HERG1A potassium channel is the predominant isoform in head and neck squamous cell carcinomas: evidence for regulation by epigenetic mechanisms

PubMed Central

Menéndez, Sofía T.; Villaronga, M. Ángeles; Rodrigo, Juan P.; Álvarez-Teijeiro, Saúl; Urdinguio, Rocío G.; Fraga, Mario F.; Suárez, Carlos; García-Pedrero, Juana M.

2016-01-01

Evidences indicate that HERG1 voltage-gated potassium channel is frequently aberrantly expressed in various cancers including head and neck squamous cell carcinomas (HNSCC), representing a clinically and biologically relevant feature during disease progression and a potential therapeutic target. The present study further and significantly extends these data investigating for the first time the expression and individual contribution of HERG1 isoforms, their clinical significance during disease progression and also the underlying regulatory mechanisms. Analysis of HERG1A and HERG1B expression using real-time RT-PCR consistently showed that HERG1A is the predominant isoform in ten HNSCC-derived cell lines tested. HERG2 and HERG3 were also detected. Immunohistochemical analysis of HERG1A expression on 133 HNSCC specimens demonstrated that HERG1A expression increased during tumour progression and correlated significantly with reduced disease-specific survival. Furthermore, our study provides original evidence supporting the involvement of histone acetylation (i.e. H3Ac and H4K16Ac activating marks) in the regulation of HERG1 expression in HNSCC. Interestingly, this mechanism was also found to regulate the expression of another oncogenic channel (Kv3.4) as well as HERG2 and HERG3. These data demonstrate that HERG1A is the predominant and disease-relevant isoform in HNSCC progression, while histone acetylation emerges as an important regulatory mechanism underlying Kv gene expression. PMID:26785772

HERG1A potassium channel is the predominant isoform in head and neck squamous cell carcinomas: evidence for regulation by epigenetic mechanisms.

PubMed

Menéndez, Sofía T; Villaronga, M Ángeles; Rodrigo, Juan P; Álvarez-Teijeiro, Saúl; Urdinguio, Rocío G; Fraga, Mario F; Suárez, Carlos; García-Pedrero, Juana M

2016-01-20

Evidences indicate that HERG1 voltage-gated potassium channel is frequently aberrantly expressed in various cancers including head and neck squamous cell carcinomas (HNSCC), representing a clinically and biologically relevant feature during disease progression and a potential therapeutic target. The present study further and significantly extends these data investigating for the first time the expression and individual contribution of HERG1 isoforms, their clinical significance during disease progression and also the underlying regulatory mechanisms. Analysis of HERG1A and HERG1B expression using real-time RT-PCR consistently showed that HERG1A is the predominant isoform in ten HNSCC-derived cell lines tested. HERG2 and HERG3 were also detected. Immunohistochemical analysis of HERG1A expression on 133 HNSCC specimens demonstrated that HERG1A expression increased during tumour progression and correlated significantly with reduced disease-specific survival. Furthermore, our study provides original evidence supporting the involvement of histone acetylation (i.e. H3Ac and H4K16Ac activating marks) in the regulation of HERG1 expression in HNSCC. Interestingly, this mechanism was also found to regulate the expression of another oncogenic channel (Kv3.4) as well as HERG2 and HERG3. These data demonstrate that HERG1A is the predominant and disease-relevant isoform in HNSCC progression, while histone acetylation emerges as an important regulatory mechanism underlying Kv gene expression.

Distinct gene-specific mechanisms of arrhythmia revealed by cardiac gene transfer of two long QT disease genes, HERG and KCNE1.

PubMed

Hoppe, U C; Marbán, E; Johns, D C

2001-04-24

The long QT syndrome (LQTS) is a heritable disorder that predisposes to sudden cardiac death. LQTS is caused by mutations in ion channel genes including HERG and KCNE1, but the precise mechanisms remain unclear. To clarify this situation we injected adenoviral vectors expressing wild-type or LQT mutants of HERG and KCNE1 into guinea pig myocardium. End points at 48-72 h included electrophysiology in isolated myocytes and electrocardiography in vivo. HERG increased the rapid component, I(Kr), of the delayed rectifier current, thereby accelerating repolarization, increasing refractoriness, and diminishing beat-to-beat action potential variability. Conversely, HERG-G628S suppressed I(Kr) without significantly delaying repolarization. Nevertheless, HERG-G628S abbreviated refractoriness and increased beat-to-beat variability, leading to early afterdepolarizations (EADs). KCNE1 increased the slow component of the delayed rectifier, I(Ks), without clear phenotypic sequelae. In contrast, KCNE1-D76N suppressed I(Ks) and markedly slowed repolarization, leading to frequent EADs and electrocardiographic QT prolongation. Thus, the two genes predispose to sudden death by distinct mechanisms: the KCNE1 mutant flagrantly undermines cardiac repolarization, and HERG-G628S subtly facilitates the genesis and propagation of premature beats. Our ability to produce electrocardiographic long QT in vivo with a clinical KCNE1 mutation demonstrates the utility of somatic gene transfer in creating genotype-specific disease models.

Computational Modeling Reveals Key Contributions of KCNQ and hERG Currents to the Malleability of Uterine Action Potentials Underpinning Labor

PubMed Central

Tong, Wing-Chiu; Tribe, Rachel M.; Smith, Roger; Taggart, Michael J.

2014-01-01

The electrical excitability of uterine smooth muscle cells is a key determinant of the contraction of the organ during labor and is manifested by spontaneous, periodic action potentials (APs). Near the end of term, APs vary in shape and size reflecting an ability to change the frequency, duration and amplitude of uterine contractions. A recent mathematical model quantified several ionic features of the electrical excitability in uterine smooth muscle cells. It replicated many of the experimentally recorded uterine AP configurations but its limitations were evident when trying to simulate the long-duration bursting APs characteristic of labor. A computational parameter search suggested that delayed rectifying K+ currents could be a key model component requiring improvement to produce the longer-lasting bursting APs. Of the delayed rectifying K+ currents family it is of interest that KCNQ and hERG channels have been reported to be gestationally regulated in the uterus. These currents exhibit features similar to the broadly defined uterine I K1 of the original mathematical model. We thus formulated new quantitative descriptions for several I KCNQ and I hERG. Incorporation of these currents into the uterine cell model enabled simulations of the long-lasting bursting APs. Moreover, we used this modified model to simulate the effects of different contributions of I KCNQ and I hERG on AP form. Our findings suggest that the alterations in expression of hERG and KCNQ channels can potentially provide a mechanism for fine tuning of AP forms that lends a malleability for changing between plateau-like and long-lasting bursting-type APs as uterine cells prepare for parturition. PMID:25474527

Genetic Screening in C. Elegans Identifies Rho-GTPAse Activating Protein 6 as Novel HERG Regulator

PubMed Central

Potet, Franck; Petersen, Christina I.; Boutaud, Olivier; Shuai, Wen; Stepanovic, Svetlana Z.; Balser, Jeffrey R.; Kupershmidt, Sabina

2009-01-01

The human ether-a-go-go related gene (HERG) constitutes the pore forming subunit of IKr, a K+ current involved in repolarization of the cardiac action potential. While mutations in HERG predispose patients to cardiac arrhythmias (Long QT syndrome; LQTS), altered function of HERG regulators are undoubtedly LQTS risk factors. We have combined RNA interference with behavioral screening in Caenorhabditis elegans to detect genes that influence function of the HERG homolog, UNC-103. One such gene encodes the worm ortholog of the rho-GTPase activating protein 6 (ARHGAP6). In addition to its GAP function, ARHGAP6 induces cytoskeletal rearrangements and activates phospholipase C (PLC). Here we show that IKr recorded in cells co-expressing HERG and ARHGAP6 was decreased by 43% compared to HERG alone. Biochemical measurements of cell-surface associated HERG revealed that ARHGAP6 reduced membrane expression of HERG by 35%, which correlates well with the reduction in current. In an atrial myocyte cell line, suppression of endogenous ARHGAP6 by virally transduced shRNA led to a 53 % enhancement of IKr. ARHGAP6 effects were maintained when we introduced a dominant negative rho-GTPase, or ARHGAP6 devoid of rhoGAP function, indicating ARHGAP6 regulation of HERG is independent of rho activation. However, ARHGAP6 lost effectiveness when PLC was inhibited. We further determined that ARHGAP6 effects are mediated by a consensus SH3 binding domain within the C-terminus of HERG, although stable ARHGAP6-HERG complexes were not observed. These data link a rhoGAP-activated PLC pathway to HERG membrane expression and implicate this family of proteins as candidate genes in disorders involving HERG. PMID:19038263

Correlation between human ether‐a‐go‐go‐related gene channel inhibition and action potential prolongation

PubMed Central

Saxena, P; Hortigon‐Vinagre, M P; Beyl, S; Baburin, I; Andranovits, S; Iqbal, S M; Costa, A; IJzerman, A P; Kügler, P; Timin, E

2017-01-01

Background and Purpose Human ether‐a‐go‐go‐related gene (hERG; Kv11.1) channel inhibition is a widely accepted predictor of cardiac arrhythmia. hERG channel inhibition alone is often insufficient to predict pro‐arrhythmic drug effects. This study used a library of dofetilide derivatives to investigate the relationship between standard measures of hERG current block in an expression system and changes in action potential duration (APD) in human‐induced pluripotent stem cell‐derived cardiomyocytes (hiPSC‐CMs). The interference from accompanying block of Cav1.2 and Nav1.5 channels was investigated along with an in silico AP model. Experimental Approach Drug‐induced changes in APD were assessed in hiPSC‐CMs using voltage‐sensitive dyes. The IC50 values for dofetilide and 13 derivatives on hERG current were estimated in an HEK293 expression system. The relative potency of each drug on APD was estimated by calculating the dose (D150) required to prolong the APD at 90% (APD90) repolarization by 50%. Key Results The D150 in hiPSC‐CMs was linearly correlated with IC50 of hERG current. In silico simulations supported this finding. Three derivatives inhibited hERG without prolonging APD, and these compounds also inhibited Cav1.2 and/or Nav1.5 in a channel state‐dependent manner. Adding Cav1.2 and Nav1.2 block to the in silico model recapitulated the direction but not the extent of the APD change. Conclusions and Implications Potency of hERG current inhibition correlates linearly with an index of APD in hiPSC‐CMs. The compounds that do not correlate have additional effects including concomitant block of Cav1.2 and/or Nav1.5 channels. In silico simulations of hiPSC‐CMs APs confirm the principle of the multiple ion channel effects. PMID:28681507

Rab11-dependent Recycling of the Human Ether-a-go-go-related Gene (hERG) Channel*

PubMed Central

Chen, Jeffery; Guo, Jun; Yang, Tonghua; Li, Wentao; Lamothe, Shawn M.; Kang, Yudi; Szendrey, John A.; Zhang, Shetuan

2015-01-01

The human ether-a-go-go-related gene (hERG) encodes the pore-forming subunit of the rapidly activating delayed rectifier potassium channel (IKr). A reduction in the hERG current causes long QT syndrome, which predisposes affected individuals to ventricular arrhythmias and sudden death. We reported previously that hERG channels in the plasma membrane undergo vigorous internalization under low K+ conditions. In the present study, we addressed whether hERG internalization occurs under normal K+ conditions and whether/how internalized channels are recycled back to the plasma membrane. Using patch clamp, Western blot, and confocal imaging analyses, we demonstrated that internalized hERG channels can effectively recycle back to the plasma membrane. Low K+-enhanced hERG internalization is accompanied by an increased rate of hERG recovery in the plasma membrane upon reculture following proteinase K-mediated clearance of cell-surface proteins. The increased recovery rate is not due to enhanced protein synthesis, as hERG mRNA expression was not altered by low K+ exposure, and the increased recovery was observed in the presence of the protein biosynthesis inhibitor cycloheximide. GTPase Rab11, but not Rab4, is involved in the recycling of hERG channels. Interfering with Rab11 function not only delayed hERG recovery in cells after exposure to low K+ medium but also decreased hERG expression and function in cells under normal culture conditions. We concluded that the recycling pathway plays an important role in the homeostasis of plasma membrane-bound hERG channels. PMID:26152716

Mechanism of hERG channel block by the psychoactive indole alkaloid ibogaine.

PubMed

Thurner, Patrick; Stary-Weinzinger, Anna; Gafar, Hend; Gawali, Vaibhavkumar S; Kudlacek, Oliver; Zezula, Juergen; Hilber, Karlheinz; Boehm, Stefan; Sandtner, Walter; Koenig, Xaver

2014-02-01

Ibogaine is a psychoactive indole alkaloid. Its use as an antiaddictive agent has been accompanied by QT prolongation and cardiac arrhythmias, which are most likely caused by human ether a go-go-related gene (hERG) potassium channel inhibition. Therefore, we studied in detail the interaction of ibogaine with hERG channels heterologously expressed in mammalian kidney tsA-201 cells. Currents through hERG channels were blocked regardless of whether ibogaine was applied via the extracellular or intracellular solution. The extent of inhibition was determined by the relative pH values. Block occurred during activation of the channels and was not observed for resting channels. With increasing depolarizations, ibogaine block grew and developed faster. Steady-state activation and inactivation of the channel were shifted to more negative potentials. Deactivation was slowed, whereas inactivation was accelerated. Mutations in the binding site reported for other hERG channel blockers (Y652A and F656A) reduced the potency of ibogaine, whereas an inactivation-deficient double mutant (G628C/S631C) was as sensitive as wild-type channels. Molecular drug docking indicated binding within the inner cavity of the channel independently of the protonation of ibogaine. Experimental current traces were fit to a kinetic model of hERG channel gating, revealing preferential binding of ibogaine to the open and inactivated state. Taken together, these findings show that ibogaine blocks hERG channels from the cytosolic side either in its charged form alone or in company with its uncharged form and alters the currents by changing the relative contribution of channel states over time.

The human ether-a-go-go-related gene (hERG) current inhibition selectively prolongs action potential of midmyocardial cells to augment transmural dispersion.

PubMed

Yasuda, C; Yasuda, S; Yamashita, H; Okada, J; Hisada, T; Sugiura, S

2015-08-01

The majority of drug induced arrhythmias are related to the prolongation of action potential duration following inhibition of rapidly activating delayed rectifier potassium current (I(Kr)) mediated by the hERG channel. However, for arrhythmias to develop and be sustained, not only the prolongation of action potential duration but also its transmural dispersion are required. Herein, we evaluated the effect of hERG inhibition on transmural dispersion of action potential duration using the action potential clamp technique that combined an in silico myocyte model with the actual I(Kr) measurement. Whole cell I(Kr) current was measured in Chinese hamster ovary cells stably expressing the hERG channel. The measured current was coupled with models of ventricular endocardial, M-, and epicardial cells to calculate the action potentials. Action potentials were evaluated under control condition and in the presence of 1, 10, or 100 μM disopyramide, an hERG inhibitor. Disopyramide dose-dependently increased the action potential durations of the three cell types. However, action potential duration of M-cells increased disproportionately at higher doses, and was significantly different from that of epicardial and endocardial cells (dispersion of repolarization). By contrast, the effects of disopyramide on peak I(Kr) and instantaneous current-voltage relation were similar in all cell types. Simulation study suggested that the reduced repolarization reserve of M-cell with smaller amount of slowly activating delayed rectifier potassium current levels off at longer action potential duration to make such differences. The action potential clamp technique is useful for studying the mechanism of arrhythmogenesis by hERG inhibition through the transmural dispersion of repolarization.

Ion Conduction through the hERG Potassium Channel

PubMed Central

Cavalli, Andrea; Recanatini, Maurizio

2012-01-01

The inward rectifier voltage-gated potassium channel hERG is of primary importance for the regulation of the membrane potential of cardiomyocytes. Unlike most voltage-gated K+-channels, hERG shows a low elementary conductance at physiological voltage and potassium concentration. To investigate the molecular features underlying this unusual behavior, we simulated the ion conduction through the selectivity filter at a fully atomistic level by means of molecular dynamics-based methods, using a homology-derived model. According to our calculations, permeation of potassium ions can occur along two pathways, one involving site vacancies inside the filter (showing an energy barrier of about 6 kcal mol−1), and the other characterized by the presence of a knock-on intermediate (about 8 kcal mol−1). These barriers are indeed in accordance with a low conductance behavior, and can be explained in terms of a series of distinctive structural features displayed by the hERG ion permeation pathway. PMID:23133669

Mutant MiRP1 subunits modulate HERG K+ channel gating: a mechanism for pro-arrhythmia in long QT syndrome type 6

PubMed Central

Lu, Yu; Mahaut-Smith, Martyn P; Huang, Christopher L-H; Vandenberg, Jamie I

2003-01-01

Mutations in KCNE2, which encodes the minK-related protein 1 (MiRP1), are associated with an increased risk of arrhythmias; however, the underlying mechanisms are unknown. MiRP1 is thought to associate with many K+ channel α-subunits, including HERG K+ channels, which have a major role in suppressing arrhythmias initiated by premature beats. In this study we have investigated in chinese hamster ovary (CHO) cells at 37 °C the effects of co-expressing HERG K+ channels with either wild-type (WT) MiRP1 or one of three mutant MiRP1 subunits, T8A, Q9E and M54T. The most significant effects of MiRP1 subunits on HERG channels were a more negative steady-state activation for HERG + T8A MiRP1 and a more positive steady-state activation for HERG + M54T MiRP1 compared to either HERG + WT MiRP1 or HERG alone. All three mutants caused a significant slowing of deactivation at depolarised potentials. T8A MiRP1 also caused an acceleration of inactivation and recovery from inactivation compared to HERG + WT MiRP1. During ventricular action potential clamp experiments there was a significant decrease in current in the early phases of the action potential for HERG + WT MiRP1 channels compared to HERG alone. This effect was not as prominent for the mutant MiRP1 subunits. During premature action potential clamp protocols, the T8A and Q9E mutants, but not the M54T mutant, resulted in significantly larger current spikes during closely coupled premature beats, compared to HERG + WT MiRP1. At longer coupling intervals, all three mutants resulted in larger current spikes than HERG alone or HERG + WT MiRP1 channels. It is therefore possible that augmentation of HERG currents in the early diastolic period may be pro-arrhythmic. PMID:12923204

Calpain activation by ROS mediates human ether-a-go-go-related gene protein degradation by intermittent hypoxia.

PubMed

Wang, N; Kang, H S; Ahmmed, G; Khan, S A; Makarenko, V V; Prabhakar, N R; Nanduri, J

2016-03-01

Human ether-a-go-go-related gene (hERG) channels conduct delayed rectifier K(+) current. However, little information is available on physiological situations affecting hERG channel protein and function. In the present study we examined the effects of intermittent hypoxia (IH), which is a hallmark manifestation of sleep apnea, on hERG channel protein and function. Experiments were performed on SH-SY5Y neuroblastoma cells, which express hERG protein. Cells were exposed to IH consisting of alternating cycles of 30 s of hypoxia (1.5% O2) and 5 min of 20% O2. IH decreased hERG protein expression in a stimulus-dependent manner. A similar reduction in hERG protein was also seen in adrenal medullary chromaffin cells from IH-exposed neonatal rats. The decreased hERG protein was associated with attenuated hERG K(+) current. IH-evoked hERG protein degradation was not due to reduced transcription or increased proteosome/lysomal degradation. Rather it was mediated by calcium-activated calpain proteases. Both COOH- and NH2-terminal sequences of the hERG protein were the targets of calpain-dependent degradation. IH increased reactive oxygen species (ROS) levels, intracellular Ca(2+) concentration ([Ca(2+)]i), calpain enzyme activity, and hERG protein degradation, and all these effects were prevented by manganese-(111)-tetrakis-(1-methyl-4-pyridyl)-porphyrin pentachloride, a membrane-permeable ROS scavenger. These results demonstrate that activation of calpains by ROS-dependent elevation of [Ca(2+)]i mediates hERG protein degradation by IH. Copyright © 2016 the American Physiological Society.

Role of the pH in state-dependent blockade of hERG currents

NASA Astrophysics Data System (ADS)

Wang, Yibo; Guo, Jiqing; Perissinotti, Laura L.; Lees-Miller, James; Teng, Guoqi; Durdagi, Serdar; Duff, Henry J.; Noskov, Sergei Yu.

2016-10-01

Mutations that reduce inactivation of the voltage-gated Kv11.1 potassium channel (hERG) reduce binding for a number of blockers. State specific block of the inactivated state of hERG block may increase risks of drug-induced Torsade de pointes. In this study, molecular simulations of dofetilide binding to the previously developed and experimentally validated models of the hERG channel in open and open-inactivated states were combined with voltage-clamp experiments to unravel the mechanism(s) of state-dependent blockade. The computations of the free energy profiles associated with the drug block to its binding pocket in the intra-cavitary site display startling differences in the open and open-inactivated states of the channel. It was also found that drug ionization may play a crucial role in preferential targeting to the open-inactivated state of the pore domain. pH-dependent hERG blockade by dofetilie was studied with patch-clamp recordings. The results show that low pH increases the extent and speed of drug-induced block. Both experimental and computational findings indicate that binding to the open-inactivated state is of key importance to our understanding of the dofetilide’s mode of action.

Voltage-Dependent Gating of hERG Potassium Channels

PubMed Central

Cheng, Yen May; Claydon, Tom W.

2012-01-01

The mechanisms by which voltage-gated channels sense changes in membrane voltage and energetically couple this with opening of the ion conducting pore has been the source of significant interest. In voltage-gated potassium (Kv) channels, much of our knowledge in this area comes from Shaker-type channels, for which voltage-dependent gating is quite rapid. In these channels, activation and deactivation are associated with rapid reconfiguration of the voltage-sensing domain unit that is electromechanically coupled, via the S4–S5 linker helix, to the rate-limiting opening of an intracellular pore gate. However, fast voltage-dependent gating kinetics are not typical of all Kv channels, such as Kv11.1 (human ether-à-go-go related gene, hERG), which activates and deactivates very slowly. Compared to Shaker channels, our understanding of the mechanisms underlying slow hERG gating is much poorer. Here, we present a comparative review of the structure–function relationships underlying activation and deactivation gating in Shaker and hERG channels, with a focus on the roles of the voltage-sensing domain and the S4–S5 linker that couples voltage sensor movements to the pore. Measurements of gating current kinetics and fluorimetric analysis of voltage sensor movement are consistent with models suggesting that the hERG activation pathway contains a voltage independent step, which limits voltage sensor transitions. Constraints upon hERG voltage sensor movement may result from loose packing of the S4 helices and additional intra-voltage sensor counter-charge interactions. More recent data suggest that key amino acid differences in the hERG voltage-sensing unit and S4–S5 linker, relative to fast activating Shaker-type Kv channels, may also contribute to the increased stability of the resting state of the voltage sensor. PMID:22586397

hERG Blockade by Iboga Alkaloids.

PubMed

Alper, Kenneth; Bai, Rong; Liu, Nian; Fowler, Steven J; Huang, Xi-Ping; Priori, Silvia G; Ruan, Yanfei

2016-01-01

The iboga alkaloids are a class of naturally occurring and synthetic compounds, some of which modify drug self-administration and withdrawal in humans and preclinical models. Ibogaine, the prototypic iboga alkaloid that is utilized clinically to treat addictions, has been associated with QT prolongation, torsades de pointes and fatalities. hERG blockade as IKr was measured using the whole-cell patch clamp technique in HEK 293 cells. This yielded the following IC50 values: ibogaine manufactured by semisynthesis via voacangine (4.09 ± 0.69 µM) or by extraction from T. iboga (3.53 ± 0.16 µM); ibogaine's principal metabolite noribogaine (2.86 ± 0.68 µM); and voacangine (2.25 ± 0.34 µM). In contrast, the IC50 of 18-methoxycoronaridine, a product of rational synthesis and current focus of drug development was >50 µM. hERG blockade was voltage dependent for all of the compounds, consistent with low-affinity blockade. hERG channel binding affinities (K i) for the entire set of compounds, including 18-MC, ranged from 0.71 to 3.89 µM, suggesting that 18-MC binds to the hERG channel with affinity similar to the other compounds, but the interaction produces substantially less hERG blockade. In view of the extended half-life of noribogaine, these results may relate to observations of persistent QT prolongation and cardiac arrhythmia at delayed intervals of days following ibogaine ingestion. The apparent structure-activity relationships regarding positions of substitutions on the ibogamine skeleton suggest that the iboga alkaloids might provide an informative paradigm for investigation of the structural biology of the hERG channel.

The Role of Monoubiquitination in Endocytic Degradation of Human Ether-a-go-go-related Gene (hERG) Channels under Low K+ Conditions*

PubMed Central

Sun, Tao; Guo, Jun; Shallow, Heidi; Yang, Tonghua; Xu, Jianmin; Li, Wentao; Hanson, Christian; Wu, James G.; Li, Xian; Massaeli, Hamid; Zhang, Shetuan

2011-01-01

A reduction in extracellular K+ concentration ([K+]o) causes cardiac arrhythmias and triggers internalization of the cardiac rapidly activating delayed rectifier potassium channel (IKr) encoded by the human ether-a-go-go-related gene (hERG). We investigated the role of ubiquitin (Ub) in endocytic degradation of hERG channels stably expressed in HEK cells. Under low K+ conditions, UbKO, a lysine-less mutant Ub that only supports monoubiquitination, preferentially interacted and selectively enhanced degradation of the mature hERG channels. Overexpression of Vps24 protein, also known as charged multivesicular body protein 3, significantly accelerated degradation of mature hERG channels, whereas knockdown of Vps24 impeded this process. Moreover, the lysosomal inhibitor bafilomycin A1 inhibited degradation of the internalized mature hERG channels. Thus, monoubiquitination directs mature hERG channels to degrade through the multivesicular body/lysosome pathway. Interestingly, the protease inhibitor lactacystin inhibited the low K+-induced hERG endocytosis and concomitantly led to an accumulation of monoubiquitinated mature hERG channels, suggesting that deubiquitination is also required for the endocytic degradation. Consistently, overexpression of the endosomal deubiquitinating enzyme signal transducing adaptor molecule-binding protein significantly accelerated whereas knockdown of endogenous signal transducing adaptor molecule-binding protein impeded degradation of the mature hERG channels under low K+ conditions. Thus, monoubiquitin dynamically mediates endocytic degradation of mature hERG channels under low K+ conditions. PMID:21177251

Identification of quaternary ammonium compounds as potent inhibitors of hERG potassium channels

PubMed Central

Xia, Menghang; Shahane, Sampada; Huang, Ruili; Titus, Steven A.; Shum, Enoch; Zhao, Yong; Southall, Noel; Zheng, Wei; Witt, Kristine L.; Tice, Raymond R.; Austin, Christopher P.

2011-01-01

The human ether-a-go-go-related gene (hERG) channel, a member of a family of voltage-gated potassium (K+) channels, plays a critical role in the repolarization of the cardiac action potential. The reduction of hERG channel activity as a result of adverse drug effects or genetic mutations may cause QT interval prolongation and potentially lead to acquired long QT syndrome. Thus, screening for hERG channel activity is important in drug development. Cardiotoxicity associated with the inhibition of hERG channels by environmental chemicals is also a public health concern. To assess the inhibitory effects of environmental chemicals on hERG channel function, we screened the National Toxicology Program (NTP) collection of 1408 compounds by measuring thallium influx into cells through hERG channels. Seventeen compounds with hERG channel inhibition were identified with IC50 potencies ranging from 0.26 to 22 μM. Twelve of these compounds were confirmed as hERG channel blockers in an automated whole cell patch clamp experiment. In addition, we investigated the structure-activity relationship of seven compounds belonging to the quaternary ammonium compound (QAC) series on hERG channel inhibition. Among four active QAC compounds, tetra-n-octylammonium bromide was the most potent with an IC50 value of 260 nM in the thallium influx assay and 80 nM in the patch clamp assay. The potency of this class of hERG channel inhibitors appears to depend on the number and length of their aliphatic side-chains surrounding the charged nitrogen. Profiling environmental compound libraries for hERG channel inhibition provides information useful in prioritizing these compounds for cardiotoxicity assessment in vivo. PMID:21362439

Interaction between the cardiac rapidly (IKr) and slowly (IKs) activating delayed rectifier potassium channels revealed by low K+-induced hERG endocytic degradation.

