolarization. Accordingly, KV1.2-F302L channels opened faster and at more negative potentials; however, they also exhibited enhanced inactivation: that is, F302L causes both gain- and loss-of-function effects. Coexpression of KCNA2-WT and -F302L did not fully rescue these effects. The proband's symptoms are more characteristic of patients with loss of KCNA2 function. Enhanced KV1.2 inactivation could lead to increased synaptic release in excitatory neurons, steering neuronal circuits towards epilepsy. Abstract: An exome-based diagnostic panel in an infant with epilepsy revealed a previously unreported de novo missense variant in KCNA2, which encodes voltage-gated K+ channel KV1.2. This variant causes substitution F302L, in helix S4 of the KV1.2 voltage-sensing domain (VSD). F302L does not affect KCNA2 subunit membrane trafficking. However, it does alter channel functional properties, accelerating channel opening at more hyperpolarized membrane potentials, indicating gain of function. F302L also caused loss of KV1.2 function via accelerated inactivation onset, decelerated recovery and shifted inactivation voltage dependence to more negative potentials. These effects, which are not fully rescued by coexpression of wild-type and mutant KCNA2 subunits, probably result from the enhancement of VSD function, as demonstrated by optically tracking VSD depolarization-evoked conformational rearrangements. In turn, molecular dynamics simulations suggest altered VSD exposure to membrane lipids. Compared to other encephalopathy patients with KCNA2 mutations, the proband exhibits mild neurological impairment, more characteristic of patients with KCNA2 loss of function. Based on this information, we propose a mechanism of epileptogenesis based on enhanced KV1.2 inactivation leading to increased synaptic release preferentially in excitatory neurons, and hence the perturbation of the excitatory/inhibitory balance of neuronal circuits.
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- Ali, Mai, et al.
(författare)
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Autonomous Patient/Home Health Monitoring powered by Energy Harvesting
- 2017
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Ingår i: Globecom 2017 - 2017 IEEE Global Communications Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509050192
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Konferensbidrag (refereegranskat)abstract
- This paper presents the design of an autonomous smart patient/home health monitoring system. Both patient physiological parameters as well as room conditions are being monitored continuously to insure patient safety. The sensors are connected on an IoT regime, where the collected data is wirelessly transferred to a nearby gateway which performs preliminary data analysis, commonly referred to as fog computing, to make sure emergency personnel and healthcare providers are notified in case patient being monitored is at risk. To achieve power autonomy three energy harvesting sources are proposed, namely, solar, RF and thermal. The design of the RF energy harvesting system is demonstrated, where novel multiband antenna is fabricated as well as an efficient RF-DC rectifier achieving maximum conversion efficiency of 84%. Finally, the sensor node is tested with different type of sensors and settings while being solely powered by a Photovoltaic (PV) solar cell.
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| 7. |
- Fleetwood, Oliver, et al.
(författare)
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Molecular Insights from Conformational Ensembles via Machine Learning
- 2020
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Ingår i: Biophysical Journal. - : Biophysical Society. - 0006-3495 .- 1542-0086. ; 118:3, s. 765-780
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Tidskriftsartikel (refereegranskat)abstract
- Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size. Extracting important features from the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized as resembling black boxes with limited human-interpretable insight. We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods, including neural networks, random forests, and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor, and activation of an ion channel voltage-sensor domain, unraveling features critical for signal transduction, ligand binding, and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations.
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| 8. |
- Gia, T. N., et al.
(författare)
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Fault tolerant and scalable IoT-based architecture for health monitoring
- 2015
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Ingår i: SAS 2015 - 2015 IEEE Sensors Applications Symposium, Proceedings. - : IEEE conference proceedings. - 9781479961160 ; , s. 334-339
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Konferensbidrag (refereegranskat)abstract
- A novel Internet of Things based architecture supporting scalability and fault tolerance for healthcare is presented in this paper. The wireless system is constructed on top of 6LoWPAN energy efficient communication infrastructure to maximize the operation time. Fault tolerance is achieved via backup routing between nodes and advanced service mechanisms to maintain connectivity in case of failing connections between system nodes. The presented fault tolerance approach covers many fault situations such as malfunction of sink node hardware and traffic bottleneck at a node due to a high receiving data rate. A method for extending the number of medical sensing nodes at a single gateway is presented. A complete system architecture providing a quantity of features from bio-signal acquisition such as Electrocardiogram (ECG), Electroencephalography (EEG), and Electromyography (EMG) to the representation of graphical waveforms of these gathered bio-signals for remote real-time monitoring is proposed.
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| 9. |
- Gia, T. N., et al.
(författare)
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Fog computing in healthcare Internet of Things : A case study on ECG feature extraction
- 2015
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Ingår i: Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509001545 ; , s. 356-363
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Konferensbidrag (refereegranskat)abstract
- Internet of Things technology provides a competent and structured approach to improve health and wellbeing of mankind. One of the feasible ways to offer healthcare services based on IoT is to monitor humans health in real-time using ubiquitous health monitoring systems which have the ability to acquire bio-signals from sensor nodes and send the data to the gateway via a particular wireless communication protocol. The real-time data is then transmitted to a remote cloud server for real-time processing, visualization, and diagnosis. In this paper, we enhance such a health monitoring system by exploiting the concept of fog computing at smart gateways providing advanced techniques and services such as embedded data mining, distributed storage, and notification service at the edge of network. Particularly, we choose Electrocardiogram (ECG) feature extraction as the case study as it plays an important role in diagnosis of many cardiac diseases. ECG signals are analyzed in smart gateways with features extracted including heart rate, P wave and T wave via a flexible template based on a lightweight wavelet transform mechanism. Our experimental results reveal that fog computing helps achieving more than 90% bandwidth efficiency and offering low-latency real time response at the edge of the network.
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| 10. |
- Gia, Tuan Nguyen, et al.
(författare)
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IoT-Based Fall Detection System with Energy Efficient Sensor Nodes
- 2016
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Ingår i: 2016 2ND IEEE NORDIC CIRCUITS AND SYSTEMS CONFERENCE (NORCAS). - : IEEE conference proceedings. - 9781509010950
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Konferensbidrag (refereegranskat)abstract
- Fall needs to be attentively considered due to its highly frequent occurrence especially with old people - up to one third of 65 and above year-old people around the world are risk of being injured due to falling. Furthermore, fall is a direct or indirect factor causing severe traumas such as brain injuries or bone fractures. However, timely medical attention might help to avoid serious consequences from a fall. A viable solution to solve this is an IoT-based system which takes advantage of wireless sensor networks, wearable devices, Fog and Cloud computing. To deliver sufficient degree of reliability, wearable devices working at the core of a fall detection system, are required to work for prolonged period of time. In this paper we investigate energy consumption of sensor nodes in an IoT-based fall detection system and present a design of a customized sensor node. In addition, we compare the customized sensor node with other sensor nodes, built on general purpose development boards. The results show that sensor nodes based on delicate customized devices are more energy efficient than the others based on general purpose devices while considering identical specification of micro-controller and memory capacity. Furthermore, our customized sensor node with energy efficiency selections can operate continuously up to 35 hours.
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