Selectivity Filter Research Articles

Can Ag+ Permeate through a Potassium Ion Channel? A Bottom-Up Approach by Infrared Spectroscopy of the Ag+ Complex with the Partial Peptide of a Selectivity Filter.

Silver and silver ions have a long history of antimicrobial activity and medical applications. Nevertheless, the activity of Ag+ against bacteria, how it enters a cell, has not yet been established. The K+ channel, a membrane protein, is a possible route. The addition of a channel inhibitor (4-aminopyridine) to modulate the Ag+ uptake could support this view. However, the inhibitor enhances the uptake of Ag+, the opposite result. We have applied cold ion trap infrared laser spectroscopy to complexes of Ag+ and Ac-Tyr-NHMe (a model for GYG) which is a portion of the selectivity filter in the K+ channel to consider the question of permeation. With support from quantum chemical calculations, we have determined the stable conformations of the complex. The conformations strongly suggest that Ag+ would not readily permeate the K+ channel. The mechanism of the unexpected enhancement by the inhibitor is discussed.

Mechanism underlying delayed rectifying in human voltage-mediated activation Eag2 channel

The transmembrane voltage gradient is a general physico-chemical cue that regulates diverse biological function through voltage-gated ion channels. How voltage sensing mediates ion flows remains unknown at the molecular level. Here, we report six conformations of the human Eag2 (hEag2) ranging from closed, pre-open, open, and pore dilation but non-conducting states captured by cryo-electron microscopy (cryo-EM). These multiple states illuminate dynamics of the selectivity filter and ion permeation pathway with delayed rectifier properties and Cole-Moore effect at the atomic level. Mechanistically, a short S4-S5 linker is coupled with the constrict sites to mediate voltage transducing in a non-domain-swapped configuration, resulting transitions for constrict sites of F464 and Q472 from gating to open state stabilizing for voltage energy transduction. Meanwhile, an additional potassium ion occupied at positions S6 confers the delayed rectifier property and Cole-Moore effects. These results provide insight into voltage transducing and potassium current across membrane, and shed light on the long-sought Cole-Moore effects.

The Effect of TWIK-1 Two Pore Potassium Channels on Cardiomyocytes in Low Extracellular Potassium Conditions.

At low extracellular potassium ([K+]e) conditions, human cardiomyocytes can depolarize to -40 mV. This is closely related to hypokalemia-induced fatal cardiac arrhythmia. The underlying mechanism, however, is still not well understood. TWIK-1 channels are background K+ channels that are highly expressed in human cardiomyocytes. We previously reported that TWIK-1 channels changed ion selectivity and conducted leak Na+ currents at low [K+]e. Moreover, a specific threonine residue (Thr118) within the ion selectivity filter was responsible for this altered ion selectivity. Patch clamp were used to investigate the effects of TWIK-1 channels on the membrane potentials of cardiomyocytes in response to low [K+]e. At 2.7 mM [K+]e and 1 mM [K+]e, both Chinese hamster ovary (CHO) cells and HL-1 cells ectopically expressed human TWIK-1 channels displayed inward leak Na+ currents and reconstitute depolarization of membrane potential. In contrast, cells ectopically expressed human TWIK-1-T118I mutant channels that remain high selectivity to K+ exhibited hyperpolarization of membrane potential. Furthermore, human iPSC-derived cardiomyocytes showed depolarization of membrane potential in response to 1 mM [K+]e, while the knockdown of TWIK-1 expression eliminated this phenomenon. These results demonstrate that leak Na+ currents conducted by TWIK-1 channels contribute to the depolarization of membrane potential induced by low [K+]e in human cardiomyocytes.