PubMed

Guo, Jun; Wang, Tingzhong; Yang, Tonghua; Xu, Jianmin; Li, Wentao; Fridman, Michael D; Fisher, John T; Zhang, Shetuan

2011-10-07

Cardiac repolarization is controlled by the rapidly (I(Kr)) and slowly (I(Ks)) activating delayed rectifier potassium channels. The human ether-a-go-go-related gene (hERG) encodes I(Kr), whereas KCNQ1 and KCNE1 together encode I(Ks). Decreases in I(Kr) or I(Ks) cause long QT syndrome (LQTS), a cardiac disorder with a high risk of sudden death. A reduction in extracellular K(+) concentration ([K(+)](o)) induces LQTS and selectively causes endocytic degradation of mature hERG channels from the plasma membrane. In the present study, we investigated whether I(Ks) compensates for the reduced I(Kr) under low K(+) conditions. Our data show that when hERG and KCNQ1 were expressed separately in human embryonic kidney (HEK) cells, exposure to 0 mM K(+) for 6 h completely eliminated the mature hERG channel expression but had no effect on KCNQ1. When hERG and KCNQ1 were co-expressed, KCNQ1 significantly delayed 0 mM K(+)-induced hERG reduction. Also, hERG degradation led to a significant reduction in KCNQ1 in 0 mM K(+) conditions. An interaction between hERG and KCNQ1 was identified in hERG+KCNQ1-expressing HEK cells. Furthermore, KCNQ1 preferentially co-immunoprecipitated with mature hERG channels that are localized in the plasma membrane. Biophysical and pharmacological analyses indicate that although hERG and KCNQ1 closely interact with each other, they form distinct hERG and KCNQ1 channels. These data extend our understanding of delayed rectifier potassium channel trafficking and regulation, as well as the pathology of LQTS.

hERG trafficking inhibition in drug-induced lethal cardiac arrhythmia.

PubMed

Nogawa, Hisashi; Kawai, Tomoyuki

2014-10-15

Acquired long QT syndrome induced by non-cardiovascular drugs can cause lethal cardiac arrhythmia called torsades de points and is a significant problem in drug development. The prolongation of QT interval and cardiac action potential duration are mainly due to reduced physiological function of the rapidly activating voltage-dependent potassium channels encoded by human ether-a-go-go-related gene (hERG). Structurally diverse groups of drugs are known to directly inhibit hERG channel conductance. Therefore, the ability of acute hERG inhibition is routinely assessed at the preclinical stages in pharmaceutical testing. Recent findings indicated that chronic treatment with various drugs not only inhibits hERG channels but also decreases hERG channel expression in the plasma membrane of cardiomyocytes, which has become another concern in safety pharmacology. The mechanisms involve the disruption of hERG trafficking to the surface membrane or the acceleration of hERG protein degradation. From this perspective, we present a brief overview of mechanisms of drug-induced trafficking inhibition and pathological regulation. Understanding of drug-induced hERG trafficking inhibition may provide new strategies for predicting drug-induced QT prolongation and lethal cardiac arrhythmia in pharmaceutical drug development. Copyright © 2014 Elsevier B.V. All rights reserved.

Mechanism and pharmacological rescue of berberine-induced hERG channel deficiency

PubMed Central

Yan, Meng; Zhang, Kaiping; Shi, Yanhui; Feng, Lifang; Lv, Lin; Li, Baoxin

2015-01-01

Berberine (BBR), an isoquinoline alkaloid mainly isolated from plants of Berberidaceae family, is extensively used to treat gastrointestinal infections in clinics. It has been reported that BBR can block human ether-a-go-go-related gene (hERG) potassium channel and inhibit its membrane expression. The hERG channel plays crucial role in cardiac repolarization and is the target of diverse proarrhythmic drugs. Dysfunction of hERG channel can cause long QT syndrome. However, the regulatory mechanisms of BBR effects on hERG at cell membrane level remain unknown. This study was designed to investigate in detail how BBR decreased hERG expression on cell surface and further explore its pharmacological rescue strategies. In this study, BBR decreases caveolin-1 expression in a concentration-dependent manner in human embryonic kidney 293 (HEK293) cells stably expressing hERG channel. Knocking down the basal expression of caveolin-1 alleviates BBR-induced hERG reduction. In addition, we found that aromatic tyrosine (Tyr652) and phenylalanine (Phe656) in S6 domain mediate the long-term effect of BBR on hERG by using mutation techniques. Considering both our previous and present work, we propose that BBR reduces hERG membrane stability with multiple mechanisms. Furthermore, we found that fexofenadine and resveratrol shorten action potential duration prolongated by BBR, thus having the potential effects of alleviating the cardiotoxicity of BBR. PMID:26543354

In vitro chronic effects on hERG channel caused by the marine biotoxin Azaspiracid-2

PubMed Central

Ferreiro, Sara F.; Vilariño, Natalia; Louzao, M.Carmen; Nicolaou, K. C.; Frederick, Michael O.; Botana, Luis M.

2014-01-01

Azaspiracids (AZAs) are marine biotoxins produced by the dinoflagellate Azadinium spinosum that accumulate in many shellfish species. Azaspiracid poisoning caused by AZA-contaminated seafood consumption is primarily manifested by diarrhea in humans. To protect human health, AZA-1, AZA-2 and AZA-3 content in seafood has been regulated by food safety authorities in many countries. Recently AZAs have been reported as a low/moderate hERG channel blockers. Furthermore AZA-2 has been related to arrhythmia appearance in rats, suggesting potential heart toxicity. In this study AZA-2 in vitro effects on hERG channel after chronic exposure are analyzed to further explore potential cardiotoxicity. The amount of hERG channel in the plasma membrane, hERG channel trafficking and hERG currents were evaluated up to 12 h of toxin exposure. In these conditions AZA-2 caused an increase of hERG levels in the plasma membrane, probably related to hERG retrograde trafficking impairment. Although this alteration did not translate into an increase of hERG channel-related current, more studies will be necessary to understand its mechanism and to know what consequences could have in vivo. These findings suggest that azaspiracids might have chronic cardiotoxicity related to hERG channel trafficking and they should not be overlooked when evaluating the threat to human health. PMID:25286396

Role of the activation gate in determining the extracellular potassium dependency of block of HERG by trapped drugs.

PubMed

Pareja, Kristeen; Chu, Elaine; Dodyk, Katrina; Richter, Kristofer; Miller, Alan

2013-01-01

Drug induced long QT syndrome (diLQTS) results primarily from block of the cardiac potassium channel HERG (human-ether-a-go-go related gene). In some cases long QT syndrome can result in the lethal arrhythmia torsade de pointes, an arrhythmia characterized by a rapid heart rate and severely compromised cardiac output. Many patients requiring medication present with serum potassium abnormalities due to a variety of conditions including gastrointestinal dysfunction, renal and endocrine disorders, diuretic use, and aging. Extracellular potassium influences HERG channel inactivation and can alter block of HERG by some drugs. However, block of HERG by a number of drugs is not sensitive to extracellular potassium. In this study, we show that block of WT HERG by bepridil and terfenadine, two drugs previously shown to be trapped inside the HERG channel after the channel closes, is insensitive to extracellular potassium over the range of 0 mM to 20 mM. We also show that bepridil block of the HERG mutant D540K, a mutant channel that is unable to trap drugs, is dependent on extracellular potassium, correlates with the permeant ion, and is independent of HERG inactivation. These results suggest that the lack of extracellular potassium dependency of block of HERG by some drugs may in part be related to the ability of these drugs to be trapped inside the channel after the channel closes.

Cell Surface Expression of Human Ether-a-go-go-related Gene (hERG) Channels Is Regulated by Caveolin-3 Protein via the Ubiquitin Ligase Nedd4-2*

PubMed Central

Guo, Jun; Wang, Tingzhong; Li, Xian; Shallow, Heidi; Yang, Tonghua; Li, Wentao; Xu, Jianmin; Fridman, Michael D.; Yang, Xiaolong; Zhang, Shetuan

2012-01-01

The human ether-a-go-go-related gene (hERG) encodes the rapidly activating delayed rectifier potassium channel (IKr) which plays an important role in cardiac repolarization. A reduction or increase in hERG current can cause long or short QT syndrome, respectively, leading to fatal cardiac arrhythmias. The channel density in the plasma membrane is a key determinant of the whole cell current amplitude. To gain insight into the molecular mechanisms for the regulation of hERG density at the plasma membrane, we used whole cell voltage clamp, Western blotting, and immunocytochemical methods to investigate the effects of an integral membrane protein, caveolin-3 (Cav3) on hERG expression levels. Our data demonstrate that Cav3, hERG, and ubiquitin-ligase Nedd4-2 interact with each other and form a complex. Expression of Cav3 thus enhances the hERG-Nedd4-2 interaction, leading to an increased ubiquitination and degradation of mature, plasma-membrane localized hERG channels. Disrupting Nedd4-2 interaction with hERG by mutations eliminates the effects of Cav3 on hERG channels. Knockdown of endogenous Cav3 or Nedd4-2 in cultured neonatal rat ventricular myocytes using siRNA led to an increase in native IKr. Our data demonstrate that hERG expression in the plasma membrane is regulated by Cav3 via Nedd4-2. These findings extend our understanding of the regulation of hERG channels and cardiac electrophysiology. PMID:22879586

Increased expression of HERG K+ channels contributes to myelodysplastic syndrome progression and displays correlation with prognosis stratification.

PubMed

Lu, Li; Du, Wen; Liu, Wei; Guo, Dongmei; He, Xiaoqi; Li, Huiyu

2016-12-01

Human ether-a-go-go-related gene (HERG) K + channels are shown to be aberrantly expressed in a variety of cancer cells where they play roles in contributing to cancer progression. Myelodysplastic syndromes (MDS) are a group of clinical heterogeneous disorders characterized by bone marrow failure and dysplasia of blood cells. However, the involvement of HERG K + channels in MDS development is poorly understood. The expression of HERG K + channels in untreated MDS, acute myeloid leukemia (AML) patients and the control group was detected by flow cytometry. The roles of HERG K + channels in regulation of SKM-1 cell proliferation, apoptosis, and cell cycle were determined by CCK-8 assay and flow cytometry, respectively. We found that expression of HERG K + channels in MDS patients was significantly higher than controls and was lower than AML. Percentage of HERG K + channels on CD34+CD38- cells gradually increased from controls to high-grade MDS subtypes. And HERG K + channel levels showed an ascending tendency from low-risk to high-risk MDS group. In addition, the CCK-8 assay, apoptosis and cell cycle analysis were performed and showed that blockage of HERG K + channels decreased the proliferation of MDS cells but rarely had effects on cell apoptosis and cell cycle distribution. Our study demonstrated that HERG K + channels might be a potential tumor marker of MDS. These channels were likely to contribute to MDS progression and were helpful for predicting prognosis of MDS. Inhibition of HERG K + channels might be a novel therapeutic measure for MDS.

A pharmacokinetic-pharmacodynamic model for the quantitative prediction of dofetilide clinical QT prolongation from human ether-a-go-go-related gene current inhibition data.

PubMed

Jonker, Daniël M; Kenna, Leslie A; Leishman, Derek; Wallis, Rob; Milligan, Peter A; Jonsson, E Niclas

2005-06-01

QT prolongation is an important biomarker of the arrhythmia torsades de pointes and appears to be related mainly to blockade of delayed inward cardiac rectifier potassium currents. The aim of this study was to quantify the relationship between in vitro human ether-a-go-go-related gene (hERG) potassium channel blockade and the magnitude of QT prolongation in humans for the class III antiarrhythmic dofetilide. The in vitro affinity and activity of dofetilide were determined in recombinant cell cultures expressing the hERG channel, and the QT-prolonging effect of dofetilide was assessed in 5 clinical studies (80 healthy volunteers and 17 patients with ischemic heart disease). A population pharmacokinetic-pharmacodynamic analysis of the in vitro and in vivo data was performed in NONMEM by use of the operational model of pharmacologic agonism to estimate the efficiency of transduction from ion channel binding to Fridericia-corrected QT response. A 3-compartment pharmacokinetic model with first-order absorption characterized the time course of dofetilide concentrations. On the basis of an in vitro potency of 5.13 ng/mL for potassium current inhibition and predicted unbound dofetilide concentrations, the estimated transducer ratio (tau) of 6.2 suggests that the QT response plateaus before currents are fully blocked. In our study population, 10% hERG blockade corresponds to a QT prolongation of 20 ms (95% confidence interval, 12-32 ms). With long-term dofetilide administration, tolerance develops with a half-life of 4.7 days. The current mechanism-based pharmacokinetic-pharmacodynamic model quantified the relationship between in vitro hERG channel blockade and clinical QT prolongation for dofetilide. This model may prove valuable for assessing the risk of QT prolongation in humans for other drugs that selectively block the hERG channel on the basis of in vitro assays and pharmacokinetic properties.

Involvement of Caveolin in Low K+-induced Endocytic Degradation of Cell-surface Human Ether-a-go-go-related Gene (hERG) Channels*

PubMed Central

Massaeli, Hamid; Sun, Tao; Li, Xian; Shallow, Heidi; Wu, Jimmy; Xu, Jianmin; Li, Wentao; Hanson, Christian; Guo, Jun; Zhang, Shetuan

2010-01-01

Reduction in the rapidly activating delayed rectifier K+ channel current (IKr) due to either mutations in the human ether-a-go-go-related gene (hERG) or drug block causes inherited or drug-induced long QT syndrome. A reduction in extracellular K+ concentration ([K+]o) exacerbates long QT syndrome. Recently, we demonstrated that lowering [K+]o promotes degradation of IKr in rabbit ventricular myocytes and of the hERG channel stably expressed in HEK 293 cells. In this study, we investigated the degradation pathways of hERG channels under low K+ conditions. We demonstrate that under low K+ conditions, mature hERG channels and caveolin-1 (Cav1) displayed a parallel time-dependent reduction. Mature hERG channels coprecipitated with Cav1 in co-immunoprecipitation analysis, and internalized hERG channels colocalized with Cav1 in immunocytochemistry analysis. Overexpression of Cav1 accelerated internalization of mature hERG channels in 0 mm K+o, whereas knockdown of Cav1 impeded this process. In addition, knockdown of dynamin 2 using siRNA transfection significantly impeded hERG internalization and degradation under low K+o conditions. In cultured neonatal rat ventricular myocytes, knockdown of caveolin-3 significantly impeded low K+o-induced reduction of IKr. Our data indicate that a caveolin-dependent endocytic route is involved in low K+o-induced degradation of mature hERG channels. PMID:20605793

Bag1 Co-chaperone Promotes TRC8 E3 Ligase-dependent Degradation of Misfolded Human Ether a Go-Go-related Gene (hERG) Potassium Channels.

PubMed

Hantouche, Christine; Williamson, Brittany; Valinsky, William C; Solomon, Joshua; Shrier, Alvin; Young, Jason C

2017-02-10

Cardiac long QT syndrome type 2 is caused by mutations in the human ether a go-go-related gene (hERG) potassium channel, many of which cause misfolding and degradation at the endoplasmic reticulum instead of normal trafficking to the cell surface. The Hsc70/Hsp70 chaperones assist the folding of the hERG cytosolic domains. Here, we demonstrate that the Hsp70 nucleotide exchange factor Bag1 promotes hERG degradation by the ubiquitin-proteasome system at the endoplasmic reticulum to regulate hERG levels and channel activity. Dissociation of hERG complexes containing Hsp70 and the E3 ubiquitin ligase CHIP requires the interaction of Bag1 with Hsp70, but this does not involve the Bag1 ubiquitin-like domain. The interaction with Bag1 then shifts hERG degradation to the membrane-anchored E3 ligase TRC8 and its E2-conjugating enzyme Ube2g2, as determined by siRNA screening. TRC8 interacts through the transmembrane region with hERG and decreases hERG functional expression. TRC8 also mediates degradation of the misfolded hERG-G601S disease mutant, but pharmacological stabilization of the mutant structure prevents degradation. Our results identify TRC8 as a previously unknown Hsp70-independent quality control E3 ligase for hERG. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Channel sialic acids limit hERG channel activity during the ventricular action potential.

PubMed

Norring, Sarah A; Ednie, Andrew R; Schwetz, Tara A; Du, Dongping; Yang, Hui; Bennett, Eric S

2013-02-01

Activity of human ether-a-go-go-related gene (hERG) 1 voltage-gated K(+) channels is responsible for portions of phase 2 and phase 3 repolarization of the human ventricular action potential. Here, we questioned whether and how physiologically and pathophysiologically relevant changes in surface N-glycosylation modified hERG channel function. Voltage-dependent hERG channel gating and activity were evaluated as expressed in a set of Chinese hamster ovary (CHO) cell lines under conditions of full glycosylation, no sialylation, no complex N-glycans, and following enzymatic deglycosylation of surface N-glycans. For each condition of reduced glycosylation, hERG channel steady-state activation and inactivation relationships were shifted linearly by significant depolarizing ∼9 and ∼18 mV, respectively. The hERG window current increased significantly by 50-150%, and the peak shifted by a depolarizing ∼10 mV. There was no significant change in maximum hERG current density. Deglycosylated channels were significantly more active (20-80%) than glycosylated controls during phases 2 and 3 of action potential clamp protocols. Simulations of hERG current and ventricular action potentials corroborated experimental data and predicted reduced sialylation leads to a 50-70-ms decrease in action potential duration. The data describe a novel mechanism by which hERG channel gating is modulated through physiologically and pathophysiologically relevant changes in N-glycosylation; reduced channel sialylation increases hERG channel activity during the action potential, thereby increasing the rate of action potential repolarization.

Natural products modulating the hERG channel: heartaches and hope.

PubMed

Kratz, Jadel M; Grienke, Ulrike; Scheel, Olaf; Mann, Stefan A; Rollinger, Judith M

2017-08-02

Covering: 1996-December 2016The human Ether-à-go-go Related Gene (hERG) channel is a voltage-gated potassium channel playing an essential role in the normal electrical activity in the heart. It is involved in the repolarization and termination of action potentials in excitable cardiac cells. Mutations in the hERG gene and hERG channel blockage by small molecules are associated with increased risk of fatal arrhythmias. Several drugs have been withdrawn from the market due to hERG channel-related cardiotoxicity. Moreover, as a result of its notorious ligand promiscuity, this ion channel has emerged as an important antitarget in early drug discovery and development. Surprisingly, the hERG channel blocking profile of natural compounds present in frequently consumed botanicals (i.e. dietary supplements, spices, and herbal medicinal products) is not routinely assessed. This comprehensive review will address these issues and provide a critical compilation of hERG channel data for isolated natural products and extracts over the past two decades (1996-2016). In addition, the review will provide (i) a solid basis for the molecular understanding of the physiological functions of the hERG channel, (ii) the translational potential of in vitro/in vivo results to cardiotoxicity in humans, (iii) approaches for the identification of hERG channel blockers from natural sources, (iv) future perspectives for cardiac safety guidelines and their applications within phytopharmaceuticals and dietary supplements, and (v) novel applications of hERG channel modulation (e.g. as a drug target).

Sequence and structure-specific elements of HERG mRNA determine channel synthesis and trafficking efficiency

PubMed Central

Sroubek, Jakub; Krishnan, Yamini; McDonald, Thomas V.

2013-01-01

Human ether-á-gogo-related gene (HERG) encodes a potassium channel that is highly susceptible to deleterious mutations resulting in susceptibility to fatal cardiac arrhythmias. Most mutations adversely affect HERG channel assembly and trafficking. Why the channel is so vulnerable to missense mutations is not well understood. Since nothing is known of how mRNA structural elements factor in channel processing, we synthesized a codon-modified HERG cDNA (HERG-CM) where the codons were synonymously changed to reduce GC content, secondary structure, and rare codon usage. HERG-CM produced typical IKr-like currents; however, channel synthesis and processing were markedly different. Translation efficiency was reduced for HERG-CM, as determined by heterologous expression, in vitro translation, and polysomal profiling. Trafficking efficiency to the cell surface was greatly enhanced, as assayed by immunofluorescence, subcellular fractionation, and surface labeling. Chimeras of HERG-NT/CM indicated that trafficking efficiency was largely dependent on 5′ sequences, while translation efficiency involved multiple areas. These results suggest that HERG translation and trafficking rates are independently governed by noncoding information in various regions of the mRNA molecule. Noncoding information embedded within the mRNA may play a role in the pathogenesis of hereditary arrhythmia syndromes and could provide an avenue for targeted therapeutics.—Sroubek, J., Krishnan, Y., McDonald, T V. Sequence- and structure-specific elements of HERG mRNA determine channel synthesis and trafficking efficiency. PMID:23608144

Predicting QT prolongation in humans during early drug development using hERG inhibition and an anaesthetized guinea-pig model

PubMed Central

Yao, X; Anderson, D L; Ross, S A; Lang, D G; Desai, B Z; Cooper, D C; Wheelan, P; McIntyre, M S; Bergquist, M L; MacKenzie, K I; Becherer, J D; Hashim, M A

2008-01-01

Background and purpose: Drug-induced prolongation of the QT interval can lead to torsade de pointes, a life-threatening ventricular arrhythmia. Finding appropriate assays from among the plethora of options available to predict reliably this serious adverse effect in humans remains a challenging issue for the discovery and development of drugs. The purpose of the present study was to develop and verify a reliable and relatively simple approach for assessing, during preclinical development, the propensity of drugs to prolong the QT interval in humans. Experimental approach: Sixteen marketed drugs from various pharmacological classes with a known incidence—or lack thereof—of QT prolongation in humans were examined in hERG (human ether a-go-go-related gene) patch-clamp assay and an anaesthetized guinea-pig assay for QT prolongation using specific protocols. Drug concentrations in perfusates from hERG assays and plasma samples from guinea-pigs were determined using liquid chromatography-mass spectrometry. Key results: Various pharmacological agents that inhibit hERG currents prolong the QT interval in anaesthetized guinea-pigs in a manner similar to that seen in humans and at comparable drug exposures. Several compounds not associated with QT prolongation in humans failed to prolong the QT interval in this model. Conclusions and implications: Analysis of hERG inhibitory potency in conjunction with drug exposures and QT interval measurements in anaesthetized guinea-pigs can reliably predict, during preclinical drug development, the risk of human QT prolongation. A strategy is proposed for mitigating the risk of QT prolongation of new chemical entities during early lead optimization. PMID:18587422

Interaction among hERG channel blockers is a potential mechanism of death in caffeine overdose.

PubMed

Zheng, Jifeng; Zhao, Wei; Xu, Kai; Chen, Qingmao; Chen, Yingying; Shen, Yueliang; Xiao, Liping; Jiang, Liqin; Chen, Yuan

2017-04-05

Caffeine overdose death is due to cardiac arrest, but its mechanism has not been explored in detail. In this study, our data showed that caffeine significantly prolonged the heart rate-corrected QT interval (QTc) of rabbits in vivo (P<0.05; n=7). Caffeine was also found to be a hERG channel blocker with an IC 50 of 5.04mM (n=5). Although these two findings likely link caffeine overdose death with hERG channel blockade, the amount of caffeine consumption needed to reach the IC 50 is very high. Further study demonstrated that addition another hERG blocker could lower the consumption of caffeine significantly, no matter whether two hERG blockers share the same binding sites. Our data does not rule out other possibility, however, it suggests that there is a potential causal relationship between caffeine overdose death with hERG channel and the interaction among these hERG blockers. Published by Elsevier B.V.

MiR-17-5p impairs trafficking of H-ERG K+ channel protein by targeting multiple er stress-related chaperones during chronic oxidative stress.

PubMed

Wang, Qi; Hu, Weina; Lei, Mingming; Wang, Yong; Yan, Bing; Liu, Jun; Zhang, Ren; Jin, Yuanzhe

2013-01-01

To investigate if microRNAs (miRNAs) play a role in regulating h-ERG trafficking in the setting of chronic oxidative stress as a common deleterious factor for many cardiac disorders. We treated neonatal rat ventricular myocytes and HEK293 cells with stable expression of h-ERG with H2O2 for 12 h and 48 h. Expression of miR-17-5p seed miRNAs was quantified by real-time RT-PCR. Protein levels of chaperones and h-ERG trafficking were measured by Western blot analysis. Luciferase reporter gene assay was used to study miRNA and target interactions. Whole-cell patch-clamp techniques were employed to record h-ERG K(+) current. H-ERG trafficking was impaired by H2O2 after 48 h treatment, accompanied by reciprocal changes of expression between miR-17-5p seed miRNAs and several chaperones (Hsp70, Hsc70, CANX, and Golga2), with the former upregulated and the latter downregulated. We established these chaperones as targets for miR-17-5p. Application miR-17-5p inhibitor rescued H2O2-induced impairment of h-ERG trafficking. Upregulation of endogenous by H2O2 or forced miR-17-5p expression either reduced h-ERG current. Sequestration of AP1 by its decoy molecule eliminated the upregulation of miR-17-5p, and ameliorated impairment of h-ERG trafficking. Collectively, deregulation of the miR-17-5p seed family miRNAs can cause severe impairment of h-ERG trafficking through targeting multiple ER stress-related chaperones, and activation of AP1 likely accounts for the deleterious upregulation of these miRNAs, in the setting of prolonged duration of oxidative stress. These findings revealed the role of miRNAs in h-ERG trafficking, which may contribute to the cardiac electrical disturbances associated with oxidative stress.

Mechanisms of zolpidem-induced long QT syndrome: acute inhibition of recombinant hERG K+ channels and action potential prolongation in human cardiomyocytes derived from induced pluripotent stem cells

PubMed Central

Jehle, J; Ficker, E; Wan, X; Deschenes, I; Kisselbach, J; Wiedmann, F; Staudacher, I; Schmidt, C; Schweizer, PA; Becker, R; Katus, HA; Thomas, D

2013-01-01

Background and Purpose Zolpidem, a short-acting hypnotic drug prescribed to treat insomnia, has been clinically associated with acquired long QT syndrome (LQTS) and torsade de pointes (TdP) tachyarrhythmia. LQTS is primarily attributed to reduction of cardiac human ether-a-go-go-related gene (hERG)/IKr currents. We hypothesized that zolpidem prolongs the cardiac action potential through inhibition of hERG K+ channels. Experimental Approach Two-electrode voltage clamp and whole-cell patch clamp electrophysiology was used to record hERG currents from Xenopus oocytes and from HEK 293 cells. In addition, hERG protein trafficking was evaluated in HEK 293 cells by Western blot analysis, and action potential duration (APD) was assessed in human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes. Key Results Zolpidem caused acute hERG channel blockade in oocytes (IC50 = 61.5 μM) and in HEK 293 cells (IC50 = 65.5 μM). Mutation of residues Y652 and F656 attenuated hERG inhibition, suggesting drug binding to a receptor site inside the channel pore. Channels were blocked in open and inactivated states in a voltage- and frequency-independent manner. Zolpidem accelerated hERG channel inactivation but did not affect I–V relationships of steady-state activation and inactivation. In contrast to the majority of hERG inhibitors, hERG cell surface trafficking was not impaired by zolpidem. Finally, acute zolpidem exposure resulted in APD prolongation in hiPSC-derived cardiomyocytes. Conclusions and Implications Zolpidem inhibits cardiac hERG K+ channels. Despite a relatively low affinity of zolpidem to hERG channels, APD prolongation may lead to acquired LQTS and TdP in cases of reduced repolarization reserve or zolpidem overdose. PMID:23061993

Cholesterol regulates HERG K+ channel activation by increasing phospholipase C β1 expression.

PubMed

Chun, Yoon Sun; Oh, Hyun Geun; Park, Myoung Kyu; Cho, Hana; Chung, Sungkwon

2013-01-01

Human ether-a-go-go-related gene (HERG) K(+) channel underlies the rapidly activating delayed rectifier K(+) conductance (IKr) during normal cardiac repolarization. Also, it may regulate excitability in many neuronal cells. Recently, we showed that enrichment of cell membrane with cholesterol inhibits HERG channels by reducing the levels of phosphatidylinositol 4,5-bisphosphate [PtdIns(4,5)P2] due to the activation of phospholipase C (PLC). In this study, we further explored the effect of cholesterol enrichment on HERG channel kinetics. When membrane cholesterol level was mildly increased in human embryonic kidney (HEK) 293 cells expressing HERG channel, the inactivation and deactivation kinetics of HERG current were not affected, but the activation rate was significantly decelerated at all voltages tested. The application of PtdIns(4,5)P2 or inhibitor for PLC prevented the effect of cholesterol enrichment, while the presence of antibody against PtdIns(4,5)P2 in pipette solution mimicked the effect of cholesterol enrichment. These results indicate that the effect of cholesterol enrichment on HERG channel is due to the depletion of PtdIns(4,5)P2. We also found that cholesterol enrichment significantly increases the expression of β1 and β3 isoforms of PLC (PLCβ1, PLCβ3) in the membrane. Since the effects of cholesterol enrichment on HERG channel were prevented by inhibiting transcription or by inhibiting PLCβ1 expression, we conclude that increased PLCβ1 expression leads to the deceleration of HERG channel activation rate via downregulation of PtdIns(4,5)P2. These results confirm a crosstalk between two plasma membrane-enriched lipids, cholesterol and PtdIns(4,5)P2, in the regulation of HERG channels.

