A new mechanism of voltage-dependent gating exposed by KV10.1 channels interrupted between voltage sensor and pore

Voltage-gated ion channels couple transmembrane potential changes to ion flow. Conformational changes in the voltage-sensing domain (VSD) of the channel are thought to be transmitted to the pore domain (PD) through an α-helical linker between them (S4-S5 linker). However, our recent work on channels disrupted in the S4-S5 linker has challenged this interpretation for the KCNH family. Furthermore, a recent single-particle cryo-electron microscopy structure of KV10.1 revealed that the S4-S5 linker is a short loop in this KCNH family member, confirming the need for an alternative gating model. Here we use "split" channels made by expression of VSD and PD as separate fragments to investigate the mechanism of gating in KV10.1. We find that disruption of the covalent connection within the S4 helix compromises the ability of channels to close at negative voltage, whereas disconnecting the S4-S5 linker from S5 slows down activation and deactivation kinetics. Surprisingly, voltage-clamp fluorometry and MTS accessibility assays show that the motion of the S4 voltage sensor is virtually unaffected when VSD and PD are not covalently bound. Finally, experiments using constitutively open PD mutants suggest that the presence of the VSD is structurally important for the conducting conformation of the pore. Collectively, our observations offer partial support to the gating model that assumes that an inward motion of the C-terminal S4 helix, rather than the S4-S5 linker, closes the channel gate, while also suggesting that control of the pore by the voltage sensor involves more than one mechanism

Positive Feedback between Transcriptional and Kinase Suppression in Nematodes with Extraordinary Longevity and Stress Resistance

Insulin/IGF-1 signaling (IIS) regulates development and metabolism, and modulates aging, of Caenorhabditis elegans. In nematodes, as in mammals, IIS is understood to operate through a kinase-phosphorylation cascade that inactivates the DAF-16/FOXO transcription factor. Situated at the center of this pathway, phosphatidylinositol 3-kinase (PI3K) phosphorylates PIP2 to form PIP3, a phospholipid required for membrane tethering and activation of many signaling molecules. Nonsense mutants of age-1, the nematode gene encoding the class-I catalytic subunit of PI3K, produce only a truncated protein lacking the kinase domain, and yet confer 10-fold greater longevity on second-generation (F2) homozygotes, and comparable gains in stress resistance. Their F1 parents, like weaker age-1 mutants, are far less robust—implying that maternally contributed trace amounts of PI3K activity or of PIP3 block the extreme age-1 phenotypes. We find that F2-mutant adults have <10% of wild-type kinase activity in vitro and <60% of normal phosphoprotein levels in vivo. Inactivation of PI3K not only disrupts PIP3-dependent kinase signaling, but surprisingly also attenuates transcripts of numerous IIS components, even upstream of PI3K, and those of signaling molecules that cross-talk with IIS. The age-1(mg44) nonsense mutation results, in F2 adults, in changes to kinase profiles and to expression levels of multiple transcripts that distinguish this mutant from F1 age-1 homozygotes, a weaker age-1 mutant, or wild-type adults. Most but not all of those changes are reversed by a second mutation to daf-16, implicating both DAF-16/ FOXO–dependent and –independent mechanisms. RNAi, silencing genes that are downregulated in long-lived worms, improves oxidative-stress resistance of wild-type adults. It is therefore plausible that attenuation of those genes in age-1(mg44)-F2 adults contributes to their exceptional survival. IIS in nematodes (and presumably in other species) thus involves transcriptional as well as kinase regulation in a positive-feedback circuit, favoring either survival or reproduction. Hyperlongevity of strong age-1(mg44) mutants may result from their inability to reset this molecular switch to the reproductive mode

Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer's Disease

© 2020 IEEE. Alzheimer's disease (AD) is a neurodegenerative disorder resulting in memory loss and cognitive decline caused due to the death of brain cells. It is the most common form of dementia and accounts for 60-80% of all dementia cases. There is no single test for diagnosis of AD, the doctors rely on medical history, neuropsychological assessments, computed tomography (CT) or magnetic resonance imaging (MRI) scan of the brain, etc. to confirm a diagnosis. In terms of the treatment, currently, there is neither a cure nor any way to slow the progression of AD. However, for people with mild or moderate stages of this disease, there are some medications available to temporarily reduce symptoms and help to improve quality of life. Hence, early diagnosis of AD is extremely crucial for overall better management of the disease. The researches have shown some relation between neuropsychological scores and atrophies of the brain. This can be leveraged for the early diagnosis of AD. This paper makes use of feature selection techniques to extract the most important features in the diagnosis of AD. This paper demonstrates the need to combine neuropsychological scores like mini-mental state examination (MMSE) with MRI features to provide better decisional space for early diagnosis of AD. Through the experiments, including MMSE along with other features are found to improve the classification of AD, significantly