Molecular mechanism of adsorbing triclocarban by the activated sludge in wastewater treatment systems

The ubiquitous use of antimicrobial triclocarban (TCC) has raised widespread concern about the potential impacts on wastewater treatment systems, the environment, and the human population. As important constituents of activated sludge secretions, extracellular polymeric substances (EPS) play essential roles in removing micropollutants during wastewater treatment. Here, the adsorption of TCC by sludge and EPS in a multistage biological process was investigated. The EPS was enriched upon TCC stress, and the adsorption by sludge and EPS showed multilayer heterogeneity. The adsorption constant Kf (6.3794–7.6061) of EPS was over 10 times higher than that of sludge (0.5661–0.6200) in all stages, potentially owing to the loose multilayer structure and substantial functional groups (tryptophan-protein as the main binding site) in EPS. TCC significantly inhibited the biological removal of ammonia nitrogen. Notably, TCC once reaching the cell surface, formed a stable complex with ammonia transporter (Amt, binding energy: −73.1 ± 16.6 kJ·mol−1) and bound to the selectivity filter vestibule of Amt (tryptophan 144 and serine 227), inhibiting the transport of ammonia. Our work drew molecular-level insights into the interaction between TCC and activated sludge, and evaluated the adverse impacts on treatment performance through an in silico approach, providing new information on the fate and risk of TCC in wastewater treatment systems.

DbAQP-SNP: a database of missense single-nucleotide polymorphisms in human aquaporins.

Aquaporins and aquaglyceroporins belong to the superfamily of major intrinsic proteins (MIPs), and they transport water and other neutral solutes such as glycerol. These channel proteins are involved in vital physiological processes and are implicated in several human diseases. Experimentally determined structures of MIPs from diverse organisms reveal a unique hour-glass fold with six transmembrane helices and two half-helices. MIP channels have two constrictions formed by Asn-Pro-Ala (NPA) motifs and aromatic/arginine selectivity filters (Ar/R SFs). Several reports have found associations among single-nucleotide polymorphisms (SNPs) in human aquaporins (AQPs) with diseases in specific populations. In this study, we have compiled 2798 SNPs that give rise to missense mutations in 13 human AQPs. To understand the nature of missense substitutions, we have systematically analyzed the pattern of substitutions. We found several examples in which substitutions could be considered as non-conservative that include small to big or hydrophobic to charged residues. We also analyzed these substitutions in the context of structure. We have identified SNPs that occur in NPA motifs or Ar/R SFs, and they will most certainly disrupt the structure and/or transport properties of human AQPs. We found 22 examples in which missense SNP substitutions that are mostly non-conservative in nature have given rise to pathogenic conditions as found in the Online Mendelian Inheritance in Man database. It is most likely that not all missense SNPs in human AQPs will result in diseases. However, understanding the effect of missense SNPs on the structure and function of human AQPs is important. In this direction, we have developed a database dbAQP-SNP that contains information about all 2798 SNPs. This database has several features and search options that can help the user to find SNPs in specific positions of human AQPs including the functionally and/or structurally important regions. dbAQP-SNP (http://bioinfo.iitk.ac.in/dbAQP-SNP) is freely available to the academic community. Database URL http://bioinfo.iitk.ac.in/dbAQP-SNP.

A Multi-level Random Forest Model-Based Intrusion Detection Using Fuzzy Inference System for Internet of Things Networks

Intrusion detection (ID) methods are security frameworks designed to safeguard network information systems. The strength of an intrusion detection method is dependent on the robustness of the feature selection method. This study developed a multi-level random forest algorithm for intrusion detection using a fuzzy inference system. The strengths of the filter and wrapper approaches are combined in this work to create a more advanced multi-level feature selection technique, which strengthens network security. The first stage of the multi-level feature selection is the filter method using a correlation-based feature selection to select essential features based on the multi-collinearity in the data. The correlation-based feature selection used a genetic search method to choose the best features from the feature set. The genetic search algorithm assesses the merits of each attribute, which then delivers the characteristics with the highest fitness values for selection. A rule assessment has also been used to determine whether two feature subsets have the same fitness value, which ultimately returns the feature subset with the fewest features. The second stage is a wrapper method based on the sequential forward selection method to further select top features based on the accuracy of the baseline classifier. The selected top features serve as input into the random forest algorithm for detecting intrusions. Finally, fuzzy logic was used to classify intrusions as either normal, low, medium, or high to reduce misclassification. When the developed intrusion method was compared to other existing models using the same dataset, the results revealed a higher accuracy, precision, sensitivity, specificity, and F1-score of 99.46%, 99.46%, 99.46%, 93.86%, and 99.46%, respectively. The classification of attacks using the fuzzy inference system also indicates that the developed method can correctly classify attacks with reduced misclassification. The use of a multi-level feature selection method to leverage the advantages of filter and wrapper feature selection methods and fuzzy logic for intrusion classification makes this study unique.