Evidence for the hERG Liability of Antihistamines, Antipsychotics, and Anti-Infective Agents: A Systematic Literature Review From the ARITMO Project.

PubMed

Hazell, Lorna; Raschi, Emanuel; De Ponti, Fabrizio; Thomas, Simon H L; Salvo, Francesco; Ahlberg Helgee, Ernst; Boyer, Scott; Sturkenboom, Miriam; Shakir, Saad

2017-05-01

A systematic review was performed to categorize the hERG (human ether-a-go-go-related gene) liability of antihistamines, antipsychotics, and anti-infectives and to compare it with current clinical risk of torsade de pointes (TdP). Eligible studies were hERG assays reporting half-minimal inhibitory concentrations (IC50). A "hERG safety margin" was calculated from the IC50 divided by the peak human plasma concentration (free C max ). A margin below 30 defined hERG liability. Each drug was assigned an "uncertainty score" based on volume, consistency, precision, and internal and external validity of evidence. The hERG liability was compared to existing knowledge on TdP risk (www.credibledrugs.org). Of 1828 studies, 82 were eligible, allowing calculation of safety margins for 61 drugs. Thirty-one drugs (51%) had evidence of hERG liability including 6 with no previous mention of TdP risk (eg, desloratadine, lopinavir). Conversely, 16 drugs (26%) had no evidence of hERG liability including 6 with known, or at least conditional or possible, TdP risk (eg, chlorpromazine, sulpiride). The main sources of uncertainty were the validity of the experimental conditions used (antihistamines and antipsychotics) and nonuse of reference compounds (anti-infectives). In summary, hERG liability was categorized for 3 widely used drug classes, incorporating a qualitative assessment of the strength of available evidence. Some concordance with TdP risk was observed, although several drugs had hERG liability without evidence of clinical risk and vice versa. This may be due to gaps in clinical evidence, limitations of hERG/C max data, or other patient/drug-specific factors that contribute to real-life TdP risk. © 2016, The American College of Clinical Pharmacology.

Escitalopram block of hERG potassium channels.

PubMed

Chae, Yun Ju; Jeon, Ji Hyun; Lee, Hong Joon; Kim, In-Beom; Choi, Jin-Sung; Sung, Ki-Wug; Hahn, Sang June

2014-01-01

Escitalopram, a selective serotonin reuptake inhibitor, is the pharmacologically active S-enantiomer of the racemic mixture of RS-citalopram and is widely used in the treatment of depression. The effects of escitalopram and citalopram on the human ether-a-go-go-related gene (hERG) channels expressed in human embryonic kidney cells were investigated using voltage-clamp and Western blot analyses. Both drugs blocked hERG currents in a concentration-dependent manner with an IC50 value of 2.6 μM for escitalopram and an IC50 value of 3.2 μM for citalopram. The blocking of hERG by escitalopram was voltage-dependent, with a steep increase across the voltage range of channel activation. However, voltage independence was observed over the full range of activation. The blocking by escitalopram was frequency dependent. A rapid application of escitalopram induced a rapid and reversible blocking of the tail current of hERG. The extent of the blocking by escitalopram during the depolarizing pulse was less than that during the repolarizing pulse, suggesting that escitalopram has a high affinity for the open state of the hERG channel, with a relatively lower affinity for the inactivated state. Both escitalopram and citalopram produced a reduction of hERG channel protein trafficking to the plasma membrane but did not affect the short-term internalization of the hERG channel. These results suggest that escitalopram blocked hERG currents at a supratherapeutic concentration and that it did so by preferentially binding to both the open and the inactivated states of the channels and by inhibiting the trafficking of hERG channel protein to the plasma membrane.

A Direct Interaction between the Sigma-1 Receptor and the hERG Voltage-gated K+ Channel Revealed by Atomic Force Microscopy and Homogeneous Time-resolved Fluorescence (HTRF®)*

PubMed Central

Balasuriya, Dilshan; D'Sa, Lauren; Talker, Ronel; Dupuis, Elodie; Maurin, Fabrice; Martin, Patrick; Borgese, Franck; Soriani, Olivier; Edwardson, J. Michael

2014-01-01

The sigma-1 receptor is an endoplasmic reticulum chaperone protein, widely expressed in central and peripheral tissues, which can translocate to the plasma membrane and modulate the function of various ion channels. The human ether-à-go-go-related gene encodes hERG, a cardiac voltage-gated K+ channel that is abnormally expressed in many human cancers and is known to interact functionally with the sigma-1 receptor. Our aim was to investigate the nature of the interaction between the sigma-1 receptor and hERG. We show that the two proteins can be co-isolated from a detergent extract of stably transfected HEK-293 cells, consistent with a direct interaction between them. Atomic force microscopy imaging of the isolated protein confirmed the direct binding of the sigma-1 receptor to hERG monomers, dimers, and tetramers. hERG dimers and tetramers became both singly and doubly decorated by sigma-1 receptors; however, hERG monomers were only singly decorated. The distribution of angles between pairs of sigma-1 receptors bound to hERG tetramers had two peaks, at ∼90 and ∼180° in a ratio of ∼2:1, indicating that the sigma-1 receptor interacts with hERG with 4-fold symmetry. Homogeneous time-resolved fluorescence (HTRF®) allowed the detection of the interaction between the sigma-1 receptor and hERG within the plane of the plasma membrane. This interaction was resistant to sigma ligands, but was decreased in response to cholesterol depletion of the membrane. We suggest that the sigma-1 receptor may bind to hERG in the endoplasmic reticulum, aiding its assembly and trafficking to the plasma membrane. PMID:25266722

Down-regulation of ether-a-go-go-related gene potassium channel protein through sustained stimulation of AT1 receptor by angiotensin II.

PubMed

Cai, Yue; Wang, Yuhong; Xu, Jia; Zuo, Xu; Xu, Yanfang

2014-09-26

We investigated the effects of AT1 receptor stimulation by angiotensin II (Ang II) on human ether-a-go-go-related gene (hERG) potassium channel protein in a heterogeneous expression system with the human embryonic kidney (HEK) 293 cells which stably expressed hERG channel protein and were transiently transfected with the human AT1 receptors (HEK293/hERG). Western-blot analysis showed that Ang II significantly decreased the expression of mature hERG channel protein (155-kDa band) in a time- and dose-dependent manner without affecting the level of immature hERG channel protein (135-kDa band). The relative intensity of 155-kDa band was 64.7±6.8% of control (P<0.01) after treatment of Ang II at 100nM for 24h. To investigate the effect of Ang II on the degradation of mature hERG channel protein, we blocked forward trafficking from ER to Golgi with a Golgi transit inhibitor brefeldin A (10μM). Ang II significantly enhanced the time-dependent reduction of mature hERG channel protein. In addition, the proteasomal inhibitor lactacystin (5μM) inhibited Ang II-mediated the reduction of mature hERG channel protein, but the lysosomal inhibitor bafilomycin A1 (1μM) had no effect on the protein. The protein kinase C (PKC) inhibitor bisindolylmaleimide 1 (1μM) antagonized the reduction of mature hERG channel protein induced by Ang II. The results indicate that sustained stimulation of AT1 receptors by Ang II reduces the mature hERG channel protein via accelerating channel proteasomal degradation involving the PKC pathway. Copyright © 2014 Elsevier Inc. All rights reserved.

hERG K+ channel-associated cardiac effects of the antidepressant drug desipramine.

PubMed

Staudacher, Ingo; Wang, Lu; Wan, Xiaoping; Obers, Sabrina; Wenzel, Wolfgang; Tristram, Frank; Koschny, Ronald; Staudacher, Kathrin; Kisselbach, Jana; Koelsch, Patrick; Schweizer, Patrick A; Katus, Hugo A; Ficker, Eckhard; Thomas, Dierk

2011-02-01

Cardiac side effects of antidepressant drugs are well recognized. Adverse effects precipitated by the tricyclic drug desipramine include prolonged QT intervals, torsade de pointes tachycardia, heart failure, and sudden cardiac death. QT prolongation has been primarily attributed to acute blockade of hERG/I(Kr) currents. This study was designed to provide a more complete picture of cellular effects associated with desipramine. hERG channels were expressed in Xenopus laevis oocytes and human embryonic kidney (HEK 293) cells, and potassium currents were recorded using patch clamp and two-electrode voltage clamp electrophysiology. Ventricular action potentials were recorded from guinea pig cardiomyocytes. Protein trafficking and cell viability were evaluated in HEK 293 cells and in HL-1 mouse cardiomyocytes by immunocytochemistry, Western blot analysis, or colorimetric MTT assay, respectively. We found that desipramine reduced hERG currents by binding to a receptor site inside the channel pore. hERG protein surface expression was reduced after short-term treatment, revealing a previously unrecognized mechanism. When long-term effects were studied, forward trafficking was impaired and hERG currents were decreased. Action potential duration was prolonged upon acute and chronic desipramine exposure. Finally, desipramine triggered apoptosis in cells expressing hERG channels. Desipramine exerts at least four different cellular effects: (1) direct hERG channel block, (2) acute reduction of hERG surface expression, (3) chronic disruption of hERG trafficking, and (4) induction of apoptosis. These data highlight the complexity of hERG-associated drug effects.

Hsp40 Chaperones Promote Degradation of the hERG Potassium Channel*

PubMed Central

Walker, Valerie E.; Wong, Michael J. H.; Atanasiu, Roxana; Hantouche, Christine; Young, Jason C.; Shrier, Alvin

2010-01-01

Loss of function mutations in the hERG (human ether-a-go-go related gene or KCNH2) potassium channel underlie the proarrhythmic cardiac long QT syndrome type 2. Most often this is a consequence of defective trafficking of hERG mutants to the cell surface, with channel retention and degradation at the endoplasmic reticulum. Here, we identify the Hsp40 type 1 chaperones DJA1 (DNAJA1/Hdj2) and DJA2 (DNAJA2) as key modulators of hERG degradation. Overexpression of the DJAs reduces hERG trafficking efficiency, an effect eliminated by the proteasomal inhibitor lactacystin or with DJA mutants lacking their J domains essential for Hsc70/Hsp70 activation. Both DJA1 and DJA2 cause a decrease in the amount of hERG complexed with Hsc70, indicating a preferential degradation of the complex. Similar effects were observed with the E3 ubiquitin ligase CHIP. Both the DJAs and CHIP reduce hERG stability and act differentially on folding intermediates of hERG and the disease-related trafficking mutant G601S. We propose a novel role for the DJA proteins in regulating degradation and suggest that they act at a critical point in secretory pathway quality control. PMID:19940115

The amiodarone derivative KB130015 activates hERG1 potassium channels via a novel mechanism

PubMed Central

Gessner, Guido; Macianskiene, Regina; Starkus, John G.; Schönherr, Roland; Heinemann, Stefan H.

2010-01-01

Human ether à go-go related gene (hERG1) potassium channels underlie the repolarizing IKr current in the heart. Since they are targets of various drugs with cardiac side effects we tested whether the amiodarone derivative 2-methyl-3-(3,5-diiodo-4-carboxymethoxybenzyl)benzofuran (KB130015) blocks hERG1 channels like its parent compound. Using patch-clamp and two-electrode voltage-clamp techniques we found that KB130015 blocks native and recombinant hERG1 channels at high voltages, but it activates them at low voltages. The activating effect has an apparent EC50 value of 12 μM and is brought about by an about 4-fold acceleration of activation kinetics and a shift in voltage-dependent activation by −16 mV. Channel activation was not use-dependent and was independent of inactivation gating. KB130015 presumably binds to the hERG1 pore from the cytosolic side and functionally competes with hERG1 block by amiodarone, E4031 (N-[4-[[1-[2-(6-methyl-2-pyridinyl)ethyl] -4-piperidinyl] carbonyl] phenyl] methanesulfonamide dihydrochloride), and sertindole. Vice versa, amiodarone attenuates hERG1 activation by KB130015. Based on synergic channel activation by mallotoxin and KB130015 we conclude that the hERG1 pore contains at least two sites for activators that are functionally coupled among each other and to the cavity-blocker site. KB130015 and amiodarone may serve as lead structures for the identification of hERG1 pore-interacting drugs favoring channel activation vs. block. PMID:20097192

Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

PubMed

Wacker, Soren; Noskov, Sergei Yu

2018-05-01

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R 2 ~0.8 for pIC 50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

The effects of deoxyelephantopin on the cardiac delayed rectifier potassium channel current (IKr) and human ether-a-go-go-related gene (hERG) expression.

PubMed

Teah, Yi Fan; Abduraman, Muhammad Asyraf; Amanah, Azimah; Adenan, Mohd Ilham; Sulaiman, Shaida Fariza; Tan, Mei Lan

2017-09-01

Elephantopus scaber Linn and its major bioactive component, deoxyelephantopin are known for their medicinal properties and are often reported to have various cytotoxic and antitumor activities. This plant is widely used as folk medicine for a plethora of indications although its safety profile remains unknown. Human ether-a-go-go-related gene (hERG) encodes the cardiac I Kr current which is a determinant of the duration of ventricular action potentials and QT interval. The hERG potassium channel is an important antitarget in cardiotoxicity evaluation. This study investigated the effects of deoxyelephantopin on the current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells. The hERG tail currents following depolarization pulses were insignificantly affected by deoxyelephantopin in the transfected cell line. Current reduction was less than 40% as compared with baseline at the highest concentration of 50 μM. The results were consistent with the molecular docking simulation and hERG surface protein expression. Interestingly, it does not affect the hERG expression at both transcriptional and translational level at most concentrations, although higher concentration at 10 μM caused protein accumulation. In conclusion, deoxyelephantopin is unlikely a clinically significant hERG channel and I kr blocker. Copyright © 2017 Elsevier Ltd. All rights reserved.

A novel assessment of nefazodone-induced hERG inhibition by electrophysiological and stereochemical method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shin, Dae-Seop; Park, Myoung Joo; Lee, Hyang-Ae

2014-02-01

Nefazodone was used widely as an antidepressant until it was withdrawn from the U.S. market in 2004 due to hepatotoxicity. We have investigated methods to predict various toxic effects of drug candidates to reduce the failure rate of drug discovery. An electrophysiological method was used to assess the cardiotoxicity of drug candidates. Small molecules, including withdrawn drugs, were evaluated using a patch-clamp method to establish a database of hERG inhibition. Nefazodone inhibited hERG channel activity in our system. However, nefazodone-induced hERG inhibition indicated only a theoretical risk of cardiotoxicity. Nefazodone inhibited the hERG channel in a concentration-dependent manner with anmore » IC{sub 50} of 45.3 nM in HEK-293 cells. Nefazodone accelerated both the recovery from inactivation and its onset. Nefazodone also accelerated steady-state inactivation, although it did not modify the voltage-dependent character. Alanine mutants of hERG S6 and pore region residues were used to identify the nefazodone-binding site on hERG. The hERG S6 point mutants Y652A and F656A largely abolished the inhibition by nefazodone. The pore region mutant S624A mildly reduced the inhibition by nefazodone but T623A had little effect. A docking study showed that the aromatic rings of nefazodone interact with Y652 and F656 via π–π interactions, while an amine interacted with the S624 residue in the pore region. In conclusion, Y652 and F656 in the S6 domain play critical roles in nefazodone binding. - Highlights: • Nefazodone inhibits hERG channels with an IC{sub 50} of 45.3 nM in HEK-293 cells. • Nefazodone blocks hERG channels by binding to the open channels. • Y652 and F656 are important for binding of nefazodone. • The aromatic rings of nefazodone interact with Y652 and F656 via π–π interactions.« less

Effects of Tannic Acid, Green Tea and Red Wine on hERG Channels Expressed in HEK293 Cells.

PubMed

Chu, Xi; Guo, Yusong; Xu, Bingyuan; Li, Wenya; Lin, Yue; Sun, Xiaorun; Ding, Chunhua; Zhang, Xuan

2015-01-01

Tannic acid presents in varying concentrations in plant foods, and in relatively high concentrations in green teas and red wines. Human ether-à-go-go-related gene (hERG) channels expressed in multiple tissues (e.g. heart, neurons, smooth muscle and cancer cells), and play important roles in modulating cardiac action potential repolarization and tumor cell biology. The present study investigated the effects of tannic acid, green teas and red wines on hERG currents. The effects of tannic acid, teas and red wines on hERG currents stably transfected in HEK293 cells were studied with a perforated patch clamp technique. In this study, we demonstrated that tannic acid inhibited hERG currents with an IC50 of 3.4 μM and ~100% inhibition at higher concentrations, and significantly shifted the voltage dependent activation to more positive potentials (Δ23.2 mV). Remarkably, a 100-fold dilution of multiple types of tea (green tea, oolong tea and black tea) or red wine inhibited hERG currents by ~90%, and significantly shifted the voltage dependent activation to more positive potentials (Δ30.8 mV and Δ26.0 mV, respectively). Green tea Lung Ching and red wine inhibited hERG currents, with IC50 of 0.04% and 0.19%, respectively. The effects of tannic acid, teas and red wine on hERG currents were irreversible. These results suggest tannic acid is a novel hERG channel blocker and consequently provide a new mechanistic evidence for understanding the effects of tannic acid. They also revealed the potential pharmacological basis of tea- and red wine-induced biology activities.

Effects of Tannic Acid, Green Tea and Red Wine on hERG Channels Expressed in HEK293 Cells

PubMed Central

Xu, Bingyuan; Li, Wenya; Lin, Yue; Sun, Xiaorun; Ding, Chunhua; Zhang, Xuan

2015-01-01

Tannic acid presents in varying concentrations in plant foods, and in relatively high concentrations in green teas and red wines. Human ether-à-go-go-related gene (hERG) channels expressed in multiple tissues (e.g. heart, neurons, smooth muscle and cancer cells), and play important roles in modulating cardiac action potential repolarization and tumor cell biology. The present study investigated the effects of tannic acid, green teas and red wines on hERG currents. The effects of tannic acid, teas and red wines on hERG currents stably transfected in HEK293 cells were studied with a perforated patch clamp technique. In this study, we demonstrated that tannic acid inhibited hERG currents with an IC50 of 3.4 μM and ~100% inhibition at higher concentrations, and significantly shifted the voltage dependent activation to more positive potentials (Δ23.2 mV). Remarkably, a 100-fold dilution of multiple types of tea (green tea, oolong tea and black tea) or red wine inhibited hERG currents by ~90%, and significantly shifted the voltage dependent activation to more positive potentials (Δ30.8 mV and Δ26.0 mV, respectively). Green tea Lung Ching and red wine inhibited hERG currents, with IC50 of 0.04% and 0.19%, respectively. The effects of tannic acid, teas and red wine on hERG currents were irreversible. These results suggest tannic acid is a novel hERG channel blocker and consequently provide a new mechanistic evidence for understanding the effects of tannic acid. They also revealed the potential pharmacological basis of tea- and red wine-induced biology activities. PMID:26625122

Getting to the heart of hERG K(+) channel gating.

PubMed

Perry, Matthew D; Ng, Chai-Ann; Mann, Stefan A; Sadrieh, Arash; Imtiaz, Mohammad; Hill, Adam P; Vandenberg, Jamie I

2015-06-15

Potassium ion channels encoded by the human ether-a-go-go related gene (hERG) form the ion-conducting subunit of the rapid delayed rectifier potassium current (IKr ). Although hERG channels exhibit a widespread tissue distribution they play a particularly important role in the heart. There has been considerable interest in hERG K(+) channels for three main reasons. First, they have very unusual gating kinetics, most notably rapid and voltage-dependent inactivation coupled to slow deactivation, which has led to the suggestion that they may play a specific role in the suppression of arrhythmias. Second, mutations in hERG are the cause of 30-40% of cases of congenital long QT syndrome (LQTS), the commonest inherited primary arrhythmia syndrome. Third, hERG is the molecular target for the vast majority of drugs that cause drug-induced LQTS, the commonest cause of drug-induced arrhythmias and cardiac death. Drug-induced LQTS has now been reported for a large range of both cardiac and non-cardiac drugs, in which this side effect is entirely undesired. In recent years there have been comprehensive reviews published on hERG K(+) channels (Vandenberg et al. 2012) and we will not re-cover this ground. Rather, we focus on more recent work on the structural basis and dynamics of hERG gating with an emphasis on how the latest developments may facilitate translational research in the area of stratifying risk of arrhythmias. © 2015 The Authors. The Journal of Physiology © 2015 The Physiological Society.

Getting to the heart of hERG K+ channel gating

PubMed Central

Perry, Matthew D; Ng, Chai-Ann; Mann, Stefan A; Sadrieh, Arash; Imtiaz, Mohammad; Hill, Adam P; Vandenberg, Jamie I

2015-01-01

Potassium ion channels encoded by the human ether-a-go-go related gene (hERG) form the ion-conducting subunit of the rapid delayed rectifier potassium current (IKr). Although hERG channels exhibit a widespread tissue distribution they play a particularly important role in the heart. There has been considerable interest in hERG K+ channels for three main reasons. First, they have very unusual gating kinetics, most notably rapid and voltage-dependent inactivation coupled to slow deactivation, which has led to the suggestion that they may play a specific role in the suppression of arrhythmias. Second, mutations in hERG are the cause of 30–40% of cases of congenital long QT syndrome (LQTS), the commonest inherited primary arrhythmia syndrome. Third, hERG is the molecular target for the vast majority of drugs that cause drug-induced LQTS, the commonest cause of drug-induced arrhythmias and cardiac death. Drug-induced LQTS has now been reported for a large range of both cardiac and non-cardiac drugs, in which this side effect is entirely undesired. In recent years there have been comprehensive reviews published on hERG K+ channels (Vandenberg et al. 2012) and we will not re-cover this ground. Rather, we focus on more recent work on the structural basis and dynamics of hERG gating with an emphasis on how the latest developments may facilitate translational research in the area of stratifying risk of arrhythmias. PMID:25820318

Physical and functional interaction between integrins and hERG potassium channels.

PubMed

Arcangeli, A; Becchetti, A; Cherubini, A; Crociani, O; Defilippi, P; Guasti, L; Hofmann, G; Pillozzi, S; Olivotto, M; Wanke, E

2004-11-01

Integrins are adhesion receptors capable of transmitting intracellular signals that regulate many different cellular functions. Among integrin-mediated signals, the activation of ion channels can be included. We demonstrated that a long-lasting activation of hERG (human ether-a-go-go-related gene) potassium channels occurs in both human neuroblastoma and leukaemia cells after the activation of the beta1 integrin subunit. This activation is apparently a determining factor inducing neurite extension and osteoclastic differentiation in both the cell types. More recently, we provided evidences that beta1 integrins and hERG channels co-precipitate in both the cell types. Preliminary results suggest that a macromolecular signalling complex indeed occurs between integrins and the hERG1 protein and that hERG channel activity can modulate integrin downstream signalling.

Effects of premature stimulation on HERG K+ channels

PubMed Central

Lu, Yu; Mahaut-Smith, Martyn P; Varghese, Anthony; Huang, Christopher L-H; Kemp, Paul R; Vandenberg, Jamie I

2001-01-01

The unusual kinetics of human ether-à-go-go-related gene (HERG) K+ channels are consistent with a role in the suppression of arrhythmias initiated by premature beats. Action potential clamp protocols were used to investigate the effect of premature stimulation on HERG K+ channels, transfected in Chinese hamster ovary cells, at 37 °C. HERG K+ channel currents peaked during the terminal repolarization phase of normally paced action potential waveforms. However, the magnitude of the current and the time point at which conductance was maximal depended on the type of action potential waveform used (epicardial, endocardial, Purkinje fibre or atrial). HERG K+ channel currents recorded during premature action potentials consisted of an early transient outward current followed by a sustained outward current. The magnitude of the transient current component showed a biphasic dependence on the coupling interval between the normally paced and premature action potentials and was maximal at a coupling interval equivalent to 90% repolarization (APD90) for ventricular action potentials. The largest transient current response occurred at shorter coupling intervals for Purkinje fibre (APD90– 20 ms) and atrial (APD90– 30 ms) action potentials. The magnitude of the sustained current response following premature stimulation was similar to that recorded during the first action potential for ventricular action potential waveforms. However, for Purkinje and atrial action potentials the sustained current response was significantly larger during the premature action potential than during the normally paced action potential. A Markov model that included three closed states, one open and one inactivated state with transitions permitted between the pre-open closed state and the inactivated state, successfully reproduced our results for the effects of premature stimuli, both during square pulse and action potential clamp waveforms. These properties of HERG K+ channels may help to suppress arrhythmias initiated by early afterdepolarizations and premature beats in the ventricles, Purkinje fibres or atria. PMID:11744759

Overcoming HERG affinity in the discovery of the CCR5 antagonist maraviroc.

PubMed

Price, David A; Armour, Duncan; de Groot, Marcel; Leishman, Derek; Napier, Carolyn; Perros, Manos; Stammen, Blanda L; Wood, Anthony

2006-09-01

The discovery of maraviroc 17 is described with particular reference to the generation of high selectivity over affinity for the HERG potassium channel. This was achieved through the use of a high throughput binding assay for the HERG channel that is known to show an excellent correlation with functional effects.

Towards a Structural View of Drug Binding to hERG K+ Channels.

PubMed

Vandenberg, Jamie I; Perozo, Eduardo; Allen, Toby W

2017-10-01

The human ether-a-go-go-related gene (hERG) K + channel is of great medical and pharmaceutical relevance. Inherited mutations in hERG result in congenital long-QT syndrome which is associated with a markedly increased risk of cardiac arrhythmia and sudden death. hERG K + channels are also remarkably susceptible to block by a wide range of drugs, which in turn can cause drug-induced long-QT syndrome and an increased risk of sudden death. The recent determination of the near-atomic resolution structure of the hERG K + channel, using single-particle cryo-electron microscopy (cryo-EM), provides tremendous insights into how these channels work. It also suggests a way forward in our quest to understand why these channels are so promiscuous with respect to drug binding. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatial gradients in action potential duration created by regional magnetofection of hERG are a substrate for wavebreak and turbulent propagation in cardiomyocyte monolayers.

PubMed

Campbell, Katherine; Calvo, Conrado J; Mironov, Sergey; Herron, Todd; Berenfeld, Omer; Jalife, José

2012-12-15

Spatial dispersion of action potential duration (APD) is a substrate for the maintenance of cardiac fibrillation, but the mechanisms are poorly understood. We investigated the role played by spatial APD dispersion in fibrillatory dynamics. We used an in vitro model in which spatial gradients in the expression of ether-à-go-go-related (hERG) protein, and thus rapid delayed rectifying K(+) current (I(Kr)) density, served to generate APD dispersion, high-frequency rotor formation, wavebreak and fibrillatory conduction. A unique adenovirus-mediated magnetofection technique generated well-controlled gradients in hERG and green fluorescent protein (GFP) expression in neonatal rat ventricular myocyte monolayers. Computer simulations using a realistic neonatal rat ventricular myocyte monolayer model provided crucial insight into the underlying mechanisms. Regional hERG overexpression shortened APD and increased rotor incidence in the hERG overexpressing region. An APD profile at 75 percent repolarization with a 16.6 ± 0.72 ms gradient followed the spatial profile of hERG-GFP expression; conduction velocity was not altered. Rotors in the infected region whose maximal dominant frequency was 12.9 Hz resulted in wavebreak at the interface (border zone) between infected and non-infected regions; dominant frequency distribution was uniform when the maximal dominant frequency was <12.9 Hz or the rotors resided in the uninfected region. Regularity at the border zone was lowest when rotors resided in the infected region. In simulations, a fivefold regional increase in I(Kr) abbreviated the APD and hyperpolarized the resting potential. However, the steep APD gradient at the border zone proved to be the primary mechanism of wavebreak and fibrillatory conduction. This study provides insight at the molecular level into the mechanisms by which spatial APD dispersion contributes to wavebreak, rotor stabilization and fibrillatory conduction.