Fuzzy LogicHybrid model with semantic filtering approach for pseudo relevance feedback-based query expansion

© 2017 IEEE. Individual query expansion term selection methods have been widely investigated in an attempt to improve their performance. Each expansion term selection method has its own weaknesses and strengths. To overcome the weaknesses and utilize the strengths of individual methods, this paper combined multiple term selection methods. In this paper, initially the possibility of improving the overall performance using individual query expansion (QE) term selection methods are explored. Secondly, some well-known rank aggregation approaches are used for combining multiple QE term selection methods. Thirdly, a new fuzzy logic-based QE approach that considers the relevance score produced by different rank aggregation approaches is proposed. The proposed fuzzy logic approach combines different weights of each term using fuzzy rules to infer the weights of the additional query terms. Finally, Word2vec approach is used to filter semantically irrelevant terms obtained after applying the fuzzy logic approach. The experimental results demonstrate that the proposed approaches achieve significant improvements over each individual term selection method, aggregated method and related state-of-the-art method

A new mechanism of voltage-dependent gating exposed by KV10.1 channels interrupted between voltage sensor and pore.

Voltage-gated ion channels couple transmembrane potential changes to ion flow. Conformational changes in the voltage-sensing domain (VSD) of the channel are thought to be transmitted to the pore domain (PD) through an α-helical linker between them (S4-S5 linker). However, our recent work on channels disrupted in the S4-S5 linker has challenged this interpretation for the KCNH family. Furthermore, a recent single-particle cryo-electron microscopy structure of KV10.1 revealed that the S4-S5 linker is a short loop in this KCNH family member, confirming the need for an alternative gating model. Here we use "split" channels made by expression of VSD and PD as separate fragments to investigate the mechanism of gating in KV10.1. We find that disruption of the covalent connection within the S4 helix compromises the ability of channels to close at negative voltage, whereas disconnecting the S4-S5 linker from S5 slows down activation and deactivation kinetics. Surprisingly, voltage-clamp fluorometry and MTS accessibility assays show that the motion of the S4 voltage sensor is virtually unaffected when VSD and PD are not covalently bound. Finally, experiments using constitutively open PD mutants suggest that the presence of the VSD is structurally important for the conducting conformation of the pore. Collectively, our observations offer partial support to the gating model that assumes that an inward motion of the C-terminal S4 helix, rather than the S4-S5 linker, closes the channel gate, while also suggesting that control of the pore by the voltage sensor involves more than one mechanism

Clustering‐based real‐time anomaly detection—A breakthrough in big data technologies

Off late, the ever increasing usage of a connected Internet-of-Things devices has consequently augmented the volume of real-time network data with high velocity. At the same time, threats on networks become inevitable; hence, identifying anomalies in real time network data has become crucial. To date, most of the existing anomaly detection approaches focus mainly on machine learning techniques for batch processing. Meanwhile, detection approaches which focus on the real-time analytics somehow deficient in its detection accuracy while consuming higher memory and longer execution time. As such, this paper proposes a novel framework which focuses on real-time anomaly detection based on big data technologies. In addition, this paper has also developed streaming sliding window local outlier factor coreset clustering algorithms (SSWLOFCC), which was then implemented into the framework. The proposed framework that comprises BroIDS, Flume, Kafka, Spark streaming, SparkMLlib, Matplot and HBase was evaluated to substantiate its efficacy, particularly in terms of accuracy, memory consumption, and execution time. The evaluation is done by performing critical comparative analysis using existing approaches, such as K-means, hierarchical density-based spatial clustering of applications with noise (HDBSCAN), isolation forest, spectral clustering and agglomerative clustering. Moreover, Adjusted Rand Index and memory profiler package were used for the evaluation of the proposed framework against the existing approaches. The outcome of the evaluation has substantially proven the efficacy of the proposed framework with a much higher accuracy rate of 96.51% when compared to other algorithms. Besides, the proposed framework also outperformed the existing algorithms in terms of lesser memory consumption and execution time. Ultimately the proposed solution enable analysts to precisely track and detect anomalies in real time. © 2019 John Wiley & Sons, Ltd