High selectivity and compact tunable bandpass filter using YIG material

This paper presents a novel highly selectivity tunable filter with transmission zero. Yttrium iron garnet/gadolinium gallium garnet layer structures are added to the filter structure as bandstop units to achieve transmission zero. CST Studio Suite simulates and optimizes the filter structural parameters. Transmission zero and filter passband are tuned simultaneously. Skirt selectivity can be significantly increased by placing the transmission zero at the lower side of the filter passband. The parasitic response can be suppressed by about 20 dBc when the transmission zero is located on the upper side of the filter passband. With the magnetic field shifted from 3250 to 5005 Oe, the filter tuning range is 11–16 GHz with an insertion loss of about 3.5 dB. The addition of transmission zero enhances filter selectivity and provides more flexibility in suppressing interfering signals that vary in frequency or parasitic responses at specific frequencies. This method presents an approach for designing wide tuning range filters with transmission zeros.

Glee: A granularity filter for feature selection

In the field of Granular Computing (GrC), feature selection is an attractive task. Some basics of GrC such as information granulation and granularity have well guided the explorations of feature selection technique. Nevertheless, some challenges to perform feature selection such as high computational costs of repeating information granulation and multi-granularity structure in the process of searching qualified features may also arise. In this study, to improve the efficiency and effectiveness of feature selector, a novel framework named Glee: Granularity fiLter for fEature sElection, is reported. Firstly, the value of granularity related to each feature is calculated. Secondly, all features are reordered by the corresponding values of granularity. Finally, following such a derived sequence, features will be added into the selection pool one by one until the termination condition is achieved. Glee can not only eliminate the iterative calculations of information granulation in the whole process of selecting, but also provide sequence of features which may be insensitive to data perturbation. It should also be emphasized that Glee is a general framework, and most existing termination conditions to feature selection can be embedded into it. To validate the effectiveness of Glee, it is compared with several well-established feature selection schemes in elapsed time of selecting features, stability of selected features and classification performance. Experimental results over 20 UCI datasets with both raw features and 3 different ratios of noisy features demonstrate that our framework is superior as it yields better robustness with satisfactory elapsed time.

Verification of Characteristics of Spline Filter Series Robust Filters for Surface Roughness and Proposal of Filter Selection Guidelines

In surface roughness measurements, the presence of spikes in the primary profile can disable a normal filter from extracting the shape and waviness components. The robust spline filter (RSF) has been proposed to solve this problem. However, because ISO 16610-32, an RSF standard, has a significant deficiency, it was withdrawn in the Technical Specifications stage. The other proposed RSFs only show the method and a few examples with normal roughness and spike. Therefore, because the characteristics of each RSF have not been clarified, which RSF should be used for which primary profiles is not clear. This is one of the reasons for the delay in establishing ISO. In addition, the measurement sites need the establishment of robust spline filter selection guidelines. Therefore, in this paper, the characteristics of RSFs are clarified. Additionally, the points to be considered in using RSFs are summarized. Finally, the filter that should be selected for the specific application is identified. These results are expected to contribute to the widespread use of RSFs in the industrial world, avoid confusion at the measurement site, and help establish a new RSF standard.