Spatial gradients in action potential duration created by regional magnetofection of hERG are a substrate for wavebreak and turbulent propagation in cardiomyocyte monolayers

PubMed Central

Campbell, Katherine; Calvo, Conrado J; Mironov, Sergey; Herron, Todd; Berenfeld, Omer; Jalife, José

2012-01-01

Spatial dispersion of action potential duration (APD) is a substrate for the maintenance of cardiac fibrillation, but the mechanisms are poorly understood. We investigated the role played by spatial APD dispersion in fibrillatory dynamics. We used an in vitro model in which spatial gradients in the expression of ether-à-go-go-related (hERG) protein, and thus rapid delayed rectifying K+ current (IKr) density, served to generate APD dispersion, high-frequency rotor formation, wavebreak and fibrillatory conduction. A unique adenovirus-mediated magnetofection technique generated well-controlled gradients in hERG and green fluorescent protein (GFP) expression in neonatal rat ventricular myocyte monolayers. Computer simulations using a realistic neonatal rat ventricular myocyte monolayer model provided crucial insight into the underlying mechanisms. Regional hERG overexpression shortened APD and increased rotor incidence in the hERG overexpressing region. An APD profile at 75 percent repolarization with a 16.6 ± 0.72 ms gradient followed the spatial profile of hERG-GFP expression; conduction velocity was not altered. Rotors in the infected region whose maximal dominant frequency was ≥12.9 Hz resulted in wavebreak at the interface (border zone) between infected and non-infected regions; dominant frequency distribution was uniform when the maximal dominant frequency was <12.9 Hz or the rotors resided in the uninfected region. Regularity at the border zone was lowest when rotors resided in the infected region. In simulations, a fivefold regional increase in IKr abbreviated the APD and hyperpolarized the resting potential. However, the steep APD gradient at the border zone proved to be the primary mechanism of wavebreak and fibrillatory conduction. This study provides insight at the molecular level into the mechanisms by which spatial APD dispersion contributes to wavebreak, rotor stabilization and fibrillatory conduction. PMID:23090949

Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development.

PubMed

Klon, Anthony E

2010-07-01

The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.

Mechanism of HERG potassium channel inhibition by tetra-n-octylammonium bromide and benzethonium chloride

DOE Office of Scientific and Technical Information (OSTI.GOV)

Long, Yan; Lin, Zuoxian; Xia, Menghang

Tetra-n-octylammonium bromide and benzethonium chloride are synthetic quaternary ammonium salts that are widely used in hospitals and industries for the disinfection and surface treatment and as the preservative agent. Recently, the activities of HERG channel inhibition by these compounds have been found to have potential risks to induce the long QT syndrome and cardiac arrhythmia, although the mechanism of action is still elusive. This study was conducted to investigate the mechanism of HERG channel inhibition by these compounds by using whole-cell patch clamp experiments in a CHO cell line stably expressing HERG channels. Tetra-n-octylammonium bromide and benzethonium chloride exhibited concentration-dependentmore » inhibitions of HERG channel currents with IC{sub 50} values of 4 nM and 17 nM, respectively, which were also voltage-dependent and use-dependent. Both compounds shifted the channel activation I–V curves in a hyperpolarized direction for 10–15 mV and accelerated channel activation and inactivation processes by 2-fold. In addition, tetra-n-octylammonium bromide shifted the inactivation I–V curve in a hyperpolarized direction for 24.4 mV and slowed the rate of channel deactivation by 2-fold, whereas benzethonium chloride did not. The results indicate that tetra-n-octylammonium bromide and benzethonium chloride are open-channel blockers that inhibit HERG channels in the voltage-dependent, use-dependent and state-dependent manners. - Highlights: ► Tetra-n-octylammonium and benzethonium are potent HERG channel inhibitors. ► Channel activation and inactivation processes are accelerated by the two compounds. ► Both compounds are the open-channel blockers to HERG channels. ► HERG channel inhibition by both compounds is use-, voltage- and state dependent. ► The in vivo risk of QT prolongation needs to be studied for the two compounds.« less

Development of a High-Throughput Flow Cytometry Assay to Monitor Defective Trafficking and Rescue of Long QT2 Mutant hERG Channels

PubMed Central

Kanner, Scott A.; Jain, Ananya; Colecraft, Henry M.

2018-01-01

Long QT Syndrome (LQTS) is an acquired or inherited disorder characterized by prolonged QT interval, exertion-triggered arrhythmias, and sudden cardiac death. One of the most prevalent hereditary LQTS subtypes, LQT2, results from loss-of-function mutations in the hERG channel, which conducts IKr, the rapid component of the delayed rectifier K+ current, critical for cardiac repolarization. The majority of LQT2 mutations result in Class 2 deficits characterized by impaired maturation and trafficking of hERG channels. Here, we have developed a high-throughput flow cytometric assay to analyze the surface and total expression of wild-type (WT) and mutant hERG channels with single-cell resolution. To test our method, we focused on 16 LQT2 mutations in the hERG Per-Arnt-Sim (PAS) domain that were previously studied via a widely used biochemical approach that compares levels of 135-kDa immature and 155-kDa fully glycosylated hERG protein to infer surface expression. We confirmed that LQT2 mutants expressed in HEK293 cells displayed a decreased surface density compared to WT hERG, and were differentially rescued by low temperature. However, we also uncovered some notable differences from the findings obtained via the biochemical approach. In particular, three mutations (N33T, R56Q, and A57P) with apparent WT-like hERG glycosylation patterns displayed up to 50% decreased surface expression. Furthermore, despite WT-like levels of complex glycosylation, these mutants have impaired forward trafficking, and exhibit varying half-lives at the cell surface. The results highlight utility of the surface labeling/flow cytometry approach to quantitatively assess trafficking deficiencies associated with LQT2 mutations, to discern underlying mechanisms, and to report on interventions that rescue deficits in hERG surface expression. PMID:29725305

LOFAR-Boötes: properties of high- and low-excitation radio galaxies at 0.5 < z < 2.0

NASA Astrophysics Data System (ADS)

Williams, W. L.; Calistro Rivera, G.; Best, P. N.; Hardcastle, M. J.; Röttgering, H. J. A.; Duncan, K. J.; de Gasperin, F.; Jarvis, M. J.; Miley, G. K.; Mahony, E. K.; Morabito, L. K.; Nisbet, D. M.; Prandoni, I.; Smith, D. J. B.; Tasse, C.; White, G. J.

2018-04-01

This paper presents a study of the redshift evolution of radio-loud active galactic nuclei (AGN) as a function of the properties of their galaxy hosts in the Boötes field. To achieve this we match low-frequency radio sources from deep 150-MHz LOFAR (LOw Frequency ARray) observations to an I-band-selected catalogue of galaxies, for which we have derived photometric redshifts, stellar masses, and rest-frame colours. We present spectral energy distribution (SED) fitting to determine the mid-infrared AGN contribution for the radio sources and use this information to classify them as high- versus low-excitation radio galaxies (HERGs and LERGs) or star-forming galaxies. Based on these classifications, we construct luminosity functions for the separate redshift ranges going out to z = 2. From the matched radio-optical catalogues, we select a sub-sample of 624 high power (P150 MHz > 1025 W Hz-1) radio sources between 0.5 ≤ z < 2. For this sample, we study the fraction of galaxies hosting HERGs and LERGs as a function of stellar mass and host galaxy colour. The fraction of HERGs increases with redshift, as does the fraction of sources in galaxies with lower stellar masses. We find that the fraction of galaxies that host LERGs is a strong function of stellar mass as it is in the local Universe. This, combined with the strong negative evolution of the LERG luminosity functions over this redshift range, is consistent with LERGs being fuelled by hot gas in quiescent galaxies.

Block of HERG human K(+) channel and IKr of guinea pig cardiomyocytes by chlorpromazine.

PubMed

Lee, So-Young; Choi, Se-Young; Youm, Jae Boum; Ho, Won-Kyung; Earm, Yung E; Lee, Chin O; Jo, Su-Hyun

2004-05-01

Chlorpromazine, a commonly used antipsychotic drug, has been known to induce QT prolongation and torsades de pointes, which can cause sudden death. We studied the effects of chlorpromazine on the human ether-a-go-go-related gene (HERG) channel expressed in Xenopus oocytes and on delayed rectifier K current of guinea pig ventricular myocytes. Application of chlorpromazine showed a dose-dependent decrease in the amplitudes of steady-state currents and tail currents of HERG. The decrease became more pronounced at increasingly positive potential, suggesting that the blockade of HERG by chlorpromazine is voltage dependent. IC50 for chlorpromazine block of HERG current was progressively decreased according to depolarization: IC50 values at -30, 0, and +30 mV were 10.5, 8.8, and 4.9 microM, respectively. The block of HERG current during the voltage step increased with time starting from a level 89% of the control current. In guinea pig ventricular myocytes, bath application of 2 and 5 microM chlorpromazine at 36 degree C blocked rapidly activating delayed rectifier K current (IKr) by 31 and 83%, respectively. How-ever, the same concentrations of chlorpromazine failed to significantly block slowly activating delayed rectifier K current (IKs). Our findings suggest that the arrhythmogenic side effect of chlorpromazine is caused by blockade of HERG and rapid component of delayed rectifier K current rather than by blockade of the slow component.

Localization and functional consequences of a direct interaction between TRIOBP-1 and hERG proteins in the heart.

PubMed

Jones, David K; Johnson, Ashley C; Roti Roti, Elon C; Liu, Fang; Uelmen, Rebecca; Ayers, Rebecca A; Baczko, Istvan; Tester, David J; Ackerman, Michael J; Trudeau, Matthew C; Robertson, Gail A

2018-03-22

Reduced levels of the cardiac human (h)ERG ion channel protein and the corresponding repolarizing current I Kr can cause arrhythmia and sudden cardiac death, but the underlying cellular mechanisms controlling hERG surface expression are not well understood. Here, we identified TRIOBP-1, an F-actin-binding protein previously associated with actin polymerization, as a putative hERG-interacting protein in a yeast-two hybrid screen of a cardiac library. We corroborated this interaction by performing Förster resonance energy transfer (FRET) in HEK293 cells and co-immunoprecipitation in HEK293 cells and native cardiac tissue. TRIOBP-1 overexpression reduced hERG surface expression and current density, whereas reducing TRIOBP-1 expression via shRNA knockdown resulted in increased hERG protein levels. Immunolabeling in rat cardiomyocytes showed that native TRIOBP-1 colocalized predominantly with myosin-binding protein C and secondarily with rat ERG. In human stem cell-derived cardiomyocytes, TRIOBP-1 overexpression caused intracellular co-sequestration of hERG signal, reduced native I Kr and disrupted action potential repolarization. Ca 2+ currents were also somewhat reduced and cell capacitance was increased. These findings establish that TRIOBP-1 interacts directly with hERG and can affect protein levels, I Kr magnitude and cardiac membrane excitability. © 2018. Published by The Company of Biologists Ltd.

Induction of autoimmune response to the extracellular loop of the HERG channel pore induces QTc prolongation in guinea‐pigs

PubMed Central

Fabris, Frank; Yue, Yuankun; Qu, Yongxia; Chahine, Mohamed; Sobie, Eric; Lee, Peng; Wieczorek, Rosemary; Jiang, Xian‐Cheng; Capecchi, Pier‐Leopoldo; Laghi‐Pasini, Franco; Lazzerini, Pietro‐Enea

2016-01-01

Key points Channelopathies of autoimmune origin are novel and are associated with corrected QT (QTc) prolongation and complex ventricular arrhythmias.We have recently demonstrated that anti‐SSA/Ro antibodies from patients with autoimmune diseases and with QTc prolongation on the ECG target the human ether‐à‐go‐go‐related gene (HERG) K+ channel by inhibiting the corresponding current, I Kr, at the pore region.Immunization of guinea‐pigs with a peptide (E‐pore peptide) corresponding to the extracellular loop region connecting the S5 and S6 segments of the HERG channel induces high titres of antibodies that inhibit I Kr, lengthen the action potential and cause QTc prolongation on the surface ECG. In addition, anti‐SSA/Ro‐positive sera from patients with connective tissue diseases showed high reactivity to the E‐pore peptide.The translational impact is the development of a peptide‐based approach for the diagnosis and treatment of autoimmune‐associated long QT syndrome. Abstract We recently demonstrated that anti‐SSA/52 kDa Ro antibodies (Abs) from patients with autoimmune diseases and corrected QT (QTc) prolongation directly target and inhibit the human ether‐à‐go‐go‐related gene (HERG) K+ channel at the extracellular pore (E‐pore) region, where homology with SSA/52 kDa Ro antigen was demonstrated. We tested the hypothesis that immunization of guinea‐pigs with a peptide corresponding to the E‐pore region (E‐pore peptide) will generate pathogenic inhibitory Abs and cause QTc prolongation. Guinea‐pigs were immunized with a 31‐amino‐acid peptide corresponding to the E‐pore region of HERG. On days 10–62 after immunization, ECGs were recorded and blood was sampled for the detection of E‐pore peptide Abs. Serum samples from patients with autoimmune diseases were evaluated for reactivity to E‐pore peptide by enzyme‐linked immunosorbent assay (ELISA), and histology was performed on hearts using Masson's Trichrome. Inhibition of the HERG channel was assessed by electrophysiology and by computational modelling of the human ventricular action potential. The ELISA results revealed the presence of high titres of E‐pore peptide Abs and significant QTc prolongation after immunization. High reactivity to E‐pore peptide was found using anti‐SSA/Ro Ab‐positive sera from patients with QTc prolongation. Histological data showed no evidence of fibrosis in immunized hearts. Simulations of simultaneous inhibition of repolarizing currents by anti‐SSA/Ro Ab‐positive sera showed the predominance of the HERG channel in controlling action potential duration and the QT interval. These results are the first to demonstrate that inhibitory Abs to the HERG E‐pore region induce QTc prolongation in immunized guinea‐pigs by targeting the HERG channel independently from fibrosis. The reactivity of anti‐SSA/Ro Ab‐positive sera from patients with connective tissue diseases with the E‐pore peptide opens novel pharmacotherapeutic avenues in the diagnosis and management of autoimmune‐associated QTc prolongation. PMID:27296897

Structural implications of hERG K+ channel block by a high-affinity minimally structured blocker

PubMed Central

Helliwell, Matthew V.; Zhang, Yihong; El Harchi, Aziza; Du, Chunyun; Hancox, Jules C.; Dempsey, Christopher E.

2018-01-01

Cardiac potassium channels encoded by human ether-à-go-go–related gene (hERG) are major targets for structurally diverse drugs associated with acquired long QT syndrome. This study characterized hERG channel inhibition by a minimally structured high-affinity hERG inhibitor, Cavalli-2, composed of three phenyl groups linked by polymethylene spacers around a central amino group, chosen to probe the spatial arrangement of side chain groups in the high-affinity drug-binding site of the hERG pore. hERG current (IhERG) recorded at physiological temperature from HEK293 cells was inhibited with an IC50 of 35.6 nm with time and voltage dependence characteristic of blockade contingent upon channel gating. Potency of Cavalli-2 action was markedly reduced for attenuated inactivation mutants located near (S620T; 54-fold) and remote from (N588K; 15-fold) the channel pore. The S6 Y652A and F656A mutations decreased inhibitory potency 17- and 75-fold, respectively, whereas T623A and S624A at the base of the selectivity filter also decreased potency (16- and 7-fold, respectively). The S5 helix F557L mutation decreased potency 10-fold, and both F557L and Y652A mutations eliminated voltage dependence of inhibition. Computational docking using the recent cryo-EM structure of an open channel hERG construct could only partially recapitulate experimental data, and the high dependence of Cavalli-2 block on Phe-656 is not readily explainable in that structure. A small clockwise rotation of the inner (S6) helix of the hERG pore from its configuration in the cryo-EM structure may be required to optimize Phe-656 side chain orientations compatible with high-affinity block. PMID:29545312

Mitigating hERG Inhibition: Design of Orally Bioavailable CCR5 Antagonists as Potent Inhibitors of R5 HIV-1 Replication

PubMed Central

2012-01-01

A series of CCR5 antagonists representing the thiophene-3-yl-methyl ureas were designed that met the pharmacological criteria for HIV-1 inhibition and mitigated a human ether-a-go-go related gene (hERG) inhibition liability. Reducing lipophilicity was the main design criteria used to identify compounds that did not inhibit the hERG channel, but subtle structural modifications were also important. Interestingly, within this series, compounds with low hERG inhibition prolonged the action potential duration (APD) in dog Purkinje fibers, suggesting a mixed effect on cardiac ion channels. PMID:24900457

Mitragynine and its potential blocking effects on specific cardiac potassium channels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tay, Yea Lu; Teah, Yi Fan; Chong, Yoong Min

2016-08-15

Mitragyna speciosa Korth is known for its euphoric properties and is frequently used for recreational purposes. Several poisoning and fatal cases involving mitragynine have been reported but the underlying causes remain unclear. Human ether-a-go-go-related gene (hERG) encodes the cardiac I{sub Kr} current which is a determinant of the duration of ventricular action potentials and QT interval. On the other hand, I{sub K1}, a Kir current mediated by Kir2.1 channel and I{sub KACh}, a receptor-activated Kir current mediated by GIRK channel are also known to be important in maintaining the cardiac function. This study investigated the effects of mitragynine on themore » current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells and Xenopus oocytes. The effects on Kir2.1 and GIRK channels currents were also determined in the oocytes. The hERG tail currents following depolarization pulses were inhibited by mitragynine with an IC{sub 50} value of 1.62 μM and 1.15 μM in the transfected cell line and Xenopus oocytes, respectively. The S6 point mutations of Y652A and F656A attenuated the inhibitor effects of mitragynine, indicating that mitragynine interacts with these high affinity drug-binding sites in the hERG channel pore cavity which was consistent with the molecular docking simulation. Interestingly, mitragynine does not affect the hERG expression at the transcriptional level but inhibits the protein expression. Mitragynine is also found to inhibit I{sub KACh} current with an IC{sub 50} value of 3.32 μM but has no significant effects on I{sub K1}. Blocking of both hERG and GIRK channels may cause additive cardiotoxicity risks. - Highlights: • The potential cardiac potassium channel blocking properties of mitragynine were investigated. • Mitragynine blocks hERG channel and I{sub Kr} in hERG-transfected HEK293 cells and hERG cRNA-injected Xenopus oocytes. • Mitragynine inhibits the hERG protein but not the mRNA expression. • Mitragynine inhibits GIRK channel. • Simultaneous hERG and GIRK channel blockade may cause additive cardiotoxicity risks.« less

Computational Models for Understanding of Structure, Function and Pharmacology of the Cardiac Potassium Channel Kv11.1 (hERG).

PubMed

Wacker, Soren; Noskov, Sergei Yu; Perissinotti, Laura L

2017-01-01

The rapid delayed rectifier current IKr is one of the major K+ currents involved in repolarization of the human cardiac action potential. Various inherited or drug-induced forms of the long QT syndrome (LQTS) in humans are linked to functional and structural modifications in the IKr conducting channels. IKr is carried by the potassium channel Kv11.1 encoded by the gene KCNH2 (commonly referred to as human ether-a-go-go-related gene or hERG) [1, 2]. The first necessary step for predicting emergent drug effects on the heart is determining and modeling the binding thermodynamics and kinetics of primary and major off-target drug interactions with subcellular targets. The bulk of drugs that target hERG channels are known to have complex interactions at the atomic scale. Accordingly, one of the goals for this review is to provide comprehensive guide in the universe of computational models aiming to refine our understanding of structure-function relations in Kv11.1 and its isoforms. The special emphasis is placed on the mapping of drug binding sites and tentative mechanisms of channel inhibition and activation by drugs. An overview over recent structural models and mapping of binding sites for blockers and activators of IKr current along with the discussion on agreements and discrepancies among different models is presented. There is an apparent reciprocity or feedback loop between drug binding and action potential of the cardiac myocytes. Thus one has to connect drug binding to a particular receptor so that its functional consequences impact on the action potential duration. The natural pathway is to develop multi-scale models that connect between receptor and cellular scales. The potential for such multi-scale model development is discussed through the lens of common gating models. Accordingly, the second part of this review covers an ongoing development of the kinetic models of gating transitions and cardiac ion currents carried by hERG channels with and without drug bound. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The S4–S5 Linker Acts as a Signal Integrator for hERG K+ Channel Activation and Deactivation Gating

PubMed Central

Ng, Chai Ann; Perry, Matthew D.; Tan, Peter S.; Hill, Adam P.; Kuchel, Philip W.; Vandenberg, Jamie I.

2012-01-01

Human ether-à-go-go-related gene (hERG) K+ channels have unusual gating kinetics. Characterised by slow activation/deactivation but rapid inactivation/recovery from inactivation, the unique gating kinetics underlie the central role hERG channels play in cardiac repolarisation. The slow activation and deactivation kinetics are regulated in part by the S4–S5 linker, which couples movement of the voltage sensor domain to opening of the activation gate at the distal end of the inner helix of the pore domain. It has also been suggested that cytosolic domains may interact with the S4–S5 linker to regulate activation and deactivation kinetics. Here, we show that the solution structure of a peptide corresponding to the S4–S5 linker of hERG contains an amphipathic helix. The effects of mutations at the majority of residues in the S4–S5 linker of hERG were consistent with the previously identified role in coupling voltage sensor movement to the activation gate. However, mutations to Ser543, Tyr545, Gly546 and Ala548 had more complex phenotypes indicating that these residues are involved in additional interactions. We propose a model in which the S4–S5 linker, in addition to coupling VSD movement to the activation gate, also contributes to interactions that stabilise the closed state and a separate set of interactions that stabilise the open state. The S4–S5 linker therefore acts as a signal integrator and plays a crucial role in the slow deactivation kinetics of the channel. PMID:22359612

The role of hERG1 ion channels in epithelial-mesenchymal transition and the capacity of riluzole to reduce cisplatin resistance in colorectal cancer cells.

PubMed

Fortunato, Angelo

2017-08-01

The transition of cells from the epithelial to the mesenchymal state (EMT) plays an important role in tumor progression. EMT allows cells to acquire mobility, stem-like behavior and resistance to apoptosis and drug treatment. These features turn EMT into a central process in tumor biology. Ion channels are attractive targets for the treatment of cancer since they play critical roles in controlling a wide range of physiological processes that are frequently deregulated in cancer. Here, we investigated the role of ether-a-go-go-related 1 (hERG1) ion channels in the EMT of colorectal cancer cells. We studied the epithelial-mesenchymal profile of different colorectal cancer-derived cell lines and the expression of hERG1 potassium channels in these cell lines using real-time PCR. Next, we knocked down hERG1 expression in HCT116 cells using lentivirus mediated RNA interference and characterized the hERG1 silenced cells in vitro and in vivo. Finally, we investigated the capacity of riluzole, an ion channel-modulating drug used in humans to treat amyotrophic lateral sclerosis, to reduce the resistance of the respective colorectal cancer cells to the chemotherapeutic drug cisplatin. We found that of the colorectal cancer-derived cell lines tested, HCT116 showed the highest mesenchymal profile and a high hERG1 expression. Subsequent hERG1 expression knockdown induced a change in cell morphology, which was accompanied by a reduction in the proliferative and tumorigenic capacities of the cells. Notably, we found that hERG1expression knockdown elicited a reversion of the EMT profile in HCT116 cells with a reacquisition of the epithelial-like profile. We also found that riluzole increased the sensitivity of HCT116 cisplatin-resistant cells to cisplatin. Our data indicate that hERG1 plays a role in the EMT of colorectal cancer cells and that its knockdown reduces the proliferative and tumorigenic capacities of these cells. In addition, we conclude that riluzole may be used in combination with cisplatin to reduce chemo-resistance in colorectal cancer cells.

[Lead compound optimization strategy(5) – reducing the hERG cardiac toxicity in drug development].

PubMed

Zhou, Sheng-bin; Wang, Jiang; Liu, Hong

2016-10-01

The potassium channel encoded by the human ether-a-go-go related gene(hERG) plays a very important role in the physiological and pathological processes in human. hERG potassium channel determines the outward currents which facilitate the repolarization of the myocardial cells. Some drugs were withdrawn from the market for the serious side effect of long QT interval and arrhythmia due to blockade of hERG channel. The strategies for lead compound optimization are to reduce inhibitory activity of hERG potassium channel and decrease cardiac toxicity. These methods include reduction of lipophilicity and basicity of amines, introduction of hydroxyl and acidic groups, and restricting conformation.

The hERG K+ channel: target and antitarget strategies in drug development.

PubMed

Raschi, Emanuel; Vasina, Valentina; Poluzzi, Elisabetta; De Ponti, Fabrizio

2008-03-01

The human ether-à-go-go related gene (hERG) K+ channel is of great interest for both basic researchers and clinicians because its blockade by drugs can lead to QT prolongation, which is a risk factor for torsades de pointes, a potentially life-threatening arrhythmia. A growing list of agents with "QT liability" have been withdrawn from the market or restricted in their use, whereas others did not even receive regulatory approval for this reason. Thus, hERG K+ channels have become a primary antitarget (i.e. an unwanted target) in drug development because their blockade causes potentially serious side effects. On the other hand, the recent identification and functional characterization of hERG K+ channels not only in the heart, but also in several other tissues (e.g. neurons, smooth muscle and cancer cells) may have far reaching implications for drug development for a possible exploitation of hERG as a target, especially in oncology and cardiology.

Endocytosis of hERG Is Clathrin-Independent and Involves Arf6

PubMed Central

Abuarab, Nada; Smith, Andrew J.; Hardy, Matthew E. L.; Elliott, David J. S.; Sivaprasadarao, Asipu

2013-01-01

The hERG potassium channel is critical for repolarisation of the cardiac action potential. Reduced expression of hERG at the plasma membrane, whether caused by hereditary mutations or drugs, results in long QT syndrome and increases the risk of ventricular arrhythmias. Thus, it is of fundamental importance to understand how the density of this channel at the plasma membrane is regulated. We used antibodies to an extracellular native or engineered epitope, in conjunction with immunofluorescence and ELISA, to investigate the mechanism of hERG endocytosis in recombinant cells and validated the findings in rat neonatal cardiac myocytes. The data reveal that this channel undergoes rapid internalisation, which is inhibited by neither dynasore, an inhibitor of dynamin, nor a dominant negative construct of Rab5a, into endosomes that are largely devoid of the transferrin receptor. These results support a clathrin-independent mechanism of endocytosis and exclude involvement of dynamin-dependent caveolin and RhoA mechanisms. In agreement, internalised hERG displayed marked overlap with glycosylphosphatidylinositol-anchored GFP, a clathrin-independent cargo. Endocytosis was significantly affected by cholesterol extraction with methyl-β-cyclodextrin and inhibition of Arf6 function with dominant negative Arf6-T27N-eGFP. Taken together, we conclude that hERG undergoes clathrin-independent endocytosis via a mechanism involving Arf6. PMID:24392021

Inhibitory Effects of Glycyrrhetinic Acid on the Delayed Rectifier Potassium Current in Guinea Pig Ventricular Myocytes and HERG Channel

PubMed Central

Wu, Delin; Jiang, Linqing; Wu, Hongjin; Wang, Shengqi; Zheng, Sidao; Yang, Jiyuan; Liu, Yuna; Ren, Jianxun; Chen, Xianbing

2013-01-01

Background. Licorice has long been used to treat many ailments including cardiovascular disorders in China. Recent studies have shown that the cardiac actions of licorice can be attributed to its active component, glycyrrhetinic acid (GA). However, the mechanism of action remains poorly understood. Aim. The effects of GA on the delayed rectifier potassium current (I K), the rapidly activating (I Kr) and slowly activating (I Ks) components of I K, and the HERG K+ channel expressed in HEK-293 cells were investigated. Materials and Methods. Single ventricular myocytes were isolated from guinea pig myocardium using enzymolysis. The wild type HERG gene was stably expressed in HEK293 cells. Whole-cell patch clamping was used to record I K (I Kr, I Ks) and the HERG K+ current. Results. GA (1, 5, and 10 μM) inhibited I K (I Kr, I Ks) and the HERG K+ current in a concentration-dependent manner. Conclusion. GA significantly inhibited the potassium currents in a dose- and voltage-dependent manner, suggesting that it exerts its antiarrhythmic action through the prolongation of APD and ERP owing to the inhibition of I K (I Kr, I Ks) and HERG K+ channel. PMID:24069049

Ivabradine prolongs phase 3 of cardiac repolarization and blocks the hERG1 (KCNH2) current over a concentration-range overlapping with that required to block HCN4.

PubMed

Lees-Miller, James P; Guo, Jiqing; Wang, Yibo; Perissinotti, Laura L; Noskov, Sergei Y; Duff, Henry J

2015-08-01

In Europe, ivabradine has recently been approved to treat patients with angina who have intolerance to beta blockers and/or heart failure. Ivabradine is considered to act specifically on the sinoatrial node by inhibiting the If current (the funny current) to slow automaticity. However, in vitro studies show that ivabradine prolongs phase 3 repolarization in ventricular tissue. No episodes of Torsades de Pointes have been reported in randomized clinical studies. The objective of this study is to assess whether ivabradine blocked the hERG1 current. In the present study we discovered that ivabradine prolongs action potential and blocks the hERG current over a range of concentrations overlapping with those required to block HCN4. Ivabradine produced tonic, rather than use-dependent block. The mutation Y652A significantly suppressed pharmacologic block of hERG by ivabradine. Disruption of C-type inactivation also suppressed block of hERG1 by ivabradine. Molecular docking and molecular dynamics simulations indicate that ivabradine may access the inner cavity of the hERG1 via a lipophilic route and has a well-defined binding site in the closed state of the channel. Structural organization of the binding pockets for ivabradine is discussed. Ivabradine blocks hERG and prolongs action potential duration. Our study is potentially important because it indicates the need for active post marketing surveillance of ivabradine. Importantly, proarrhythmia of a number of other drugs has only been discovered during post marketing surveillance. Copyright © 2015 Elsevier Ltd. All rights reserved.

External protons destabilize the activated voltage sensor in hERG channels.

PubMed

Shi, Yu Patrick; Cheng, Yen May; Van Slyke, Aaron C; Claydon, Tom W

2014-03-01

Extracellular acidosis shifts hERG channel activation to more depolarized potentials and accelerates channel deactivation; however, the mechanisms underlying these effects are unclear. External divalent cations, e.g., Ca(2+) and Cd(2+), mimic these effects and coordinate within a metal ion binding pocket composed of three acidic residues in hERG: D456 and D460 in S2 and D509 in S3. A common mechanism may underlie divalent cation and proton effects on hERG gating. Using two-electrode voltage clamp, we show proton sensitivity of hERG channel activation (pKa = 5.6), but not deactivation, was greatly reduced in the presence of Cd(2+) (0.1 mM), suggesting a common binding site for the Cd(2+) and proton effect on activation and separable effects of protons on activation and deactivation. Mutational analysis confirmed that D509 plays a critical role in the pH dependence of activation, as shown previously, and that cooperative actions involving D456 and D460 are also required. Importantly, neutralization of all three acidic residues abolished the proton-induced shift of activation, suggesting that the metal ion binding pocket alone accounts for the effects of protons on hERG channel activation. Voltage-clamp fluorimetry measurements demonstrated that protons shifted the voltage dependence of S4 movement to more depolarized potentials. The data indicate a site and mechanism of action for protons on hERG activation gating; protonation of D456, D460 and D509 disrupts interactions between these residues and S4 gating charges to destabilize the activated configuration of S4.

Blockade of HERG human K+ channels and IKr of guinea-pig cardiomyocytes by the antipsychotic drug clozapine.

PubMed

Lee, So-Young; Kim, Young-Jin; Kim, Kyong-Tai; Choe, Han; Jo, Su-Hyun

2006-06-01

Clozapine, a commonly used antipsychotic drug, can induce QT prolongation, which may lead to torsades de pointes and sudden death. To investigate the arrhythmogenic side effects of clozapine, we studied the impact of clozapine on human ether-a-go-go-related gene (HERG) channels expressed in Xenopus oocytes and HEK293 cells, and on the delayed rectifier K(+) currents of guinea-pig cardiomyocytes. Clozapine dose-dependently decreased the amplitudes of the currents at the end of voltage steps, and the tail currents of HERG. The IC(50) for the clozapine blockade of HERG currents in Xenopus oocytes progressively decreased relative to depolarization (39.9 microM at -40 mV, 28.3 microM at 0 mV and 22.9 microM at +40 mV), whereas the IC(50) for the clozapine-induced blockade of HERG currents in HEK293 cells at 36 degrees C was 2.5 microM at +20 mV. The clozapine-induced blockade of HERG currents was time dependent: the fractional current was 0.903 of the control at the beginning of the pulse, but declined to 0.412 after 4 s at a test potential of 0 mV. The clozapine-induced blockade of HERG currents was use-dependent, exhibiting more rapid onset and greater steady state blockade at higher frequencies of activation, with a partial relief of blockade observed when the frequency of activation was decreased. In guinea-pig ventricular myocytes held at 36 degrees C, treatment with 1 and 5 microM clozapine blocked the rapidly activating delayed rectifier K(+) current (I(Kr)) by 24.7 and 79.6%, respectively, but did not significantly block the slowly activating delayed rectifier K(+) current (I(Ks)). Our findings collectively suggest that blockade of HERG currents and I(Kr), but not I(Ks), may contribute to the arrhythmogenic side effects of clozapine.

In Vitro Cardiovascular Effects of Dihydroartemisin-Piperaquine Combination Compared with Other Antimalarials

PubMed Central

Crumb, William; Pace, Silvia; Ubben, David; Wible, Barb; Yan, Gan-Xin; Funck-Brentano, Christian

2012-01-01

The in vitro cardiac properties of dihydroartemisinin (DHA) plus piperaquine phosphate (PQP) were compared with those of other antimalarial compounds. Results with antimalarial drugs, chosen on the basis of their free therapeutic maximum concentration in plasma (Cmax), were expressed as the fold of that particular effect with respect to their Cmax. The following tests were used at 37°C: hERG (human ether-à-go-go-related gene) blockade and trafficking, rabbit heart ventricular preparations, and sodium and slow potassium ion current interference (INa and IKs, respectively). Chloroquine, halofantrine, mefloquine, and lumefantrine were tested in the hERG studies, but only chloroquine, dofetilide, lumefantrine, and the combination of artemether-lumefantrine were used in the rabbit heart ventricular preparations, hERG trafficking studies, and INa and IKs analyses. A proper reference was used in each test. In hERG studies, the high 50% inhibitory concentration (IC50) of halofantrine, which was lower than its Cmax, was confirmed. All the other compounds blocked hERG, with IC50s ranging from 3- to 30-fold their Cmaxs. In hERG trafficking studies, the facilitative effects of chloroquine at about 30-fold its Cmax were confirmed and DHA blocked it at a concentration about 300-fold its Cmax. In rabbit heart ventricular preparations, dofetilide, used as a positive control, revealed a high risk of torsades de pointes, whereas chloroquine showed a medium risk. Neither DHA-PQP nor artemether-lumefantrine displayed an in vitro signal for a significant proarrhythmic risk. Only chloroquine blocked the INa ion current and did so at about 30-fold its Cmax. No effect on IKs was detected. In conclusion, despite significant hERG blockade, DHA-PQP and artemether-lumefantrine do not appear to induce potential torsadogenic effects in vitro, affect hERG trafficking, or block sodium and slow potassium ion currents. PMID:22391528

Blockade of HERG human K+ channels and IKr of guinea-pig cardiomyocytes by the antipsychotic drug clozapine

PubMed Central

Lee, So-Young; Kim, Young-Jin; Kim, Kyong-Tai; Choe, Han; Jo, Su-Hyun

2006-01-01

Clozapine, a commonly used antipsychotic drug, can induce QT prolongation, which may lead to torsades de pointes and sudden death. To investigate the arrhythmogenic side effects of clozapine, we studied the impact of clozapine on human ether-a-go-go-related gene (HERG) channels expressed in Xenopus oocytes and HEK293 cells, and on the delayed rectifier K+ currents of guinea-pig cardiomyocytes. Clozapine dose-dependently decreased the amplitudes of the currents at the end of voltage steps, and the tail currents of HERG. The IC50 for the clozapine blockade of HERG currents in Xenopus oocytes progressively decreased relative to depolarization (39.9 μM at −40 mV, 28.3 μM at 0 mV and 22.9 μM at +40 mV), whereas the IC50 for the clozapine-induced blockade of HERG currents in HEK293 cells at 36°C was 2.5 μM at +20 mV. The clozapine-induced blockade of HERG currents was time dependent: the fractional current was 0.903 of the control at the beginning of the pulse, but declined to 0.412 after 4 s at a test potential of 0 mV. The clozapine-induced blockade of HERG currents was use-dependent, exhibiting more rapid onset and greater steady state blockade at higher frequencies of activation, with a partial relief of blockade observed when the frequency of activation was decreased. In guinea-pig ventricular myocytes held at 36°C, treatment with 1 and 5 μM clozapine blocked the rapidly activating delayed rectifier K+ current (IKr) by 24.7 and 79.6%, respectively, but did not significantly block the slowly activating delayed rectifier K+ current (IKs). Our findings collectively suggest that blockade of HERG currents and IKr, but not IKs, may contribute to the arrhythmogenic side effects of clozapine. PMID:16633353

Mechanistic Insight into Human ether-à-go-go-related Gene (hERG) K+ Channel Deactivation Gating from the Solution Structure of the EAG Domain

PubMed Central

Muskett, Frederick W.; Thouta, Samrat; Thomson, Steven J.; Bowen, Alexander; Stansfeld, Phillip J.; Mitcheson, John S.

2011-01-01

Human ether-à-go-go-related gene (hERG) K+ channels have a critical role in cardiac repolarization. hERG channels close (deactivate) very slowly, and this is vital for regulating the time course and amplitude of repolarizing current during the cardiac action potential. Accelerated deactivation is one mechanism by which inherited mutations cause long QT syndrome and potentially lethal arrhythmias. hERG deactivation is highly dependent upon an intact EAG domain (the first 135 amino acids of the N terminus). Importantly, deletion of residues 2–26 accelerates deactivation to a similar extent as removing the entire EAG domain. These and other experiments suggest the first 26 residues (NT1–26) contain structural elements required to slow deactivation by stabilizing the open conformation of the pore. Residues 26–135 form a Per-Arnt-Sim domain, but a structure for NT1–26 has not been forthcoming, and little is known about its site of interaction on the channel. In this study, we present an NMR structure for the entire EAG domain, which reveals that NT1–26 is structurally independent from the Per-Arnt-Sim domain and contains a stable amphipathic helix with one face being positively charged. Mutagenesis and electrophysiological studies indicate that neutralizing basic residues and breaking the amphipathic helix dramatically accelerate deactivation. Furthermore, scanning mutagenesis and molecular modeling studies of the cyclic nucleotide binding domain suggest that negatively charged patches on its cytoplasmic surface form an interface with the NT1–26 domain. We propose a model in which NT1–26 obstructs gating motions of the cyclic nucleotide binding domain to allosterically stabilize the open conformation of the pore. PMID:21135103

Validation and Clinical Utility of the hERG IC50:Cmax Ratio to Determine the Risk of Drug-Induced Torsades de Pointes: A Meta-Analysis.

PubMed

Lehmann, David F; Eggleston, William D; Wang, Dongliang

2018-03-01

Use of the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG) to predict torsades de pointes (TdP) risk from culprit drugs is neither sensitive nor specific. The ratio of the half-maximum inhibitory concentration of the hERG channel (hERG IC50) to the peak serum concentration of unbound drug (C max ) is used during drug development to screen out chemical entities likely to cause TdP. To validate the use of the hERG IC50:C max ratio to predict TdP risk from a culprit drug by its correlation with TdP incidence. Medline (between 1966 and March 2017) was accessed for hERG IC50 and C max values from the antihistamine, fluoroquinolone, and antipsychotic classes to identify cases of drug-induced TdP. Exposure to a culprit drug was estimated from annual revenues reported by the manufacturer. Inclusion criteria for TdP cases were provision of an ECG tracing that demonstrated QTc prolongation with TdP and normal serum values of potassium, calcium, and magnesium. Cases reported in patients with a prior rhythm disturbance and those involving a drug interaction were excluded. The Meta-Analysis of Observational Studies in Epidemiology checklist was used for epidemiological data extraction by two authors. Negligible risk drugs were defined by an hERG IC50:C max ratio that correlated with less than a 5% chance of one TdP event for every 100 million exposures (relative risk [RR] 1.0). The hERG IC50:C max ratio correlated with TdP risk (0.312; 95% confidence interval 0.205-0.476, p<0.0001), a ratio of 80 (RR 1.0). The RR from olanzapine is on par with loratadine; ziprasidone is comparable with ciprofloxacin. Drugs with an RR greater than 50 include astemizole, risperidone, haloperidol, and thioridazine. The hERG IC50:C max ratio was correlated with TdP incidence for culprit drugs. This validation provides support for the potential use of the hERG IC50:C max ratio for clinical decision making in instances of drug selection where TdP risk is a concern. © 2018 Pharmacotherapy Publications, Inc.

Sphingosine 1-phosphate and human ether-a'-go-go-related gene potassium channels modulate migration in human anaplastic thyroid cancer cells.

PubMed

Asghar, Muhammad Yasir; Viitanen, Tero; Kemppainen, Kati; Törnquist, Kid

2012-10-01

Anaplastic thyroid cancer (ATC) is the most aggressive form of human thyroid cancer, lacking any effective treatment. Sphingosine 1-phosphate (S1P) receptors and human ether-a'-go-go-related gene (HERG (KCNH2)) potassium channels are important modulators of cell migration. In this study, we have shown that the S1P(1-3) receptors are expressed in C643 and THJ-16T human ATC cell lines, both at mRNA and protein level. S1P inhibited migration of these cells and of follicular FTC-133 thyroid cancer cells. Using the S1P(1,3) inhibitor VPC-23019, the S1P(2) inhibitor JTE-013, and the S1P(2) receptor siRNA, we showed that the effect was mediated through S1P(2). Treatment of the cells with the Rho inhibitor C3 transferase abolished the effect of S1P on migration. S1P attenuated Rac activity, and inhibiting Rac decreased migration. Sphingosine kinase inhibitor enhanced basal migration of cells, and addition of exogenous S1P inhibited migration. C643 cells expressed a nonconducting HERG protein, and S1P decreased HERG protein expression. The HERG blocker E-4031 decreased migration. Interestingly, downregulating HERG protein with siRNA decreased the basal migration. In experiments using HEK cells overexpressing HERG, we showed that S1P decreased channel protein expression and current and that S1P attenuated migration of the cells. We conclude that S1P attenuates migration of C643 ATC cells by activating S1P(2) and the Rho pathway. The attenuated migration is also, in part, dependent on a S1P-induced decrease of HERG protein.

Validation of visualized transgenic zebrafish as a high throughput model to assay bradycardia related cardio toxicity risk candidates.

PubMed

Wen, Dingsheng; Liu, Aiming; Chen, Feng; Yang, Julin; Dai, Renke

2012-10-01

Drug-induced QT prolongation usually leads to torsade de pointes (TdP), thus for drugs in the early phase of development this risk should be evaluated. In the present study, we demonstrated a visualized transgenic zebrafish as an in vivo high-throughput model to assay the risk of drug-induced QT prolongation. Zebrafish larvae 48 h post-fertilization expressing green fluorescent protein in myocardium were incubated with compounds reported to induce QT prolongation or block the human ether-a-go-go-related gene (hERG) K⁺ current. The compounds sotalol, indapaminde, erythromycin, ofoxacin, levofloxacin, sparfloxacin and roxithromycin were additionally administrated by microinjection into the larvae yolk sac. The ventricle heart rate was recorded using the automatic monitoring system after incubation or microinjection. As a result, 14 out of 16 compounds inducing dog QT prolongation caused bradycardia in zebrafish. A similar result was observed with 21 out of 26 compounds which block hERG current. Among the 30 compounds which induced human QT prolongation, 25 caused bradycardia in this model. Thus, the risk of compounds causing bradycardia in this transgenic zebrafish correlated with that causing QT prolongation and hERG K⁺ current blockage in established models. The tendency that high logP values lead to high risk of QT prolongation in this model was indicated, and non-sensitivity of this model to antibacterial agents was revealed. These data suggest application of this transgenic zebrafish as a high-throughput model to screen QT prolongation-related cardio toxicity of the drug candidates. Copyright © 2012 John Wiley & Sons, Ltd.

1,2,3,4-tetrahydroquinoline-based selective human neuronal nitric oxide synthase (nNOS) inhibitors: lead optimization studies resulting in the identification of N-(1-(2-(methylamino)ethyl)-1,2,3,4-tetrahydroquinolin-6-yl)thiophene-2-carboximidamide as a preclinical development candidate.

PubMed

Ramnauth, Jailall; Renton, Paul; Dove, Peter; Annedi, Subhash C; Speed, Joanne; Silverman, Sarah; Mladenova, Gabriela; Maddaford, Shawn P; Zinghini, Salvatore; Rakhit, Suman; Andrews, John; Lee, David K H; Zhang, Dongqin; Porreca, Frank

2012-03-22

Numerous studies have shown that selective nNOS inhibitors could be therapeutic in many neurological disorders. Previously, we reported a series of 1,2,3,4-tetrahydroquinoline-based potent and selective nNOS inhibitors, highlighted by 1 ( J. Med. Chem. 2011 , 54 , 5562 - 5575 ). Despite showing activity in two rodent pain models, 1 suffered from low oral bioavailability (18%) and moderate hERG channel inhibition (IC(50) = 4.7 μM). To optimize the properties of 1, we synthesized a small focused library containing various alkylamino groups on the 1-position of the 1,2,3,4-tetrahydroquinoline scaffold. The compounds were triaged based on their activity in the NOS and hERG manual patch clamp assays and their calculated physicochemical parameters. From these studies, we identified 47 as a potent and selective nNOS inhibitor with improved oral bioavailability (60%) and no hERG channel inhibition (IC(50) > 30 μM). Furthermore, 47 was efficacious in the Chung model of neuropathic pain and has an excellent safety profile, making it a promising preclinical development candidate.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koenig, Xaver; Kovar, Michael; Rubi, Lena

The plant alkaloid ibogaine has promising anti-addictive properties. Albeit not licenced as a therapeutic drug, and despite hints that ibogaine may perturb the heart rhythm, this alkaloid is used to treat drug addicts. We have recently reported that ibogaine inhibits human ERG (hERG) potassium channels at concentrations similar to the drugs affinity for several of its known brain targets. Thereby the drug may disturb the heart's electrophysiology. Here, to assess the drug's cardiac ion channel profile in more detail, we studied the effects of ibogaine and its congener 18-Methoxycoronaridine (18-MC) on various cardiac voltage-gated ion channels. We confirmed that heterologouslymore » expressed hERG currents are reduced by ibogaine in low micromolar concentrations. Moreover, at higher concentrations, the drug also reduced human Na{sub v}1.5 sodium and Ca{sub v}1.2 calcium currents. Ion currents were as well reduced by 18-MC, yet with diminished potency. Unexpectedly, although blocking hERG channels, ibogaine did not prolong the action potential (AP) in guinea pig cardiomyocytes at low micromolar concentrations. Higher concentrations (≥ 10 μM) even shortened the AP. These findings can be explained by the drug's calcium channel inhibition, which counteracts the AP-prolonging effect generated by hERG blockade. Implementation of ibogaine's inhibitory effects on human ion channels in a computer model of a ventricular cardiomyocyte, on the other hand, suggested that ibogaine does prolong the AP in the human heart. We conclude that therapeutic concentrations of ibogaine have the propensity to prolong the QT interval of the electrocardiogram in humans. In some cases this may lead to cardiac arrhythmias. - Highlights: • We study effects of anti-addiction drug ibogaine on ionic currents in cardiomyocytes. • We assess the cardiac ion channel profile of ibogaine. • Ibogaine inhibits hERG potassium, sodium and calcium channels. • Ibogaine’s effects on ion channels are a potential source of cardiac arrhythmias. • 18-Methoxycoronaridine has a lower affinity for cardiac ion channels than ibogaine.« less

Blockade of HERG human K+ channel and IKr of guinea pig cardiomyocytes by prochlorperazine.

PubMed

Kim, Moon-Doo; Eun, Su-Yong; Jo, Su-Hyun

2006-08-21

Prochlorperazine, a drug for the symptomatic control of nausea, vomiting and psychiatric disorders, can induce prolonged QT, torsades de pointes and sudden death. We studied the effects of prochlorperazine on human ether-a-go-go-related gene (HERG) channels expressed in Xenopus oocytes and also in the delayed rectifier K+ current of guinea pig cardiomyocytes. Prochlorperazine induced a concentration-dependent decrease in current amplitudes at the end of the voltage steps and tail currents of HERG. The IC50 for a prochlorperazine block of HERG current in Xenopus oocytes progressively decreased relative to the degree of depolarization, from 42.1 microM at -40 mV to 37.4 microM at 0 mV to 22.6 microM at +40 mV. The block of HERG by prochlorperazine was use-dependent, exhibiting a more rapid onset and a greater steady-state block at higher frequencies of activation, while there was partial relief of the block with reduced frequencies. In guinea pig ventricular myocytes, bath applications of 0.5 and 1 muM prochlorperazine at 36 degrees C blocked rapidly activating delayed rectifier K+ current by 38.9% and 76.5%, respectively, but did not significantly block slowly activating delayed rectifier K+ current. Our findings suggest that the arrhythmogenic side effects of prochlorperazine are caused by a blockade of HERG and the rapid component of the delayed rectifier K+ current rather than by a blockade of the slow component.

Voltage-sensing domain mode shift is coupled to the activation gate by the N-terminal tail of hERG channels.

PubMed

Tan, Peter S; Perry, Matthew D; Ng, Chai Ann; Vandenberg, Jamie I; Hill, Adam P

2012-09-01

Human ether-a-go-go-related gene (hERG) potassium channels exhibit unique gating kinetics characterized by unusually slow activation and deactivation. The N terminus of the channel, which contains an amphipathic helix and an unstructured tail, has been shown to be involved in regulation of this slow deactivation. However, the mechanism of how this occurs and the connection between voltage-sensing domain (VSD) return and closing of the gate are unclear. To examine this relationship, we have used voltage-clamp fluorometry to simultaneously measure VSD motion and gate closure in N-terminally truncated constructs. We report that mode shifting of the hERG VSD results in a corresponding shift in the voltage-dependent equilibrium of channel closing and that at negative potentials, coupling of the mode-shifted VSD to the gate defines the rate of channel closure. Deletion of the first 25 aa from the N terminus of hERG does not alter mode shifting of the VSD but uncouples the shift from closure of the cytoplasmic gate. Based on these observations, we propose the N-terminal tail as an adaptor that couples voltage sensor return to gate closure to define slow deactivation gating in hERG channels. Furthermore, because the mode shift occurs on a time scale relevant to the cardiac action potential, we suggest a physiological role for this phenomenon in maximizing current flow through hERG channels during repolarization.

Inactivation gating determines nicotine blockade of human HERG channels.

PubMed

Wang, H Z; Shi, H; Liao, S J; Wang, Z

1999-09-01

We have previously found that nicotine blocked multiple K+ currents, including the rapid component of delayed rectifier K+ currents (IKr), by interacting directly with the channels. To shed some light on the mechanisms of interaction between nicotine and channels, we performed detailed analysis on the human ether-à-go-go-related gene (HERG) channels, which are believed to be equivalent to the native I(Kr) when expressed in Xenopus oocytes. Nicotine suppressed the HERG channels in a concentration-dependent manner with greater potency with voltage protocols, which favor channel inactivation. Nicotine caused dramatic shifts of the voltage-dependent inactivation curve to more negative potentials and accelerated the inactivation process. Conversely, maneuvers that weakened the channel inactivation gating considerably relieved the blockade. Elevating the extracellular K+ concentration from 5 to 20 mM increased the nicotine concentration (by approximately 100-fold) needed to achieve the same degree of inhibition. Moreover, nicotine lost its ability to block the HERG channels when a single mutation was introduced to a residue located after transmembrane domain 6 (S631A) to remove the rapid channel inactivation. Our data suggest that the inactivation gating determines nicotine blockade of the HERG channels.

Rehabilitating drug-induced long-QT promoters: In-silico design of hERG-neutral cisapride analogues with retained pharmacological activity

PubMed Central

2014-01-01

Background The human ether-a-go-go related gene 1 (hERG1), which codes for a potassium ion channel, is a key element in the cardiac delayed rectified potassium current, IKr, and plays an important role in the normal repolarization of the heart’s action potential. Many approved drugs have been withdrawn from the market due to their prolongation of the QT interval. Most of these drugs have high potencies for their principal targets and are often irreplaceable, thus “rehabilitation” studies for decreasing their high hERG1 blocking affinities, while keeping them active at the binding sites of their targets, have been proposed to enable these drugs to re-enter the market. Methods In this proof-of-principle study, we focus on cisapride, a gastroprokinetic agent withdrawn from the market due to its high hERG1 blocking affinity. Here we tested an a priori strategy to predict a compound’s cardiotoxicity using de novo drug design with molecular docking and Molecular Dynamics (MD) simulations to generate a strategy for the rehabilitation of cisapride. Results We focused on two key receptors, a target interaction with the (adenosine) receptor and an off-target interaction with hERG1 channels. An analysis of the fragment interactions of cisapride at human A2A adenosine receptors and hERG1 central cavities helped us to identify the key chemical groups responsible for the drug activity and hERG1 blockade. A set of cisapride derivatives with reduced cardiotoxicity was then proposed using an in-silico two-tier approach. This set was compared against a large dataset of commercially available cisapride analogs and derivatives. Conclusions An interaction decomposition of cisapride and cisapride derivatives allowed for the identification of key active scaffolds and functional groups that may be responsible for the unwanted blockade of hERG1. PMID:24606761

Rehabilitating drug-induced long-QT promoters: in-silico design of hERG-neutral cisapride analogues with retained pharmacological activity.

PubMed

Durdagi, Serdar; Randall, Trevor; Duff, Henry J; Chamberlin, Adam; Noskov, Sergei Y

2014-03-08

The human ether-a-go-go related gene 1 (hERG1), which codes for a potassium ion channel, is a key element in the cardiac delayed rectified potassium current, IKr, and plays an important role in the normal repolarization of the heart's action potential. Many approved drugs have been withdrawn from the market due to their prolongation of the QT interval. Most of these drugs have high potencies for their principal targets and are often irreplaceable, thus "rehabilitation" studies for decreasing their high hERG1 blocking affinities, while keeping them active at the binding sites of their targets, have been proposed to enable these drugs to re-enter the market. In this proof-of-principle study, we focus on cisapride, a gastroprokinetic agent withdrawn from the market due to its high hERG1 blocking affinity. Here we tested an a priori strategy to predict a compound's cardiotoxicity using de novo drug design with molecular docking and Molecular Dynamics (MD) simulations to generate a strategy for the rehabilitation of cisapride. We focused on two key receptors, a target interaction with the (adenosine) receptor and an off-target interaction with hERG1 channels. An analysis of the fragment interactions of cisapride at human A2A adenosine receptors and hERG1 central cavities helped us to identify the key chemical groups responsible for the drug activity and hERG1 blockade. A set of cisapride derivatives with reduced cardiotoxicity was then proposed using an in-silico two-tier approach. This set was compared against a large dataset of commercially available cisapride analogs and derivatives. An interaction decomposition of cisapride and cisapride derivatives allowed for the identification of key active scaffolds and functional groups that may be responsible for the unwanted blockade of hERG1.

Strategies to reduce the risk of drug-induced QT interval prolongation: a pharmaceutical company perspective.

PubMed

Pollard, C E; Valentin, J-P; Hammond, T G

2008-08-01

Drug-induced prolongation of the QT interval is having a significant impact on the ability of the pharmaceutical industry to develop new drugs. The development implications for a compound causing a significant effect in the 'Thorough QT/QTc Study' -- as defined in the clinical regulatory guidance (ICH E14) -- are substantial. In view of this, and the fact that QT interval prolongation is linked to direct inhibition of the hERG channel, in the early stages of drug discovery the focus is on testing for and screening out hERG activity. This has led to understanding of how to produce low potency hERG blockers whilst retaining desirable properties. Despite this, a number of factors mean that when an integrated risk assessment is generated towards the end of the discovery phase (by conducting at least an in vivo QT assessment) a QT interval prolongation risk is still often apparent; inhibition of hERG channel trafficking and partitioning into cardiac tissue are just two confounding factors. However, emerging information suggests that hERG safety margins have high predictive value and that when hERG and in vivo non-clinical data are combined, their predictive value to man, whilst not perfect, is >80%. Although understanding the anomalies is important and is being addressed, of greater importance is developing a better understanding of TdP, with the aim of being able to predict TdP rather than using an imperfect surrogate marker (QT interval prolongation). Without an understanding of how to predict TdP risk, high-benefit drugs for serious indications may never be marketed.