Joint think locally and globally: Communication-efficient federated learning with feature-aligned filter selection

Federated learning, as a distributed machine learning framework that can protect data privacy, has recently received increasing attention. In federated learning, a shared global model is obtained through parameter interaction, which leads to frequent parameter communication during the training process. The limited bandwidth of IoT and edge devices that deploy federated learning further affects communication and learning efficiency. In this paper, an enhanced federated learning technique is presented by proposing a feature-aligned filter selection method. Besides, it is believed that the training gap between the global model and the local model on each node should be focused on during the training process. Then, we can select the local contribution parameters to improve the communication efficiency while ensuring the performance of the shared global model. Therefore, the Geometric Median of each layer in the global model is adopted as the criterion to select important filters in the local model, and then the parameters interact with other nodes to achieve efficient communication. The results under a variety of experimental settings demonstrate that our proposed federated learning scheme can effectively enhance communication efficiency and ensure the performance of the global model. Compared with the state-of-the-art methods, a maximum of 6.5× improvements in communication efficiency can be obtained.

An aluminum coated all-fiber mirror and a Mach–Zehnder interferometer for developing a switchable and tunable multi-wavelength laser

In this paper, a tunable and switchable multi-wavelength erbium doped linear fiber laser, based on an optical fiber loop mirror containing a Mach–Zehnder interferometer (MZI) and an aluminum coated optical fiber cleaved end is presented. The MZI, obtained by core-offset splicing a piece of photonic crystal fiber (PCF) between two segments of a single mode fiber (SMF), acts as a wavelength selective filter (WSF). The full reflecting mirror implemented is obtained by thermal evaporation of aluminum on a cleaved fiber end, technique which has not been deeply explored for compact fiber laser configurations. In this way, both optical loop mirror and the aluminum coated fiber work together to make a resonant cavity. By rotating the wave plates of a polarization controller, the polarization state of the cavity modes diversifies, and the laser setup can emit one, two, three or four laser wavelengths, which can be switched from 1526 to 1562 nm. In addition, this laser arrangement can be tuned within a 5 nm interval and has a side-mode suppression ratio of 42 dB for single-wavelength operation. Finally, the proposed laser configuration achieved a high stability for more than an hour at room temperature.

A Poisson-Nernst-Planck single ion channel model and its effective finite element solver

A single ion channel is a membrane protein with an ion selectivity filter that allows only a single species of ions (such as potassium ions) to pass through in the “open” state. Its selectivity filter also naturally separates a solvent domain into an intracellular domain and an extracellular domain. Such biological and geometrical characteristics of a single ion channel are novelly adopted in the construction of a new kind of dielectric continuum ion channel model, called the Poisson-Nernst-Planck single ion channel (PNPSIC) model, in this paper. An effective PNPSIC finite element solver is then developed and implemented as a software package workable for a single ion channel with a three-dimensional X-ray crystallographic molecular structure and a mixture of multiple ionic species. Numerical results for a potassium channel confirm the convergence and efficiency of the PNPSIC finite element solver and demonstrate the high performance of the software package. Moreover, the PNPSIC model is applied to the calculation of electric current and validated by biophysical experimental data.

Mass flows and characteristic of mercury emitted from coal-fired power plant equipped with seawater flue gas desulphurization

This study investigated the characteristics of mercury emitted from a large-scale coal-fired power plant in Taiwan via intensive sampling campaigns. The air pollution control devices equipped in this coal-fired power plant include selective catalytic reduction, fabric filter and seawater flue gas desulfurization. During three sampling campaigns, mercury concentrations emitted from this large-scale coal fired power plant ranged from 0.04 to 0.35 μg Nm−3. In addition, solid and liquid samples including coal, bottom ash, fly ash and inlet/outlet seawater were collected and analyzed for mercury content to provide the mass flows of mercury in this plant. The results indicate that mercury emitted from this coal-fired power plant is dominated by Hg0, and the emission factor of mercury ranges from 0.37 to 3.37 mg Hg t−1 coal, which is relatively lower than those reported in other studies. The relative enrichment factor of mercury in fly ash (0.23–1.22) is significantly higher than that of bottom ash (0.10 × 10–3–0.89 × 10–3). Although this coal-fired power plant has a relatively low mercury atmospheric emission which accounts for 6.4% of the total mercury output, it is essential to further control mercury released from fly ash (62.6% of the total mercury output) and discharged seawater (33.7% of the total mercury output) to reduce environmental damage and human health effects.