Structure activity relationship of C-2 ether substituted 1,5-naphthyridine analogs of oxabicyclooctane-linked novel bacterial topoisomerase inhibitors as broad-spectrum antibacterial agents (Part-5).

PubMed

Singh, Sheo B; Kaelin, David E; Meinke, Peter T; Wu, Jin; Miesel, Lynn; Tan, Christopher M; Olsen, David B; Lagrutta, Armando; Fukuda, Hideyuki; Kishii, Ryuta; Takei, Masaya; Takeuchi, Tomoko; Takano, Hisashi; Ohata, Kohei; Kurasaki, Haruaki; Nishimura, Akinori; Shibata, Takeshi; Fukuda, Yasumichi

2015-09-01

Oxabicyclooctane linked novel bacterial topoisomerase inhibitors (NBTIs) are new class of recently reported broad-spectrum antibacterial agents. They target bacterial DNA gyrase and topoisomerase IV and bind to a site different than quinolones. They show no cross-resistance to known antibiotics and provide opportunity to combat drug-resistant bacteria. A structure activity relationship of the C-2 substituted ether analogs of 1,5-naphthyridine oxabicyclooctane-linked NBTIs are described. Synthesis and antibacterial activities of a total of 63 analogs have been summarized representing alkyl, cyclo alkyl, fluoro alkyl, hydroxy alkyl, amino alkyl, and carboxyl alkyl ethers. All compounds were tested against three key strains each of Gram-positive and Gram-negative bacteria as well as for hERG binding activities. Many key compounds were also tested for the functional hERG activity. Six compounds were evaluated for efficacy in a murine bacteremia model of Staphylococcus aureus infection. Significant tolerance for the ether substitution (including polar groups such as amino and carboxyl) at C-2 was observed for S. aureus activity however the same was not true for Enterococcus faecium and Gram-negative strains. Reduced clogD generally showed reduced hERG activity and improved in vivo efficacy but was generally associated with decreased overall potency. One of the best compounds was hydroxy propyl ether (16), which mainly retained the potency, spectrum and in vivo efficacy of AM8085 associated with the decreased hERG activity and improved physical property. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evolving regulatory paradigm for proarrhythmic risk assessment for new drugs.

PubMed

Vicente, Jose; Stockbridge, Norman; Strauss, David G

Fourteen drugs were removed from the market worldwide because their potential to cause torsade de pointes (torsade), a potentially fatal ventricular arrhythmia. The observation that most drugs that cause torsade block the potassium channel encoded by the human ether-à-go-go related gene (hERG) and prolong the heart rate corrected QT interval (QTc) on the ECG, led to a focus on screening new drugs for their potential to block the hERG potassium channel and prolong QTc. This has been a successful strategy keeping torsadogenic drugs off the market, but has resulted in drugs being dropped from development, sometimes inappropriately. This is because not all drugs that block the hERG potassium channel and prolong QTc cause torsade, sometimes because they block other channels. The regulatory paradigm is evolving to improve proarrhythmic risk prediction. ECG studies can now use exposure-response modeling for assessing the effect of a drug on the QTc in small sample size first-in-human studies. Furthermore, the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative is developing and validating a new in vitro paradigm for cardiac safety evaluation of new drugs that provides a more accurate and comprehensive mechanistic-based assessment of proarrhythmic potential. Under CiPA, the prediction of proarrhythmic potential will come from in vitro ion channel assessments coupled with an in silico model of the human ventricular myocyte. The preclinical assessment will be checked with an assessment of human phase 1 ECG data to determine if there are unexpected ion channel effects in humans compared to preclinical ion channel data. While there is ongoing validation work, the heart rate corrected J-T peak interval is likely to be assessed under CiPA to detect inward current block in presence of hERG potassium channel block. Copyright © 2016 Elsevier Inc. All rights reserved.

Integrated analysis of drug-induced gene expression profiles predicts novel hERG inhibitors.

PubMed

Babcock, Joseph J; Du, Fang; Xu, Kaiping; Wheelan, Sarah J; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors

PubMed Central

Babcock, Joseph J.; Du, Fang; Xu, Kaiping; Wheelan, Sarah J.; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays. PMID:23936032

Components of gating charge movement and S4 voltage-sensor exposure during activation of hERG channels.

PubMed

Wang, Zhuren; Dou, Ying; Goodchild, Samuel J; Es-Salah-Lamoureux, Zeineb; Fedida, David

2013-04-01

The human ether-á-go-go-related gene (hERG) K(+) channel encodes the pore-forming α subunit of the rapid delayed rectifier current, IKr, and has unique activation gating kinetics, in that the α subunit of the channel activates and deactivates very slowly, which focuses the role of IKr current to a critical period during action potential repolarization in the heart. Despite its physiological importance, fundamental mechanistic properties of hERG channel activation gating remain unclear, including how voltage-sensor movement rate limits pore opening. Here, we study this directly by recording voltage-sensor domain currents in mammalian cells for the first time and measuring the rates of voltage-sensor modification by [2-(trimethylammonium)ethyl] methanethiosulfonate chloride (MTSET). Gating currents recorded from hERG channels expressed in mammalian tsA201 cells using low resistance pipettes show two charge systems, defined as Q(1) and Q(2), with V(1/2)'s of -55.7 (equivalent charge, z = 1.60) and -54.2 mV (z = 1.30), respectively, with the Q(2) charge system carrying approximately two thirds of the overall gating charge. The time constants for charge movement at 0 mV were 2.5 and 36.2 ms for Q(1) and Q(2), decreasing to 4.3 ms for Q(2) at +60 mV, an order of magnitude faster than the time constants of ionic current appearance at these potentials. The voltage and time dependence of Q2 movement closely correlated with the rate of MTSET modification of I521C in the outermost region of the S4 segment, which had a V(1/2) of -64 mV and time constants of 36 ± 8.5 ms and 11.6 ± 6.3 ms at 0 and +60 mV, respectively. Modeling of Q(1) and Q(2) charge systems showed that a minimal scheme of three transitions is sufficient to account for the experimental findings. These data point to activation steps further downstream of voltage-sensor movement that provide the major delays to pore opening in hERG channels.

Anti-addiction drug ibogaine inhibits voltage-gated ionic currents: A study to assess the drug's cardiac ion channel profile☆

PubMed Central

Koenig, Xaver; Kovar, Michael; Rubi, Lena; Mike, Agnes K.; Lukacs, Peter; Gawali, Vaibhavkumar S.; Todt, Hannes; Hilber, Karlheinz; Sandtner, Walter

2013-01-01

The plant alkaloid ibogaine has promising anti-addictive properties. Albeit not licenced as a therapeutic drug, and despite hints that ibogaine may perturb the heart rhythm, this alkaloid is used to treat drug addicts. We have recently reported that ibogaine inhibits human ERG (hERG) potassium channels at concentrations similar to the drugs affinity for several of its known brain targets. Thereby the drug may disturb the heart's electrophysiology. Here, to assess the drug's cardiac ion channel profile in more detail, we studied the effects of ibogaine and its congener 18-Methoxycoronaridine (18-MC) on various cardiac voltage-gated ion channels. We confirmed that heterologously expressed hERG currents are reduced by ibogaine in low micromolar concentrations. Moreover, at higher concentrations, the drug also reduced human Nav1.5 sodium and Cav1.2 calcium currents. Ion currents were as well reduced by 18-MC, yet with diminished potency. Unexpectedly, although blocking hERG channels, ibogaine did not prolong the action potential (AP) in guinea pig cardiomyocytes at low micromolar concentrations. Higher concentrations (≥ 10 μM) even shortened the AP. These findings can be explained by the drug's calcium channel inhibition, which counteracts the AP-prolonging effect generated by hERG blockade. Implementation of ibogaine's inhibitory effects on human ion channels in a computer model of a ventricular cardiomyocyte, on the other hand, suggested that ibogaine does prolong the AP in the human heart. We conclude that therapeutic concentrations of ibogaine have the propensity to prolong the QT interval of the electrocardiogram in humans. In some cases this may lead to cardiac arrhythmias. PMID:23707769

Anti-addiction drug ibogaine inhibits voltage-gated ionic currents: a study to assess the drug's cardiac ion channel profile.

PubMed

Koenig, Xaver; Kovar, Michael; Rubi, Lena; Mike, Agnes K; Lukacs, Peter; Gawali, Vaibhavkumar S; Todt, Hannes; Hilber, Karlheinz; Sandtner, Walter

2013-12-01

The plant alkaloid ibogaine has promising anti-addictive properties. Albeit not licensed as a therapeutic drug, and despite hints that ibogaine may perturb the heart rhythm, this alkaloid is used to treat drug addicts. We have recently reported that ibogaine inhibits human ERG (hERG) potassium channels at concentrations similar to the drugs affinity for several of its known brain targets. Thereby the drug may disturb the heart's electrophysiology. Here, to assess the drug's cardiac ion channel profile in more detail, we studied the effects of ibogaine and its congener 18-Methoxycoronaridine (18-MC) on various cardiac voltage-gated ion channels. We confirmed that heterologously expressed hERG currents are reduced by ibogaine in low micromolar concentrations. Moreover, at higher concentrations, the drug also reduced human Nav1.5 sodium and Cav1.2 calcium currents. Ion currents were as well reduced by 18-MC, yet with diminished potency. Unexpectedly, although blocking hERG channels, ibogaine did not prolong the action potential (AP) in guinea pig cardiomyocytes at low micromolar concentrations. Higher concentrations (≥ 10 μM) even shortened the AP. These findings can be explained by the drug's calcium channel inhibition, which counteracts the AP-prolonging effect generated by hERG blockade. Implementation of ibogaine's inhibitory effects on human ion channels in a computer model of a ventricular cardiomyocyte, on the other hand, suggested that ibogaine does prolong the AP in the human heart. We conclude that therapeutic concentrations of ibogaine have the propensity to prolong the QT interval of the electrocardiogram in humans. In some cases this may lead to cardiac arrhythmias. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Proline Scan of the hERG Channel S6 Helix Reveals the Location of the Intracellular Pore Gate

PubMed Central

Thouta, Samrat; Sokolov, Stanislav; Abe, Yuki; Clark, Sheldon J.; Cheng, Yen M.; Claydon, Tom W.

2014-01-01

In Shaker-like channels, the activation gate is formed at the bundle crossing by the convergence of the inner S6 helices near a conserved proline-valine-proline motif, which introduces a kink that allows for electromechanical coupling with voltage sensor motions via the S4-S5 linker. Human ether-a-go-go-related gene (hERG) channels lack the proline-valine-proline motif and the location of the intracellular pore gate and how it is coupled to S4 movement is less clear. Here, we show that proline substitutions within the S6 of hERG perturbed pore gate closure, trapping channels in the open state. Performing a proline scan of the inner S6 helix, from Ile655 to Tyr667 revealed that gate perturbation occurred with proximal (I655P-Q664P), but not distal (R665P-Y667P) substitutions, suggesting that Gln664 marks the position of the intracellular gate in hERG channels. Using voltage-clamp fluorimetry and gating current analysis, we demonstrate that proline substitutions trap the activation gate open by disrupting the coupling between the voltage-sensing unit and the pore of the channel. We characterize voltage sensor movement in one such trapped-open mutant channel and demonstrate the kinetics of what we interpret to be intrinsic hERG voltage sensor movement. PMID:24606930

Discovery of Selective Phosphodiesterase 1 Inhibitors with Memory Enhancing Properties.

PubMed

Dyck, Brian; Branstetter, Bryan; Gharbaoui, Tawfik; Hudson, Andrew R; Breitenbucher, J Guy; Gomez, Laurent; Botrous, Iriny; Marrone, Tami; Barido, Richard; Allerston, Charles K; Cedervall, E Peder; Xu, Rui; Sridhar, Vandana; Barker, Ryan; Aertgeerts, Kathleen; Schmelzer, Kara; Neul, David; Lee, Dong; Massari, Mark Eben; Andersen, Carsten B; Sebring, Kristen; Zhou, Xianbo; Petroski, Robert; Limberis, James; Augustin, Martin; Chun, Lawrence E; Edwards, Thomas E; Peters, Marco; Tabatabaei, Ali

2017-04-27

A series of potent thienotriazolopyrimidinone-based PDE1 inhibitors was discovered. X-ray crystal structures of example compounds from this series in complex with the catalytic domain of PDE1B and PDE10A were determined, allowing optimization of PDE1B potency and PDE selectivity. Reduction of hERG affinity led to greater than a 3000-fold selectivity for PDE1B over hERG. 6-(4-Methoxybenzyl)-9-((tetrahydro-2H-pyran-4-yl)methyl)-8,9,10,11-tetrahydropyrido[4',3':4,5]thieno[3,2-e][1,2,4]triazolo[1,5-c]pyrimidin-5(6H)-one was identified as an orally bioavailable and brain penetrating PDE1B enzyme inhibitor with potent memory-enhancing effects in a rat model of object recognition memory.

A functional Kv1.2-hERG chimaeric channel expressed in Pichia pastoris

PubMed Central

Dhillon, Mandeep S.; Cockcroft, Christopher J.; Munsey, Tim; Smith, Kathrine J.; Powell, Andrew J.; Carter, Paul; Wrighton, David C.; Rong, Hong-lin; Yusaf, Shahnaz P.; Sivaprasadarao, Asipu

2014-01-01

Members of the six-transmembrane segment family of ion channels share a common structural design. However, there are sequence differences between the members that confer distinct biophysical properties on individual channels. Currently, we do not have 3D structures for all members of the family to help explain the molecular basis for the differences in their biophysical properties and pharmacology. This is due to low-level expression of many members in native or heterologous systems. One exception is rat Kv1.2 which has been overexpressed in Pichia pastoris and crystallised. Here, we tested chimaeras of rat Kv1.2 with the hERG channel for function in Xenopus oocytes and for overexpression in Pichia. Chimaera containing the S1–S6 transmembrane region of HERG showed functional and pharmacological properties similar to hERG and could be overexpressed and purified from Pichia. Our results demonstrate that rat Kv1.2 could serve as a surrogate to express difficult-to-overexpress members of the six-transmembrane segment channel family. PMID:24569544

In silico Analysis of Conformational Changes Induced by Mutation of Aromatic Binding Residues: Consequences for Drug Binding in the hERG K+ Channel

PubMed Central

Knape, Kirsten; Linder, Tobias; Wolschann, Peter; Beyer, Anton; Stary-Weinzinger, Anna

2011-01-01

Pharmacological inhibition of cardiac hERG K+ channels is associated with increased risk of lethal arrhythmias. Many drugs reduce hERG current by directly binding to the channel, thereby blocking ion conduction. Mutation of two aromatic residues (F656 and Y652) substantially decreases the potency of numerous structurally diverse compounds. Nevertheless, some drugs are only weakly affected by mutation Y652A. In this study we utilize molecular dynamics simulations and docking studies to analyze the different effects of mutation Y652A on a selected number of hERG blockers. MD simulations reveal conformational changes in the binding site induced by mutation Y652A. Loss of π-π-stacking between the two aromatic residues induces a conformational change of the F656 side chain from a cavity facing to cavity lining orientation. Docking studies and MD simulations qualitatively reproduce the diverse experimentally observed modulatory effects of mutation Y652A and provide a new structural interpretation for the sensitivity differences. PMID:22194911

Stereoselective Inhibition of the hERG1 Potassium Channel

PubMed Central

Grilo, Liliana Sintra; Carrupt, Pierre-Alain; Abriel, Hugues

2010-01-01

A growing number of drugs have been shown to prolong cardiac repolarization, predisposing individuals to life-threatening ventricular arrhythmias known as Torsades de Pointes. Most of these drugs are known to interfere with the human ether à-gogo related gene 1 (hERG1) channel, whose current is one of the main determinants of action potential duration. Prolonged repolarization is reflected by lengthening of the QT interval of the electrocardiogram, as seen in the suitably named drug-induced long QT syndrome. Chirality (presence of an asymmetric atom) is a common feature of marketed drugs, which can therefore exist in at least two enantiomers with distinct three-dimensional structures and possibly distinct biological fates. Both the pharmacokinetic and pharmacodynamic properties can differ between enantiomers, as well as also between individuals who take the drug due to metabolic polymorphisms. Despite the large number of reports about drugs reducing the hERG1 current, potential stereoselective contributions have only been scarcely investigated. In this review, we present a non-exhaustive list of clinically important molecules which display chiral toxicity that may be related to hERG1-blocking properties. We particularly focus on methadone cardiotoxicity, which illustrates the importance of the stereoselective effect of drug chirality as well as individual variations resulting from pharmacogenetics. Furthermore, it seems likely that, during drug development, consideration of chirality in lead optimization and systematic assessment of the hERG1 current block with all enantiomers could contribute to the reduction of the risk of drug-induced LQTS. PMID:21833176

Evidence for a crucial modulating role of the sodium channel in the QTc prolongation related to antipsychotics.

PubMed

Silvestre, Jordi S; O'Neill, Michael F; Prous, Josep R

2014-04-01

Blockade of the cardiac hERG channel is recognized as the main mechanism underlying the QT prolongation induced by many classes of drugs, including antipsychotics. However, antipsychotics interact with a variety of other pharmacological targets that could also modulate cardiac function. The present study aims to identify those key factors involved in the QT prolongation induced by antipsychotics. The interactions of 28 antipsychotics were measured on a variety of pharmacological targets. Binding affinity (K(i)), functional channel blockade (IC₅₀), and the corresponding ratios to total and free plasma drug concentration were compared with the corrected QT changes (QTc) associated with the therapeutic use of these drugs by multivariable linear regression analysis to determine the best predictors of QTc. Besides confirming hERG as the primary predictor of QTc, all analyses consistently show the concomitant involvement of Na(V)1.5 channel as modulating factor of the QTc related to hERG blockade. In particular, the hERG/Na(V)1.5 ratio explains the 57% of the overall QTc variability associated with antipsychotics. Since it is known that inhibition of late I Na could offset the dysfunctional effects of hERG blockade, we hypothesize the inhibition of late I(Na) as a crucial compensatory mechanism of the QTc associated with antipsychotics and hence an important factor to consider concomitantly with hERG blockade to appraise the arrhythmogenic risk of these drugs more accurately.

Tryptophan scanning mutagenesis of the HERG K+ channel: the S4 domain is loosely packed and likely to be lipid exposed

PubMed Central

Subbiah, Rajesh N; Kondo, Mari; Campbell, Terence J; Vandenberg, Jamie I

2005-01-01

Inherited mutations or drug-induced block of voltage-gated ion channels, including the human ether-à-go-go-related gene (HERG) K+ channel, are significant causes of malignant arrhythmias and sudden death. The fourth transmembrane domain (S4) of these channels contains multiple positive charges that move across the membrane electric field in response to changes in transmembrane voltage. In HERG K+ channels, the movement of the S4 domain across the transmembrane electric field is particularly slow. To examine the basis of the slow movement of the HERG S4 domain and specifically to probe the relationship between the S4 domain with the lipid bilayer and rest of the channel protein, we individually mutated each of the S4 amino acids in HERG (L524–L539) to tryptophan, and characterized the activation and deactivation properties of the mutant channels in Xenopus oocytes, using two-electrode voltage-clamp methods. Tryptophan has a large bulky hydrophobic sidechain and so should be tolerated at positions that interact with lipid, but not at positions involved in close protein–protein interactions. Significantly, we found that all S4 tryptophan mutants were functional. These data indicate that the S4 domain is loosely packed within the rest of the voltage sensor domain and is likely to be lipid exposed. Further, we identified residues K525, R528 and K538 as being the most important for slow activation of the channels. PMID:16166152

Inhibition of human ether-a-go-go-related gene K+ channel and IKr of guinea pig cardiomyocytes by antipsychotic drug trifluoperazine.

PubMed

Choi, Se-Young; Koh, Young-Sang; Jo, Su-Hyun

2005-05-01

Trifluoperazine, a commonly used antipsychotic drug, has been known to induce QT prolongation and torsades de pointes, which can cause sudden death. We studied the effects of trifluoperazine on the human ether-a-go-go-related gene (HERG) channel expressed in Xenopus oocytes and on the delayed rectifier K(+) current of guinea pig cardiomyocytes. The application of trifluoperazine showed a dose-dependent decrease in current amplitudes at the end of voltage steps and tail currents of HERG. The IC(50) for a trifluoperazine block of HERG current progressively decreased according to depolarization: IC(50) values at -40, 0, and +40 mV were 21.6, 16.6, and 9.29 microM, respectively. The voltage dependence of the block could be fitted with a monoexponential function, and the fractional electrical distance was estimated to be delta = 0.65. The block of HERG by trifluoperazine was use-dependent, exhibiting more rapid onset and greater steady-state block at higher frequencies of activation; there was partial relief of the block with decreasing frequency. In guinea pig ventricular myocytes, bath applications of 0.5 and 2 microM trifluoperazine at 36 degrees C blocked the rapidly activating delayed rectifier K(+) current by 32.4 and 72.9%, respectively; however, the same concentrations of trifluoperazine failed to significantly block the slowly activating delayed rectifier K(+) current. Our findings suggest the arrhythmogenic side effect of trifluoperazine is caused by a blockade of HERG and the rapid component of the delayed rectifier K(+) current rather than by the blockade of the slow component.

Development of recombinant cell line co-expressing mutated Nav1.5, Kir2.1, and hERG for the safety assay of drug candidates.

PubMed

Fujii, Masato; Ohya, Susumu; Yamamura, Hisao; Imaizumi, Yuji

2012-07-01

To provide a high-throughput screening method for human ether-a-go-go-gene-related gene (hERG) K(+) channel inhibition, a new recombinant cell line, in which single action potential (AP)-induced cell death was produced by gene transfection. Mutated human cardiac Na(+) channel Nav1.5 (IFM/Q3), which shows extremely slow inactivation, and wild-type inward rectifier K(+) channel, Kir2.1, were stably co-expressed in HEK293 cells (IFM/Q3+Kir2.1). In IFM/Q3+Kir2.1, application of single electrical stimulation (ES) elicited a long AP lasting more than 30 s and led cells to die by more than 70%, whereas HEK293 co-transfected with wild-type Nav1.5 and Kir2.1 fully survived. The additional expression of hERG K(+) channels in IFM/Q3+Kir2.1 shortened the duration of evoked AP and thereby markedly reduced the cell death. The treatment of the cells with hERG channel inhibitors such as nifekalant, E-4031, cisapride, terfenadine, and verapamil, recovered the prolonged AP and dose-dependently facilitated cell death upon ES. The EC(50) values to induce the cell death were 3 µM, 19 nM, 17 nM, 74 nM, and 3 µM, respectively, whereas 10 µM nifedipine did not induce cell death. Results indicate the high utility of this cell system for hERG K(+) channel safety assay.

Automated Patch-Clamp Methods for the hERG Cardiac Potassium Channel.

PubMed

Houtmann, Sylvie; Schombert, Brigitte; Sanson, Camille; Partiseti, Michel; Bohme, G Andrees

2017-01-01

The human Ether-a-go-go Related Gene (hERG) product has been identified as a central ion channel underlying both familial forms of elongated QT interval on the electrocardiogram and drug-induced elongation of the same QT segment. Indeed, reduced function of this potassium channel involved in the repolarization of the cardiac action potential can produce a type of life-threatening cardiac ventricular arrhythmias called Torsades de Pointes (TdP). Therefore, hERG inhibitory activity of newly synthetized molecules is a relevant structure-activity metric for compound prioritization and optimization in medicinal chemistry phases of drug discovery. Electrophysiology remains the gold standard for the functional assessment of ion channel pharmacology. The recent years have witnessed automatization and parallelization of the manual patch-clamp technique, allowing higher throughput screening on recombinant hERG channels. However, the multi-well plate format of automatized patch-clamp does not allow visual detection of potential micro-precipitation of poorly soluble compounds. In this chapter we describe bench procedures for the culture and preparation of hERG-expressing CHO cells for recording on an automated patch-clamp workstation. We also show that the sensitivity of the assay can be improved by adding a surfactant to the extracellular medium.

Alleviating CYP and hERG liabilities by structure optimization of dihydrofuran-fused tricyclic benzo[d]imidazole series - Potent, selective and orally efficacious microsomal prostaglandin E synthase-1 (mPGES-1) inhibitors: Part-2.

PubMed

Muthukaman, Nagarajan; Deshmukh, Sanjay; Tambe, Macchindra; Pisal, Dnyandeo; Tondlekar, Shital; Shaikh, Mahamadhanif; Sarode, Neelam; Kattige, Vidya G; Sawant, Pooja; Pisat, Monali; Karande, Vikas; Honnegowda, Srinivasa; Kulkarni, Abhay; Behera, Dayanidhi; Jadhav, Satyawan B; Sangana, Ramchandra R; Gudi, Girish S; Khairatkar-Joshi, Neelima; Gharat, Laxmikant A

2018-04-15

In an effort to identify CYP and hERG clean mPGES-1 inhibitors from the dihydrofuran-fused tricyclic benzo[d]imidazole series lead 7, an extensive structure-activity relationship (SAR) studies were performed. Optimization of A, D and E-rings in 7 afforded many potent compounds with human whole blood potency in the range of 160-950 nM. Selected inhibitors 21d, 21j, 21m, 21n, 21p and 22b provided selectivity against COX-enzymes and mPGES-1 isoforms (mPGES-2 and cPGES) along with sufficient selectivity against prostanoid synthases. Most of the tested analogs demonstrated required metabolic stability in liver microsomes, low hERG and CYP liability. Oral pharmacokinetics and bioavailability of lead compounds 21j, 21m and 21p are discussed in multiple species like rat, guinea pig, dog, and cynomolgus monkey. Besides, these compounds revealed low to moderate activity against human pregnane X receptor (hPXR). The selected lead 21j further demonstrated in vivo efficacy in acute hyperalgesia (ED 50 : 39.6 mg/kg) and MIA-induced osteoarthritic pain models (ED 50 : 106 mg/kg). Copyright © 2018 Elsevier Ltd. All rights reserved.

Spin properties of supermassive black holes with powerful outflows

NASA Astrophysics Data System (ADS)

Daly, Ruth. A.

2016-05-01

Relationships between beam power and accretion disc luminosity are studied for a sample of 55 high excitation radio galaxies (HERG), 13 low excitation radio galaxies (LERG), and 29 radio loud quasars (RLQ) with powerful outflows. The ratio of beam power to disc luminosity tends to be high for LERG, low for RLQ, and spans the full range of values for HERG. Writing general expressions for the disc luminosity and beam power and applying the empirically determined relationships allows a function that parametrizes the spins of the holes to be estimated. Interestingly, one of the solutions that is consistent with the data has a functional form that is remarkably similar to that expected in the generalized Blandford-Znajek model with a magnetic field that is similar in form to that expected in magnetically arrested disk (MAD) and advection-dominated accretion flow (ADAF) models. Values of the spin function, obtained independent of specific outflow models, suggest that spin and active galactic nucleus type are not related for these types of sources. The spin function can be used to solve for black hole spin in the context of particular outflow models, and one example is provided.

BmTx3, a scorpion toxin with two putative functional faces separately active on A-type K+ and HERG currents.