Comparing filter and wrapper approaches for feature selection in handwritten character recognition

It is generally agreed that the selection of an appropriate set of features is a fundamental process in the development of any pattern recognition system. Its purpose is to identify the truly distinctive subset of features to reduce the size of the search space, without decreasing the classification performance. This problem is particularly relevant in the field of handwriting recognition, due to the enormous variability of character shape, which has led to the development of a large variety of feature sets that are becoming increasingly larger in terms of the number of attributes. While promising, the results achieved so far have several limitations, which include, among others, the computational complexity of selecting and evaluating feature subsets and the difficulty in evaluating the interactions among features. In a previous study, we tried to overcome some of the above limitations by adopting a feature-ranking-based technique: a large study was carried out considering different filter-based techniques for feature subset evaluation. The aim of this work is to extend the previous study by presenting a broad comparison between filter and wrapper techniques for feature selection in the field of handwritten character recognition. In the experiments, we analysed one of the most effective and widely used set of features in handwriting recognition, applied to standard real-word databases of handwritten characters. The experimental results confirmed that filter and wrapper approaches achieve similar performances, with the former selecting fewer features at a lower computational cost.

Reduced processing efficiency impacts auditory detection of amplitude modulation in children: Evidence from an experimental and modelling study

Detection of amplitude modulations (AM) improves until 10 years of age. This development may not be explained only by sensory maturation but also by improvements in processing efficiency: the ability to make efficient use of available sensory information. This hypothesis was tested on 86 6-to-9-year-olds and 15 adults using AM-detection tasks assessing sensitivity, masking and response consistency. Sensitivity was estimated by the detection thresholds of a sinusoidal AM applied to a pure-tone carrier; AM masking was estimated as threshold elevation produced when replacing the pure-tone carrier by a narrowband noise; response consistency was estimated using a double-pass paradigm where the same set of stimuli was presented twice. Results showed that AM sensitivity improved from childhood to adulthood. AM masking did not change with age, indicating that the selectivity of AM filters was adult-like by 6 years. However, response consistency increased developmentally, suggesting reduced processing efficiency in childhood. At the group level, double-pass data were well simulated by a model of the auditory system assuming higher internal noise for children than adults. At the individual level, a sub-optimal decision strategy was added to better capture inter-individual variability. Thus, both systematic and stochastic inefficiencies may explain worse AM detection in childhood.

Evaluating the sampling effort for the metabarcoding-based detection of fish environmental DNA in the open ocean.

Clarifying the effect of the sampling protocol on the detection of environmental DNA (eDNA) is essential for appropriately designing biodiversity research. However, technical issues influencing eDNA detection in the open ocean, which consists of water masses with varying environmental conditions, have not been thoroughly investigated. This study evaluated the sampling effort for the metabarcoding-based detection of fish eDNA using replicate sampling with filters of different pore sizes (0.22 and 0.45 μm) in the subtropical and subarctic northwestern Pacific Ocean and Arctic Chukchi Sea. The asymptotic analysis predicted that the accumulation curves for detected taxa did not saturate in most cases, indicating that our sampling effort (7 or 8 replicates, corresponding to 10.5-40 L of filtration in total) was insufficient to fully assess the species diversity in the open ocean and that tens of replicates or a substantial filtration volume were required. The Jaccard dissimilarities between filtration replicates were comparable with those between the filter types at any site. In subtropical and subarctic sites, turnover dominated the dissimilarity, suggesting that the filter pore size had a negligible effect. In contrast, nestedness dominated the dissimilarity in the Chukchi Sea, implying that the 0.22 μm filter could collect a broader range of eDNA than the 0.45 μm filter. Therefore, the effect of filter selection on the collection of fish eDNA likely varies depending on the region. These findings highlight the highly stochastic nature of fish eDNA collection in the open ocean and the difficulty of standardizing the sampling protocol across various water masses.

A 24.25–30 GHz radio frequency up‐down converter with harmonic distortions rejection for 5G millimeter wave radio channel emulator applications

AbstractIn this paper, a 24.25–30 GHz fully integrated radio frequency (RF) up‐down converter with harmonic distortions rejection for 5G millimeter wave radio channel emulator applications is proposed and implemented in a 0.1‐μm GaAs pseudomorphic high‐electron‐mobility transistor technology. The RF up‐down converter integrates a frequency doubling amplifier chain local oscillator, a harmonic suppression mixer, and three frequency selective filters for unwanted harmonics suppression. To prevent the unwanted harmonics from leaking into the RF band, a higher sideband and high‐intermediate frequency mixer core with harmonic suppression filters is proposed. With this method, the unwanted harmonics are well suppressed. To effectively reduce the chip area, a miniaturized Marchand Balun based on a folded structure is proposed. The measured results demonstrate good in‐band spectral purity with more than 70 dB suppression of unwanted harmonics. Benefit from the high‐side injection mixing scheme and harmonics suppression method, the presented up‐down converter achieves a dynamic power range of −100 to −5 dBm with a conversion loss of 10 dB. The implemented RF up‐down converter occupies only 0.9 × 2.7 mm2 in size, with a power consumption of 400 mW under 4‐V supply.

Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining

In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this topic. Most of them frequently used feature-driven supervised approaches and hand-crafted linguistic patterns. However, the implicit and ambiguous statement of causation prevented these methods from achieving great recall and precision. They cover a limited set of implicit causality and are difficult to extend. In this work, a novel MCKN (multi-column knowledge-oriented network) is introduced. This model includes various knowledge-oriented channels/columns (KCs), where each channel integrates prior human knowledge to capture language cues of causation. MCKN uses unique convolutional word filters (wf) generated automatically using WordNet and FrameNet. To reduce MCKN’s dimensionality, we use filter selection and clustering approaches. Our model delivers superior performance on the Alternative Lexicalization (AltLexes) dataset, proving that MCKN is a simpler and distinctive approach for informal datasets.

Two-dimensional photonic crystals as selective filters for thermophotovoltaic applications.

The optical properties and performance of the two-dimensional photonic crystal (2D PhC) filters at normal incidence were simulated, and the best geometric parameters were obtained with the help of a global optimization program. The honeycomb structure has better performance, including high in-band transmittance, high out-band reflectance, and low parasitic absorption. The power density performance and conversion efficiency can reach 80.6% and 62.5%. Furthermore, the deeper cavity structure and multi-layer structure were designed to improve the performance of the filter. The deeper one can reduce the influence of transmission diffraction, increases the power density performance and conversion efficiency. The multi-layer structure reduces the parasitic absorption significantly and increases the conversion efficiency to 65.5%. These filters have both high efficiency and high power density, avoid the challenge of high-temperature stability faced by emitters, also easier and cheaper to fabricate compared to the 2D PhC emitters. These results suggest that the 2D PhC filters can be used in thermophotovoltaic systems for long-duration missions to improve the conversion efficiency.

Membrane phospholipids control gating of the mechanosensitive potassium leak channel TREK1

Tandem pore domain (K2P) potassium channels modulate resting membrane potentials and shape cellular excitability. For the mechanosensitive subfamily of K2Ps, the composition of phospholipids within the bilayer strongly influences channel activity. To examine the molecular details of K2P lipid modulation, we solved cryo-EM structures of the TREK1 K2P channel bound to either the anionic lipid phosphatidic acid (PA) or the zwitterionic lipid phosphatidylethanolamine (PE). At the extracellular face of TREK1, a PA lipid inserts its hydrocarbon tail into a pocket behind the selectivity filter, causing a structural rearrangement that recapitulates mutations and pharmacology known to activate TREK1. At the cytoplasmic face, PA and PE lipids compete to modulate the conformation of the TREK1 TM4 gating helix. Our findings demonstrate two distinct pathways by which anionic lipids enhance TREK1 activity and provide a framework for a model that integrates lipid gating with the effects of other mechanosensitive K2P modulators.