PubMed

Huys, Isabelle; Xu, Chen-Qi; Wang, Cheng-Zhong; Vacher, Hélène; Martin-Eauclaire, Marie-France; Chi, Cheng-Wu; Tytgat, Jan

2004-03-15

A novel HERG channel blocker was isolated from the venom of the scorpion Buthus martensi Karsch, sequenced and characterized at the pharmacological level after chemical synthesis. According to the determined amino acid sequence, the cDNA and genomic genes were then cloned. The genomic gene consists of two exons interrupted by an intron of 65 bp at position -6 upstream from the mature toxin. The protein sequence of this toxin was completely identical with that of a known A-type K+ current blocker BmTx3, belonging to scorpion alpha-KTx subfamily 15. Thus BmTx3 is the first reported alpha-KTx peptide also showing HERG-blocking activity, like gamma-KTx peptides. Moreover, different from classical alpha-KTx peptides, such as charybdotoxin, BmTx3 cannot block Shaker -type K+ channels. Phylogenetic tree analysis reveals that this toxin takes an intermediate position between classical alpha-KTx and gamma-KTx toxins. From a structural point of view, we propose that two separate functional faces might exist on the BmTx3 molecule, responsible for the two different K+-current-blocking functions. Face A, composed of Arg18 and Lys19 in the alpha-helix side, might correspond to HERG blocking activity, whereas Face B, containing a putative functional dyad (Lys27 and Tyr36) in the beta-sheet side, might correspond to A-type blocking activity. A specific deletion mutant with the disrupted Face B, BmTx3-Y36P37del, loses the A-type current-blocking activity, but keeps a similar HERG-blocking activity, as seen with the wild-type toxin.

The 5-HT(4) agonists cisapride, mosapride, and CJ-033466, a Novel potent compound, exhibit different human ether-a-go-go-related gene (hERG)-blocking activities.

PubMed

Toga, Tetsuo; Kohmura, Yumi; Kawatsu, Ryoichi

2007-10-01

The blocking effect of three 5-HT(4) agonists, cisapride, mosapride, and the newly discovered CJ-033466 on the human ether-a-go-go-related gene (hERG) channel was studied using a whole cell patch-clamp technique in HEK293 cells. Cisapride was found to be the most potent of the hERG blockers. CJ-033466 had the widest safety margin between its hERG blocking activity and 5-HT(4) agonism among the tested compounds. This suggests a lower clinical risk of cardiac arrhythmia in CJ-033466 compared with the other 2 agonists. Therefore, CJ-033466 has the potential to be a drug with higher therapeutic efficacy and less cardiac risk than both cisapride and mosapride.

Molecular basis and drug sensitivity of the delayed rectifier (IKr) in the fish heart.

PubMed

Hassinen, Minna; Haverinen, Jaakko; Vornanen, Matti

2015-01-01

Fishes are increasingly used as models for human cardiac diseases, creating a need for a better understanding of the molecular basis of fish cardiac ion currents. To this end we cloned KCNH6 channel of the crucian carp (Carassius carassius) that produces the rapid component of the delayed rectifier K(+) current (IKr), the main repolarising current of the fish heart. KCNH6 (ccErg2) was the main isoform of the Kv11 potassium channel family with relative transcript levels of 98.9% and 99.6% in crucian carp atrium and ventricle, respectively. KCNH2 (ccErg1), an orthologue to human cardiac Erg (Herg) channel, was only slightly expressed in the crucian carp heart. The native atrial IKr and the cloned ccErg2 were inhibited by similar concentrations of verapamil, terfenadine and KB-R7943 (P>0.05), while the atrial IKr was about an order of magnitude more sensitive to E-4031 than ccErg2 (P<0.05) suggesting that some accessory β-subunits may be involved. Sensitivity of the crucian carp atrial IKr to E-4031, terfenadine and KB-R7943 was similar to what has been reported for the Herg channel. In contrast, the sensitivity of the crucian carp IKr to verapamil was approximately 30 times higher than the previously reported values for the Herg current. In conclusion, the cardiac IKr is produced by non-orthologous gene products in fish (Erg2) and mammalian hearts (Erg1) and some marked differences exist in drug sensitivity between fish and mammalian Erg1/2 which need to be taken into account when using fish heart as a model for human heart. Copyright © 2015 Elsevier Inc. All rights reserved.

"A Nightmare Land, a Place of Death": An Exploration of the Moon as a Motif in Herge's "Destination Moon" (1953) and "Explorers on the Moon" (1954)

ERIC Educational Resources Information Center

Beauvais, Clementine

2010-01-01

This article analyses the symbolic meaning of the Moon in two "bande dessinee" books from the Tintin series, Herge's "Destination Moon" ("Objectif Lune," 1953) and its sequel "Explorers on the Moon" ("On a Marche sur la Lune," 1954). It argues that these two volumes stand out in the series for their graphic, narrative and philosophical emphasis on…

Toward a New Gold Standard for Early Safety: Automated Temperature-Controlled hERG Test on the PatchLiner®

PubMed Central

Polonchuk, Liudmila

2012-01-01

The Patchliner® temperature-controlled automated patch clamp system was evaluated for testing drug effects on potassium currents through human ether-à-go-go related gene (hERG) channels expressed in Chinese hamster ovary cells at 35–37°C. IC50 values for a set of reference drugs were compared with those obtained using the conventional voltage clamp technique. The results showed good correlation between the data obtained using automated and conventional electrophysiology. Based on these results, the Patchliner® represents an innovative automated electrophysiology platform for conducting the hERG assay that substantially increases throughput and has the advantage of operating at physiological temperature. It allows fast, accurate, and direct assessment of channel function to identify potential proarrhythmic side effects and sets a new standard in ion channel research for drug safety testing. PMID:22303293

Comparison of electrophysiological data from human-induced pluripotent stem cell-derived cardiomyocytes to functional preclinical safety assays.

PubMed

Harris, Kate; Aylott, Mike; Cui, Yi; Louttit, James B; McMahon, Nicholas C; Sridhar, Arun

2013-08-01

Human-induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) are a potential source to develop assays for predictive electrophysiological safety screening. Published studies show that the relevant physiology and pharmacology exist but does not show the translation between stem cell cardiomyocyte assays and other preclinical safety screening assays, which is crucial for drug discovery and safety scientists and the regulators. Our studies are the first to show the pharmacology of ion channel blockade and compare them with existing functional cardiac electrophysiology studies. Ten compounds (a mixture of pure hERG [E-4031 and Cisapride], hERG and sodium [Flecainide, Mexiletine, Quinidine, and Terfenadine], calcium channel blockers [Nifedipine and Verapamil], and two proprietary compounds [GSK A and B]) were tested, and results from hiPSC-CMs studied on multielectrode arrays (MEA) were compared with other preclincial models and clinical drug concentrations and effects using integrated risk assessment plots. All ion channel blockers produced (1) functional effects on repolarization and depolarization around the IC25 and IC50 values and (2) excessive blockade of hERG and/or blockade of sodium current precipitated arrhythmias. Our MEA data show that hiPSC-CMs demonstrate relevant pharmacology and show excellent correlations to current functional cardiac electrophysiological studies. Based on these results, MEA assays using iPSC-CMs offer a reliable, cost effective, and surrogate to preclinical in vitro testing, in addition to the 3Rs (refine, reduce, and replace animals in research) benefit.

Interactions between charged residues in the transmembrane segments of the voltage-sensing domain in the hERG channel.

PubMed

Zhang, M; Liu, J; Jiang, M; Wu, D-M; Sonawane, K; Guy, H R; Tseng, G-N

2005-10-01

Studies on voltage-gated K channels such as Shaker have shown that positive charges in the voltage-sensor (S4) can form salt bridges with negative charges in the surrounding transmembrane segments in a state-dependent manner, and different charge pairings can stabilize the channels in closed or open states. The goal of this study is to identify such charge interactions in the hERG channel. This knowledge can provide constraints on the spatial relationship among transmembrane segments in the channel's voltage-sensing domain, which are necessary for modeling its structure. We first study the effects of reversing S4's positive charges on channel activation. Reversing positive charges at the outer (K525D) and inner (K538D) ends of S4 markedly accelerates hERG activation, whereas reversing the 4 positive charges in between either has no effect or slows activation. We then use the 'mutant cycle analysis' to test whether D456 (outer end of S2) and D411 (inner end of S1) can pair with K525 and K538, respectively. Other positive charges predicted to be able, or unable, to interact with D456 or D411 are also included in the analysis. The results are consistent with predictions based on the distribution of these charged residues, and confirm that there is functional coupling between D456 and K525 and between D411 and K538.

F463L increases the potential of dofetilide on human ether-a-go-go-related gene (hERG) channels.

PubMed

Cheng, Gong; Wu, Jine; Han, Wenqi; Sun, Chaofeng

2018-06-01

Mutations in genes related to long QT syndrome (LQTS) is recognized as an independent risk of drug-induced LQTS. We previously screened a mutation F463L in a Chinese patient with LQT2, syncope, and epilepsy. Here, we planned to illustrate how F463L influences the action of dofetilide on hERG channels. F463L-hERG plasmids were transfected into the stable Human Embryonic Kidney 293 (HEK293) cells expressing WT-hERG to generate heterozygous mutant (WT + F463L-hERG). Whole-cell patch clamp and laser confocal scanning microscopy were used to evaluate electrophysiological consequences and the membrane distribution of hERG protein. In comparison of WT-hERG channels exposed to dofetilide, heterozygous F463L-hERG channels showed a reduction in the density of tail currents when exposed amidarone. F463L-hERG also altered the action of dofetilide on the gating properties of hERG channels. Images of dofetilide-treated cells expressing heterozygous F463L showed a severe retention and reduction of protein expression on the membrane compared to WT. In conclusion, dofetilide displays a powerful inhibitory effect on the currents from cells expressing heterozygous F463L, thus showing an additive suppression of currents by F463L with dofetilide. © 2018 Wiley Periodicals, Inc.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yagi, Yukihiro; Pharmaceutical Research Center, Meiji Seika Pharma Co., Ltd., 760 Morooka-cho, Kohoku-ku, Yokohama, Kanagawa 222–8567; Nakamura, Yuji

Fingolimod, a sphingosine 1-phosphate (S1P) receptor subtype 1, 3, 4 and 5 modulator, has been used for the treatment of patients with relapsing forms of multiple sclerosis, but atrioventricular conduction block and/or QT-interval prolongation have been reported in some patients after the first dose. In this study, we directly compared the electropharmacological profiles of fingolimod with those of siponimod, a modulator of sphingosine 1-phosphate receptor subtype 1 and 5, using in vivo guinea-pig model and in vitro human ether-a-go-go-related gene (hERG) assay to better understand the onset mechanisms of the clinically observed adverse events. Fingolimod (0.01 and 0.1 mg/kg) ormore » siponimod (0.001 and 0.01 mg/kg) was intravenously infused over 10 min to the halothane-anaesthetized guinea pigs (n = 4), whereas the effects of fingolimod (1 μmol/L) and siponimod (1 μmol/L) on hERG current were examined (n = 3). The high doses of fingolimod and siponimod induced atrioventricular conduction block, whereas the low dose of siponimod prolonged PR interval, which was not observed by that of fingolimod. The high dose of fingolimod prolonged QT interval, which was not observed by either dose of siponimod. Meanwhile, fingolimod significantly inhibited hERG current, which was not observed by siponimod. These results suggest that S1P receptor subtype 1 in the heart could be one of the candidates for fingolimod- and siponimod-induced atrioventricular conduction block since S1P receptor subtype 5 is localized at the brain, and that direct I{sub Kr} inhibition may play a key role in fingolimod-induced QT-interval prolongation. - Highlights: • Fingolimod and siponimod are S1P{sub 1,3,4,5} and S1P{sub 1,5} receptor modulators, respectively. • Fingolimod and siponimod induced AV block in the halothane-anesthetized guinea pigs. • S1P{sub 1} in the hearts may be the target of fingolimod- and siponimod-induced AV block. • Fingolimod directly inhibited hERG current, which was not observed by siponimod. • I{sub Kr} inhibition may play a key role in the fingolimod-induced QT prolongation.« less

Trafficking-deficient hERG K+ channels linked to long QT syndrome are regulated by a microtubule-dependent quality control compartment in the ER

PubMed Central

Smith, Jennifer L.; McBride, Christie M.; Nataraj, Parvathi S.; Bartos, Daniel C.; January, Craig T.

2011-01-01

The human ether-a-go-go related gene (hERG) encodes the voltage-gated K+ channel that underlies the rapidly activating delayed-rectifier current in cardiac myocytes. hERG is synthesized in the endoplasmic reticulum (ER) as an “immature” N-linked glycoprotein and is terminally glycosylated in the Golgi apparatus. Most hERG missense mutations linked to long QT syndrome type 2 (LQT2) reduce the terminal glycosylation and functional expression. We tested the hypothesis that a distinct pre-Golgi compartment negatively regulates the trafficking of some LQT2 mutations to the Golgi apparatus. We found that treating cells in nocodazole, a microtubule depolymerizing agent, altered the subcellular localization, functional expression, and glycosylation of the LQT2 mutation G601S-hERG differently from wild-type hERG (WT-hERG). G601S-hERG quickly redistributed to peripheral compartments that partially colocalized with KDEL (Lys-Asp-Glu-Leu) chaperones but not calnexin, Sec31, or the ER golgi intermediate compartment (ERGIC). Treating cells in E-4031, a drug that increases the functional expression of G601S-hERG, prevented the accumulation of G601S-hERG to the peripheral compartments and increased G601S-hERG colocalization with the ERGIC. Coexpressing the temperature-sensitive mutant G protein from vesicular stomatitis virus, a mutant N-linked glycoprotein that is retained in the ER, showed it was not restricted to the same peripheral compartments as G601S-hERG at nonpermissive temperatures. We conclude that the trafficking of G601S-hERG is negatively regulated by a microtubule-dependent compartment within the ER. Identifying mechanisms that prevent the sorting or promote the release of LQT2 channels from this compartment may represent a novel therapeutic strategy for LQT2. PMID:21490315

In silico design of novel hERG-neutral sildenafil-like PDE5 inhibitors.

PubMed

Kayık, Gülru; Tüzün, Nurcan Ş; Durdagi, Serdar

2017-10-01

Cyclic nucleotide phosphodiesterase enzymes (PDEs) have functions in regulating the levels of intracellular second messengers, 3', 5'-cyclic adenosine monophosphate (cAMP) and 3', 5'-cyclic guanosine monophosphate (cGMP), via hydrolysis and decomposing mechanisms in cells. They take essential roles in modulating various cellular activities such as memory and smooth muscle functions. PDE type 5 (PDE5) inhibitors enhance the vasodilatory effects of cGMP in the corpus cavernosum and they are used to treat erectile dysfunction. Patch clamp experiments showed that the IC 50 values of the human ether-à-go-go-related gene (hERG1) potassium (K) ion channel blocking affinity of PDE5 inhibitors sildenafil, vardenafil, and tadalafil as 33, 12, and 100 μM, respectively. hERG1 channel is responsible for the regulation of the action potential of human ventricular myocyte by contributing the rapid component of delayed rectifier K + current (I Kr ) component of the cardiac action potential. In this work, interaction patterns and binding affinity predictions of selected PDE5 inhibitors against the hERG1 channel are studied. It is attempted to develop PDE5 inhibitor analogs with lower binding affinity to hERG1 ion channel while keeping their pharmacological activity against their principal target PDE5 using in silico methods. Based on detailed analyses of docking poses and predicted interaction energies, novel analogs of PDE5 inhibitors with lower predicted binding affinity to hERG1 channels without loosing their principal target activity were proposed. Moreover, molecular dynamics (MD) simulations and post-processing MD analyses (i.e. Molecular Mechanics/Generalized Born Surface Area calculations) were performed. Detailed analysis of molecular simulations helped us to better understand the PDE5 inhibitor-target binding interactions in the atomic level. Results of this study can be useful for designing of novel and safe PDE5 inhibitors with enhanced activity and other tailored properties.

18β-Glycyrrhetinic acid preferentially blocks late Na current generated by ΔKPQ Nav1.5 channels

PubMed Central

Du, Yi-mei; Xia, Cheng-kun; Zhao, Ning; Dong, Qian; Lei, Ming; Xia, Jia-hong

2012-01-01

Aim: To compare the effects of two stereoisomeric forms of glycyrrhetinic acid on different components of Na+ current, HERG and Kv1.5 channel currents. Methods: Wild-type (WT) and long QT syndrome type 3 (LQT-3) mutant ΔKPQ Nav1.5 channels, as well as HERG and Kv1.5 channels were expressed in Xenopus oocytes. In addition, isolated human atrial myocytes were used. Two-microelectrode voltage-clamp technique was used to record the voltage-activated currents. Results: Superfusion of 18β-glycyrrhetinic acid (18β-GA, 1–100 μmol/L) blocked both the peak current (INa,P) and late current (INa,L) generated by WT and ΔKPQ Nav1.5 channels in a concentration-dependent manner, while 18α-glycyrrhetinic acid (18α-GA) at the same concentrations had no effects. 18β-GA preferentially blocked INa,L (IC50=37.2±14.4 μmol/L) to INa,P (IC50=100.4±11.2 μmol/L) generated by ΔKPQ Nav1.5 channels. In human atrial myocytes, 18β-GA (30 μmol/L) inhibited 47% of INa,P and 87% of INa,L induced by Anemonia sulcata toxin (ATX-II, 30 nmol/L). Superfusion of 18β-GA (100 μmol/L) had no effects on HERG and Kv1.5 channel currents. Conclusion: 18β-GA preferentially blocked the late Na current without affecting HERG and Kv1.5 channels. PMID:22609834

Evaluation of nefazodone-induced cardiotoxicity in human induced pluripotent stem cell-derived cardiomyocytes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sujeong, E-mail: crystalee@gmail.com; Lee, Hyang-Ae, E-mail: hyangaelee@gmail.com; Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 110-799

2016-04-01

The recent establishment of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), which express the major cardiac ion channels and recapitulate spontaneous mechanical and electrical activities, may provide a possible solution for the lack of in vitro human-based cardiotoxicity testing models. Cardiotoxicity induced by the antidepressant nefazodone was previously revealed to cause an acquired QT prolongation by hERG channel blockade. To elucidate the cellular mechanisms underlying the cardiotoxicity of nefazodone beyond hERG, its effects on cardiac action potentials (APs) and ion channels were investigated using hiPSC-CMs with whole-cell patch clamp techniques. In a proof of principle study, we examined the effectsmore » of cardioactive channel blockers on the electrophysiological profile of hiPSC-CMs in advance of the evaluation of nefazodone. Nefazodone dose-dependently prolonged the AP duration at 90% (APD{sub 90}) and 50% (APD{sub 50}) repolarization, reduced the maximum upstroke velocity (dV/dt{sub max}) and induced early after depolarizations. Voltage-clamp studies of hiPSC-CMs revealed that nefazodone inhibited various voltage-gated ion channel currents including I{sub Kr}, I{sub Ks}, I{sub Na}, and I{sub Ca}. Among them, I{sub Kr} and I{sub Na} showed relatively higher sensitivity to nefazodone, consistent with the changes in the AP parameters. In summary, hiPSC-CMs enabled an integrated approach to evaluate the complex interactions of nefazodone with cardiac ion channels. These results suggest that hiPSC-CMs can be an effective model for detecting drug-induced arrhythmogenicity beyond the current standard assay of heterologously expressed hERG K{sup +} channels. - Highlights: • Nefazodone prolonged APD and decreased upstroke velocity of APs in hiPSC-CMs. • Nefazodone inhibited cardiac ion channels, especially I{sub Kr} and I{sub Na}, in hiPSC-CMs. • Nefazodone-induced AP changes are mainly the result of I{sub Kr} and I{sub Na} inhibition. • hiPSC-CMs are sensitive model to validate nefazodone-induced cardiotoxicity. • hiPSC-CMs provide an integrated approach for evaluating mechanism of drug actions.« less

hERGCentral: a large database to store, retrieve, and analyze compound-human Ether-à-go-go related gene channel interactions to facilitate cardiotoxicity assessment in drug development.

PubMed

Du, Fang; Yu, Haibo; Zou, Beiyan; Babcock, Joseph; Long, Shunyou; Li, Min

2011-12-01

The unintended and often promiscous inhibition of the cardiac human Ether-à-go-go related gene (hERG) potassium channel is a common cause for either delay or removal of therapeutic compounds from development and withdrawal of marketed drugs. The clinical manifestion is prolongation of the duration between QRS complex and T-wave measured by surface electrocardiogram (ECG)-hence Long QT Syndrome. There are several useful online resources documenting hERG inhibition by known drugs and bioactives. However, their utilities remain somewhat limited because they are biased toward well-studied compounds and their number of data points tends to be much smaller than many commercial compound libraries. The hERGCentral ( www.hergcentral.org ) is mainly based on experimental data obtained from a primary screen by electrophysiology against more than 300,000 structurally diverse compounds. The system is aimed to display and combine three resources: primary electrophysiological data, literature, as well as online reports and chemical library collections. Currently, hERGCentral has annotated datasets of more than 300,000 compounds including structures and chemophysiological properties of compounds, raw traces, and biophysical properties. The system enables a variety of query formats, including searches for hERG effects according to either chemical structure or properties, and alternatively according to the specific biophysical properties of current changes caused by a compound. Therefore, the hERGCentral, as a unique and evolving resource, will facilitate investigation of chemically induced hERG inhibition and therefore drug development. © MARY ANN LIEBERT, INC.

hERGCentral: A Large Database to Store, Retrieve, and Analyze Compound-Human Ether-à-go-go Related Gene Channel Interactions to Facilitate Cardiotoxicity Assessment in Drug Development

PubMed Central

Du, Fang; Yu, Haibo; Zou, Beiyan; Babcock, Joseph; Long, Shunyou

2011-01-01

Abstract The unintended and often promiscous inhibition of the cardiac human Ether-à-go-go related gene (hERG) potassium channel is a common cause for either delay or removal of therapeutic compounds from development and withdrawal of marketed drugs. The clinical manifestion is prolongation of the duration between QRS complex and T-wave measured by surface electrocardiogram (ECG)—hence Long QT Syndrome. There are several useful online resources documenting hERG inhibition by known drugs and bioactives. However, their utilities remain somewhat limited because they are biased toward well-studied compounds and their number of data points tends to be much smaller than many commercial compound libraries. The hERGCentral (www.hergcentral.org) is mainly based on experimental data obtained from a primary screen by electrophysiology against more than 300,000 structurally diverse compounds. The system is aimed to display and combine three resources: primary electrophysiological data, literature, as well as online reports and chemical library collections. Currently, hERGCentral has annotated datasets of more than 300,000 compounds including structures and chemophysiological properties of compounds, raw traces, and biophysical properties. The system enables a variety of query formats, including searches for hERG effects according to either chemical structure or properties, and alternatively according to the specific biophysical properties of current changes caused by a compound. Therefore, the hERGCentral, as a unique and evolving resource, will facilitate investigation of chemically induced hERG inhibition and therefore drug development. PMID:22149888

The etomidate analog ET-26 HCl retains superior myocardial performance: Comparisons with etomidate in vivo and in vitro.

PubMed

Liu, Xingxing; Song, Haibo; Yang, Jun; Zhou, Cheng; Kang, Yi; Yang, Linghui; Liu, Jin; Zhang, Wensheng

2018-01-01

(R)-2-methoxyethyl1-(1-phenylethyl)-1H-imidazole-5-carboxylate hydrochloride (ET-26 HCl) is a novel etomidate analogue. The purpose of this study was to characterize whether ET-26 HCl could retain the superior myocardial performance of etomidate in vivo and in vitro. In vivo, the influence of ET-26 HCl and etomidate on the cardiac function of dogs was confirmed using echocardiography and electrocardiogram. In vitro, a Langendorff preparation was used to examine direct myocardial performance in isolated rat hearts, and a whole-cell patch-clamp technique was used to study effects on the human ether-a-go-go-related gene (hERG) channel. In vivo, after a single bolus administration of ET-26 HCl or etomidate, no significant difference in echocardiography and electrocardiogram parameters was observed. No arrhythmia occurred and no QT interval prolongation happened during the study period. In the in vitro Langendorff preparation, none of the cardiac parameters were abnormal, and the hERG recordings showed that ET-26 HCl and etomidate inhibited the tail current of the hERG in a concentration-dependent manner with an IC50 of 742.51 μM and 263.60 μM, respectively. In conclusion, through an in vivo experiment and a whole organ preparation, the current study found that ET-26 HCl can maintain a myocardial performance that is similar to that of etomidate. In addition, the electrophysiology study indicated that ET-26 HCl and etomidate inhibited the hERG at a supra-therapeutic concentration.

Automated Electrophysiology Makes the Pace for Cardiac Ion Channel Safety Screening

PubMed Central

Möller, Clemens; Witchel, Harry

2011-01-01

The field of automated patch-clamp electrophysiology has emerged from the tension between the pharmaceutical industry’s need for high-throughput compound screening versus its need to be conservative due to regulatory requirements. On the one hand, hERG channel screening was increasingly requested for new chemical entities, as the correlation between blockade of the ion channel coded by hERG and torsades de pointes cardiac arrhythmia gained increasing attention. On the other hand, manual patch-clamping, typically quoted as the “gold-standard” for understanding ion channel function and modulation, was far too slow (and, consequently, too expensive) for keeping pace with the numbers of compounds submitted for hERG channel investigations from pharmaceutical R&D departments. In consequence it became more common for some pharmaceutical companies to outsource safety pharmacological investigations, with a focus on hERG channel interactions. This outsourcing has allowed those pharmaceutical companies to build up operational flexibility and greater independence from internal resources, and allowed them to obtain access to the latest technological developments that emerged in automated patch-clamp electrophysiology – much of which arose in specialized biotech companies. Assays for nearly all major cardiac ion channels are now available by automated patch-clamping using heterologous expression systems, and recently, automated action potential recordings from stem-cell derived cardiomyocytes have been demonstrated. Today, most of the large pharmaceutical companies have acquired automated electrophysiology robots and have established various automated cardiac ion channel safety screening assays on these, in addition to outsourcing parts of their needs for safety screening. PMID:22131974

Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity.

PubMed

Papadatos, George; Alkarouri, Muhammad; Gillet, Valerie J; Willett, Peter; Kadirkamanathan, Visakan; Luscombe, Christopher N; Bravi, Gianpaolo; Richmond, Nicola J; Pickett, Stephen D; Hussain, Jameed; Pritchard, John M; Cooper, Anthony W J; Macdonald, Simon J F

2010-10-25

Previous studies of the analysis of molecular matched pairs (MMPs) have often assumed that the effect of a substructural transformation on a molecular property is independent of the context (i.e., the local structural environment in which that transformation occurs). Experiments with large sets of hERG, solubility, and lipophilicity data demonstrate that the inclusion of contextual information can enhance the predictive power of MMP analyses, with significant trends (both positive and negative) being identified that are not apparent when using conventional, context-independent approaches.

Novel Terminal Bipheny-Based Diapophytoene Desaturases (CrtN) Inhibitors as Anti-MRSA/VISR/LRSA Agents with Reduced hERG Activity.

PubMed

Li, Baoli; Ni, Shuaishuai; Mao, Fei; Chen, Feifei; Liu, Yifu; Wei, Hanwen; Chen, Wenhua; Zhu, Jin; Lan, Lefu; Li, Jian

2018-01-11

CrtN has been identified as an attractive and druggable target for treating pigmented Staphylococcus aureus infections. More than 100 new compounds were synthesized, which target the overwhelming the defects of the CrtN inhibitor 1. Analogues 23a and 23b demonstrated a significant activity against pigmented S. aureus Newman and 13 MRSA strains (IC 50 = 0.02-10.5 nM), along with lower hERG inhibition (IC 50 > 30 μM, ∼10-fold decrease in comparison with 1). Furthermore, 23a and 23b were confirmed to reduce the staphylococcal load in the kidney and heart in a mouse model with normal treatment deeper than pretreatment ones, comparable even with vancomycin and linezolid. Remarkably, 23a could strongly block the pigment biosynthesis of these nine multidrug-resistant MRSA strains, including excellent activity against LRSA strains and VISA strains in vivo, and all of which demonstrated that 23a has a huge potential against intractable MRSA, VISA, and LRSA issues as a therapeutic drug.

Development of (6R)-2-Nitro-6-[4-(trifluoromethoxy)phenoxy]-6,7-dihydro-5H-imidazo[2,1-b][1,3]oxazine (DNDI-8219): A New Lead for Visceral Leishmaniasis

PubMed Central

2018-01-01

Discovery of the potent antileishmanial effects of antitubercular 6-nitro-2,3-dihydroimidazo[2,1-b][1,3]oxazoles and 7-substituted 2-nitro-5,6-dihydroimidazo[2,1-b][1,3]oxazines stimulated the examination of further scaffolds (e.g., 2-nitro-5,6,7,8-tetrahydroimidazo[2,1-b][1,3]oxazepines), but the results for these seemed less attractive. Following the screening of a 900-compound pretomanid analogue library, several hits with more suitable potency, solubility, and microsomal stability were identified, and the superior efficacy of newly synthesized 6R enantiomers with phenylpyridine-based side chains was established through head-to-head assessments in a Leishmania donovani mouse model. Two such leads (R-84 and R-89) displayed promising activity in the more stringent Leishmania infantum hamster model but were unexpectedly found to be potent inhibitors of hERG. An extensive structure–activity relationship investigation pinpointed two compounds (R-6 and pyridine R-136) with better solubility and pharmacokinetic properties that also provided excellent oral efficacy in the same hamster model (>97% parasite clearance at 25 mg/kg, twice daily) and exhibited minimal hERG inhibition. Additional profiling earmarked R-6 as the favored backup development candidate. PMID:29461823

Can non‐clinical repolarization assays predict the results of clinical thorough QT studies? Results from a research consortium

PubMed Central

Park, Eunjung; Gintant, Gary A; Bi, Daoqin; Kozeli, Devi; Pettit, Syril D; Skinner, Matthew; Willard, James; Wisialowski, Todd; Koerner, John; Valentin, Jean‐Pierre

2018-01-01

Background and Purpose Translation of non‐clinical markers of delayed ventricular repolarization to clinical prolongation of the QT interval corrected for heart rate (QTc) (a biomarker for torsades de pointes proarrhythmia) remains an issue in drug discovery and regulatory evaluations. We retrospectively analysed 150 drug applications in a US Food and Drug Administration database to determine the utility of established non‐clinical in vitro IKr current human ether‐à‐go‐go‐related gene (hERG), action potential duration (APD) and in vivo (QTc) repolarization assays to detect and predict clinical QTc prolongation. Experimental Approach The predictive performance of three non‐clinical assays was compared with clinical thorough QT study outcomes based on free clinical plasma drug concentrations using sensitivity and specificity, receiver operating characteristic (ROC) curves, positive (PPVs) and negative predictive values (NPVs) and likelihood ratios (LRs). Key Results Non‐clinical assays demonstrated robust specificity (high true negative rate) but poor sensitivity (low true positive rate) for clinical QTc prolongation at low‐intermediate (1×–30×) clinical exposure multiples. The QTc assay provided the most robust PPVs and NPVs (ability to predict clinical QTc prolongation). ROC curves (overall test accuracy) and LRs (ability to influence post‐test probabilities) demonstrated overall marginal performance for hERG and QTc assays (best at 30× exposures), while the APD assay demonstrated minimal value. Conclusions and Implications The predictive value of hERG, APD and QTc assays varies, with drug concentrations strongly affecting translational performance. While useful in guiding preclinical candidates without clinical QT prolongation, hERG and QTc repolarization assays provide greater value compared with the APD assay. PMID:29181850

Mechanism of As2O3-Induced Action Potential Prolongation and Using hiPS-CMs to Evaluate the Rescue Efficacy of Drugs With Different Rescue Mechanism.

PubMed

Yan, Meng; Feng, Lifang; Shi, Yanhui; Wang, Junnan; Liu, Yan; Li, Fengmei; Li, Baoxin

2017-08-01

Arsenic trioxide (As2O3) has been verified as a breakthrough in the management of acute promyelocytic leukemia in recent decades. However, cardiotoxicity, especially long QT syndrome (LQTS) has become the most important issue during As2O3 treatment. The characterized mechanisms behind this adverse effect are inhibition of cardiac hERG channel trafficking and increase of cardiac calcium currents. In our study, we found a new pathway underlying As2O3-induced cardiotoxicity that As2O3 accelerates lysosomal degradation of hERG on plasma membrane after using brefeldin A (BFA) to block protein trafficking. Then we explored pharmacological rescue strategies on As2O3-induced LQTS, and found that 4 therapeutic agents exert rescue efficacy via 3 different pathways: fexofenadine and astemizole facilitate hERG trafficking via promotion of channel-chaperone formation after As2O3 incubation; ranolazine slows hERG degradation in the presence of As2O3; and resveratrol shows significant attenuation on calcium current increase triggered by As2O3. Moreover, we used human-induced pluripotent stem cell derived cardiomyocytes (hiPS-CMs) to evaluate the rescue effects of the above agents on As2O3-induced prolongation of action potential duration (APD) and demonstrated that fexofenadine and resveratrol significantly ameliorate the prolonged APD. These observations suggested that pharmacological chaperone like fexofenadine and resveratrol might have the potential to protect against the cardiotoxicity of As2O3. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Different protein kinase C isoenzymes mediate inhibition of cardiac rapidly activating delayed rectifier K+ current by different G-protein coupled receptors.

PubMed

Liu, Xueli; Wang, Yuhong; Zhang, Hua; Shen, Li; Xu, Yanfang

2017-12-01

Elevated angiotensin II (Ang II) and sympathetic activity contributes to a high risk of ventricular arrhythmias in heart disease. The rapidly activating delayed rectifier K + current (I Kr ) carried by the hERG channels plays a critical role in cardiac repolarization, and decreased I Kr is involved in increased cardiac arrhythmogenicity. Stimulation of α 1A -adrenoreceptors or angiotensin II AT 1 receptors is known to inhibit I Kr via PKC. Here, we have identified the PKC isoenzymes mediating the inhibition of I Kr by activation of these two different GPCRs. The whole-cell patch-clamp technique was used to record I Kr in guinea pig cardiomyocytes and HEK293 cells co-transfected with hERG and α 1A -adrenoreceptor or AT 1 receptor genes. A broad spectrum PKC inhibitor Gö6983 (not inhibiting PKCε), a selective cPKC inhibitor Gö6976 and a PKCα-specific inhibitor peptide, blocked the inhibition of I Kr by the α 1A -adrenoreceptor agonist A61603. However, these inhibitors did not affect the reduction of I Kr by activation of AT 1 receptors, whereas the PKCε-selective inhibitor peptide did block the effect. The effects of angiotensin II and the PKCε activator peptide were inhibited in mutant hERG channels in which 17 of the 18 PKC phosphorylation sites were deleted, whereas a deletion of the N-terminus of the hERG channels selectively prevented the inhibition elicited by A61603 and the cPKC activator peptide. Our results indicated that inhibition of I Kr by activation of α 1A -adrenoreceptors or AT 1 receptors were mediated by PKCα and PKCε isoforms respectively, through different molecular mechanisms. © 2017 The British Pharmacological Society.

Kinesin spindle protein (KSP) inhibitors. Part 3: synthesis and evaluation of phenolic 2,4-diaryl-2,5-dihydropyrroles with reduced hERG binding and employment of a phosphate prodrug strategy for aqueous solubility.

PubMed

Garbaccio, Robert M; Fraley, Mark E; Tasber, Edward S; Olson, Christy M; Hoffman, William F; Arrington, Kenneth L; Torrent, Maricel; Buser, Carolyn A; Walsh, Eileen S; Hamilton, Kelly; Schaber, Michael D; Fernandes, Christine; Lobell, Robert B; Tao, Weikang; South, Vicki J; Yan, Youwei; Kuo, Lawrence C; Prueksaritanont, Thomayant; Slaughter, Donald E; Shu, Cathy; Heimbrook, David C; Kohl, Nancy E; Huber, Hans E; Hartman, George D

2006-04-01

2,4-Diaryl-2,5-dihydropyrroles have been discovered to be novel, potent and water-soluble inhibitors of KSP, an emerging therapeutic target for the treatment of cancer. A potential concern for these basic KSP inhibitors (1 and 2) was hERG binding that can be minimized by incorporation of a potency-enhancing C2 phenol combined with neutral N1 side chains. Aqueous solubility was restored to these, and other, non-basic inhibitors, through a phosphate prodrug strategy.

Discovery of 3-morpholino-imidazole[1,5- a ]pyrazine BTK inhibitors for rheumatoid arthritis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boga, Sobhana Babu; Alhassan, Abdul-Basit; Liu, Jian

8-Amino-imidazo[1,5-a]pyrazine-based Bruton’s tyrosine kinase (BTK) inhibitors, such as 6, exhibited potent inhibition of BTK but required improvements in both kinase and hERG selectivity (Liu et al., 2016; Gao et al., 2017). In an effort to maintain the inhibitory activity of these analogs and improve their selectivity profiles, we carried out SAR exploration of groups at the 3-position of pyrazine compound 6. This effort led to the discovery of the morpholine group as an optimized pharmacophore. Compounds 13, 23 and 38 displayed excellent BTK potencies, kinase and hERG selectivities, and pharmacokinetic profiles.

Discovery of 5-Chloro-1-(5-chloro-2-(methylsulfonyl)benzyl)-2-imino-1,2-dihydropyridine-3-carboxamide (TAK-259) as a Novel, Selective, and Orally Active α1D Adrenoceptor Antagonist with Antiurinary Frequency Effects: Reducing Human Ether-a-go-go-Related Gene (hERG) Liabilities.

PubMed

Sakauchi, Nobuki; Kohara, Yasuhisa; Sato, Ayumu; Suzaki, Tomohiko; Imai, Yumi; Okabe, Yuichi; Imai, Shigemitsu; Saikawa, Reiko; Nagabukuro, Hiroshi; Kuno, Haruhiko; Fujita, Hisashi; Kamo, Izumi; Yoshida, Masato

2016-04-14

A novel structural class of iminopyridine derivative 1 was identified as a potent and selective human α1D adrenoceptor (α1D adrenergic receptor; α1D-AR) antagonist against α1A- and α1B-AR through screening of an in-house compound library. From initial structure-activity relationship studies, we found lead compound 9m with hERG K(+) channel liability. To develop analogues with reduced hERG K(+) channel inhibition, a combination of site-directed mutagenesis and docking studies was employed. Further optimization led to the discovery of (R)-9s and 9u, which showed antagonistic activity by a bladder strip test in rats with bladder outlet obstruction, as well as ameliorated cystitis-induced urinary frequency in rats. Ultimately, 9u was selected as a clinical candidate. This is the first study to show the utility of iminopyridine derivatives as selective α1D-AR antagonists and evaluate their effects in vivo.

Discovery of Potent Benzocycloalkane Derived Diapophytoene Desaturase Inhibitors with an Enhanced Safety Profile for the Treatment of MRSA, VISA, and LRSA Infections.

PubMed

Li, Baoli; Ni, Shuaishuai; Chen, Feifei; Mao, Fei; Wei, Hanwen; Liu, Yifu; Zhu, Jin; Lan, Lefu; Li, Jian

2018-03-09

Blocking the biosynthesis process of staphyloxanthin has emerged as a promising antivirulence strategy. Our previous research revealed that diapophytoene desaturase was an attractive and druggable target against infections caused by pigmented Staphylococcus aureus. Benzocycloalkane-derived compounds were effective inhibitors of diapophytoene desaturase but limited by high hERG (human Ether-a-go-go Related Gene) inhibition activity. Here, we identified a new type of benzo-hepta-containing cycloalkane derivative as diapophytoene desaturase inhibitors. Among the fifty-eight analogues, 48 (hERG inhibition activity, half maximal inhibitory concentration, IC 50 , of 16.1 μM) and 51 (hERG inhibition activity, IC 50 > 40 μM) were distinguished for effectively inhibiting the pigment production of Staphylococcus aureus Newman and three methicillin-resistant Staphylococcus aureus strains, and the four strains were highly sensitize to hydrogen peroxide killing without a bactericidal growth effect. In an in vivo assay, 48 and 51 displayed a comparable effect with linezolid and vancomycin in livers and hearts in mice against Staphylococcus aureus Newman and a more considerable effect against Mu50 and NRS271 with normal administration.

Impact of Ancillary Subunits on Ventricular Repolarization

PubMed Central

Abbott, Geoffrey W.; Xu, Xianghua; Roepke, Torsten K.

2007-01-01

Voltage-gated potassium (Kv) channels generate the outward K+ ion currents that constitute the primary force in ventricular repolarization. Kv channels comprise tetramers of pore-forming α subunits and, in probably the majority of cases in vivo, ancillary or β subunits that help define the properties of the Kv current generated. Ancillary subunits can be broadly categorized as cytoplasmic or transmembrane, and can modify Kv channel trafficking, conductance, gating, ion selectivity, regulation and pharmacology. Because of their often profound effects on Kv channel function, studies of the molecular correlates of ventricular repolarization must take into account ancillary subunits as well as α subunits. Cytoplasmic ancillary subunits include the Kvβ subunits, which regulate a range of Kv channels and may link channel gating to redox potential; and the KChIPs, which appear most often associated with Kv4 subfamily channels that generate the ventricular Ito current. Transmembrane ancillary subunits include the MinK-related proteins (MiRPs) encoded by KCNE genes, which modulate members of most Kv α subunit subfamilies; and the putative 12-transmembrane domain KCR1 protein which modulates hERG. In some cases, such as the ventricular IKs channel complex, it is well-established that the KCNQ1 α subunit must co-assemble with the MinK (KCNE1) single transmembrane domain ancillary subunit for recapitulation of the characteristic, unusually slowly-activating IKs current. In other cases it is not so clear-cut, and in particular the roles of the other MinK-related proteins (MiRPs 1–4) in regulating cardiac Kv channels such as KCNQ1 and hERG in vivo are under debate. MiRP1 alters hERG function and pharmacology, and inherited MiRP1 mutations are associated with inherited and acquired arrhythmias, but controversy exists over the native role of MiRP1 in regulating hERG (and therefore ventricular IKr) in vivo. Some ancillary subunits may exhibit varied expression to shape spatial Kv current variation, e.g. KChIP2 and the epicardial-endocardial Ito current density gradient. Indeed, it is likely that most native ventricular Kv channels exhibit temporal and spatial heterogeneity of subunit composition, complicating both modeling of their functional impact on the ventricular action potential and design of specific current-targeted compounds. Here, we discuss current thinking and lines of experimentation aimed at resolving the complexities of the Kv channel complexes that repolarize the human ventricular myocardium. PMID:17993327

Cardiotoxicity screening: a review of rapid-throughput in vitro approaches.

PubMed

Li, Xichun; Zhang, Rui; Zhao, Bin; Lossin, Christoph; Cao, Zhengyu

2016-08-01

Cardiac toxicity represents one of the leading causes of drug failure along different stages of drug development. Multiple very successful pharmaceuticals had to be pulled from the market or labeled with strict usage warnings due to adverse cardiac effects. In order to protect clinical trial participants and patients, the International Conference on Harmonization published guidelines to recommend that all new drugs to be tested preclinically for hERG (Kv11.1) channel sensitivity before submitting for regulatory reviews. However, extensive studies have demonstrated that measurement of hERG activity has limitations due to the multiple molecular targets of drug compound through which it may mitigate or abolish a potential arrhythmia, and therefore, a model measuring multiple ion channel effects is likely to be more predictive. Several phenotypic rapid-throughput methods have been developed to predict the potential cardiac toxic compounds in the early stages of drug development using embryonic stem cells- or human induced pluripotent stem cell-derived cardiomyocytes. These rapid-throughput methods include microelectrode array-based field potential assay, impedance-based or Ca(2+) dynamics-based cardiomyocytes contractility assays. This review aims to discuss advantages and limitations of these phenotypic assays for cardiac toxicity assessment.

Improving the developability profile of pyrrolidine progesterone receptor partial agonists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kallander, Lara S.; Washburn, David G.; Hoang, Tram H.

2010-09-17

The previously reported pyrrolidine class of progesterone receptor partial agonists demonstrated excellent potency but suffered from serious liabilities including hERG blockade and high volume of distribution in the rat. The basic pyrrolidine amine was intentionally converted to a sulfonamide, carbamate, or amide to address these liabilities. The evaluation of the degree of partial agonism for these non-basic pyrrolidine derivatives and demonstration of their efficacy in an in vivo model of endometriosis is disclosed herein.

Discovery of 3-morpholino-imidazole[1,5-a]pyrazine BTK inhibitors for rheumatoid arthritis.

PubMed

Boga, Sobhana Babu; Alhassan, Abdul-Basit; Liu, Jian; Guiadeen, Deodial; Krikorian, Arto; Gao, Xiaolei; Wang, James; Yu, Younong; Anand, Rajan; Liu, Shilan; Yang, Chundao; Wu, Hao; Cai, Jiaqiang; Zhu, Hugh; Desai, Jagdish; Maloney, Kevin; Gao, Ying-Duo; Fischmann, Thierry O; Presland, Jeremy; Mansueto, My; Xu, Zangwei; Leccese, Erica; Knemeyer, Ian; Garlisi, Charles G; Bays, Nathan; Stivers, Peter; Brandish, Philip E; Hicks, Alexandra; Cooper, Alan; Kim, Ronald M; Kozlowski, Joseph A

2017-08-15

8-Amino-imidazo[1,5-a]pyrazine-based Bruton's tyrosine kinase (BTK) inhibitors, such as 6, exhibited potent inhibition of BTK but required improvements in both kinase and hERG selectivity (Liu et al., 2016; Gao et al., 2017). In an effort to maintain the inhibitory activity of these analogs and improve their selectivity profiles, we carried out SAR exploration of groups at the 3-position of pyrazine compound 6. This effort led to the discovery of the morpholine group as an optimized pharmacophore. Compounds 13, 23 and 38 displayed excellent BTK potencies, kinase and hERG selectivities, and pharmacokinetic profiles. Copyright © 2017 Elsevier Ltd. All rights reserved.

Digging into Lipid Membrane Permeation for Cardiac Ion Channel Blocker d-Sotalol with All-Atom Simulations

PubMed Central

DeMarco, Kevin R.; Bekker, Slava; Clancy, Colleen E.; Noskov, Sergei Y.; Vorobyov, Igor

2018-01-01

Interactions of drug molecules with lipid membranes play crucial role in their accessibility of cellular targets and can be an important predictor of their therapeutic and safety profiles. Very little is known about spatial localization of various drugs in the lipid bilayers, their active form (ionization state) or translocation rates and therefore potency to bind to different sites in membrane proteins. All-atom molecular simulations may help to map drug partitioning kinetics and thermodynamics, thus providing in-depth assessment of drug lipophilicity. As a proof of principle, we evaluated extensively lipid membrane partitioning of d-sotalol, well-known blocker of a cardiac potassium channel Kv11.1 encoded by the hERG gene, with reported substantial proclivity for arrhythmogenesis. We developed the positively charged (cationic) and neutral d-sotalol models, compatible with the biomolecular CHARMM force field, and subjected them to all-atom molecular dynamics (MD) simulations of drug partitioning through hydrated lipid membranes, aiming to elucidate thermodynamics and kinetics of their translocation and thus putative propensities for hydrophobic and aqueous hERG access. We found that only a neutral form of d-sotalol accumulates in the membrane interior and can move across the bilayer within millisecond time scale, and can be relevant to a lipophilic channel access. The computed water-membrane partitioning coefficient for this form is in good agreement with experiment. There is a large energetic barrier for a cationic form of the drug, dominant in water, to cross the membrane, resulting in slow membrane translocation kinetics. However, this form of the drug can be important for an aqueous access pathway through the intracellular gate of hERG. This route will likely occur after a neutral form of a drug crosses the membrane and subsequently re-protonates. Our study serves to demonstrate a first step toward a framework for multi-scale in silico safety pharmacology, and identifies some of the challenges that lie therein. PMID:29449809

In vivo effects of the IKr agonist NS3623 on cardiac electrophysiology of the guinea pig.

PubMed

Hansen, Rie Schultz; Olesen, Søren-Peter; Rønn, Lars Christian B; Grunnet, Morten

2008-07-01

The long QT syndrome is characterized by a prolongation of the QT interval measured on the surface electrocardiogram. Prolonging the QT interval increases the risk of dangerous ventricular fibrillations, eventually leading to sudden cardiac death. Pharmacologically induced QT interval prolongations are most often caused by antagonizing effects on the repolarizing cardiac current called IKr. In humans IKr is mediated by the human ether-a-go-go related gene (hERG) potassium channel. We recently presented NS3623, a compound that selectively activates this channel. The present study was dedicated to examining the in vivo effects of NS3623. Injection of 30 mg/kg NS3623 shortened the corrected QT interval by 25 +/- 4% in anaesthetized guinea pigs. Accordingly, 50 mg/kg of NS3623 shortened the QT interval by 30 +/- 6% in conscious guinea pigs. Finally, pharmacologically induced QT prolongation by a hERG channel antagonist (0.15 mg/kg E-4031) could be reverted by injection of NS3623 (50 mg/kg) in conscious guinea pigs. In conclusion, the present in vivo study demonstrates that injection of the hERG channel agonist NS3623 results in shortening of the QTc interval as well as reversal of a pharmacologically induced QT prolongation in both anaesthetized and conscious guinea pigs.

Tox-Database.net: a curated resource for data describing chemical triggered in vitro cardiac ion channels inhibition

PubMed Central

2012-01-01

Background Drugs safety issues are now recognized as being factors generating the most reasons for drug withdrawals at various levels of development and at the post-approval stage. Among them cardiotoxicity remains the main reason, despite the substantial effort put into in vitro and in vivo testing, with the main focus put on hERG channel inhibition as the hypothesized surrogate of drug proarrhythmic potency. The large interest in the IKr current has resulted in the development of predictive tools and informative databases describing a drug's susceptibility to interactions with the hERG channel, although there are no similar, publicly available sets of data describing other ionic currents driven by the human cardiomyocyte ionic channels, which are recognized as an overlooked drug safety target. Discussion The aim of this database development and publication was to provide a scientifically useful, easily usable and clearly verifiable set of information describing not only IKr (hERG), but also other human cardiomyocyte specific ionic channels inhibition data (IKs, INa, ICa). Summary The broad range of data (chemical space and in vitro settings) and the easy to use user interface makes tox-database.net a useful tool for interested scientists. Database URL http://tox-database.net. PMID:22947121

Brain-penetrating 2-aminobenzimidazole H(1)-antihistamines for the treatment of insomnia.

PubMed

Coon, Timothy; Moree, Wilna J; Li, Binfeng; Yu, Jinghua; Zamani-Kord, Said; Malany, Siobhan; Santos, Mark A; Hernandez, Lisa M; Petroski, Robert E; Sun, Aixia; Wen, Jenny; Sullivan, Sue; Haelewyn, Jason; Hedrick, Michael; Hoare, Samuel J; Bradbury, Margaret J; Crowe, Paul D; Beaton, Graham

2009-08-01

The benzimidazole core of the selective non-brain-penetrating H(1)-antihistamine mizolastine was used to identify a series of brain-penetrating H(1)-antihistamines for the potential treatment of insomnia. Using cassette PK studies, brain-penetrating H(1)-antihistamines were identified and in vivo efficacy was demonstrated in a rat EEG/EMG model. Further optimization focused on strategies to attenuate an identified hERG liability, leading to the discovery of 4i with a promising in vitro profile.

Simple idea to generate fragment and pharmacophore descriptors and their implications in chemical informatics.

PubMed

Catana, Cornel

2009-03-01

Using a well-defined set of fragments/pharmacophores, a new methodology to calculate fragment/ pharmacophore descriptors for any molecule onto which at least one fragment/pharmacophore can be mapped is presented. To each fragment/pharmacophore present in a molecule, we attach a descriptor that is calculated by identifying the molecule's atoms onto which it maps and summing over its constituent atomic descriptors. The attached descriptors are named C-fragment/pharmacophore descriptors, and this methodology can be applied to any descriptors defined at the atomic level, such as the partition coefficient, molar refractivity, electrotopological state, etc. By using this methodology, the same fragment/pharmacophore can be shown to have different values in different molecules resulting in better discrimination power. As we know, fragment and pharmacophore fingerprints have a lot of applications in chemical informatics. This study has attempted to find the impact of replacing the traditional value of "1" in a fingerprint with real numbers derived form C-fragment/pharmacophore descriptors. One way to do this is to assess the utility of C-fragment/ pharmacophore descriptors in modeling different end points. Here, we exemplify with data from CYP and hERG. The fact that, in many cases, the obtained models were fairly successful and C-fragment descriptors were ranked among the top ones supports the idea that they play an important role in correlation. When we modeled hERG with C-pharmacophore descriptors, however, the model performances decreased slightly, and we attribute this, mainly to the fact that there is no technique capable of handling multiple instances (states). We hope this will open new research, especially in the emerging field of machine learning. Further research is needed to see the impact of C-fragment/pharmacophore descriptors in similarity/dissimilarity applications.

The N–Terminal Tail of hERG Contains an Amphipathic α–Helix That Regulates Channel Deactivation

PubMed Central

Mobli, Mehdi; Ke, Ying; Kuchel, Philip W.; King, Glenn F.; Stock, Daniela; Vandenberg, Jamie I.

2011-01-01

The cytoplasmic N–terminal domain of the human ether–a–go–go related gene (hERG) K+ channel is critical for the slow deactivation kinetics of the channel. However, the mechanism(s) by which the N–terminal domain regulates deactivation remains to be determined. Here we show that the solution NMR structure of the N–terminal 135 residues of hERG contains a previously described Per–Arnt–Sim (PAS) domain (residues 26–135) as well as an amphipathic α–helix (residues 13–23) and an initial unstructured segment (residues 2–9). Deletion of residues 2–25, only the unstructured segment (residues 2–9) or replacement of the α–helix with a flexible linker all result in enhanced rates of deactivation. Thus, both the initial flexible segment and the α–helix are required but neither is sufficient to confer slow deactivation kinetics. Alanine scanning mutagenesis identified R5 and G6 in the initial flexible segment as critical for slow deactivation. Alanine mutants in the helical region had less dramatic phenotypes. We propose that the PAS domain is bound close to the central core of the channel and that the N–terminal α–helix ensures that the flexible tail is correctly orientated for interaction with the activation gating machinery to stabilize the open state of the channel. PMID:21249148

Inhibition of potassium currents is involved in antiarrhythmic effect of moderate ethanol on atrial fibrillation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Baode; Li, Chenxing

Excessive consumption of alcohol is a well-established risk factor of atrial fibrillation (AF). However, the effects of moderate alcohol drinking remain to be elucidated. This study was designed to determine the effects of moderate ethanol ingestion on atrial fibrillation and the electrophysiological mechanisms. In acetylcholine-induced canine and mouse AF models, the moderate ethanol prevented the generation and persistence of AF through prolonging the latent period of AF and shortening the duration of AF. The action potential duration (APD) was remarkably prolonged under the concentration range of 12.5–50.0 mM ethanol in guinea pig atrial myocytes. Ultra-rapid delayed rectified potassium currents (I{submore » Kv1.5}) were markedly inhibited by 12.5–50.0 mM ethanol in a concentration-dependent manner. Ethanol with 50.0 mM could inhibit rapid delayed rectifier potassium currents (I{sub hERG}). Ethanol under 6.25–50.0 mM did not affect on inward rectifier potassium currents (I{sub Kir2.1}). Collectively, the present study provided an evidence that moderate ethanol intake can prolong the APD of atrial myocytes by inhibition of I{sub Kv1.5} and I{sub hERG}, which contributed to preventing the development and duration of AF. - Highlights: • Moderate ethanol prevented the development of AF in animal models. • Moderate ethanol prolonged APD in guinea pig atrial myocytes. • Moderate ethanol inhibited Kv1.5 currents.« less

Inhibition of potassium currents is involved in antiarrhythmic effect of moderate ethanol on atrial fibrillation.

PubMed

Yang, Baode; Li, Chenxing; Sun, Junyi; Wang, Xinghui; Liu, Xinling; Yang, Chun; Chen, Lina; Zhou, Jun; Hu, Hao

2017-05-01

Excessive consumption of alcohol is a well-established risk factor of atrial fibrillation (AF). However, the effects of moderate alcohol drinking remain to be elucidated. This study was designed to determine the effects of moderate ethanol ingestion on atrial fibrillation and the electrophysiological mechanisms. In acetylcholine-induced canine and mouse AF models, the moderate ethanol prevented the generation and persistence of AF through prolonging the latent period of AF and shortening the duration of AF. The action potential duration (APD) was remarkably prolonged under the concentration range of 12.5-50.0mM ethanol in guinea pig atrial myocytes. Ultra-rapid delayed rectified potassium currents (I Kv1.5 ) were markedly inhibited by 12.5-50.0mM ethanol in a concentration-dependent manner. Ethanol with 50.0mM could inhibit rapid delayed rectifier potassium currents (I hERG ). Ethanol under 6.25-50.0mM did not affect on inward rectifier potassium currents (I Kir2.1 ). Collectively, the present study provided an evidence that moderate ethanol intake can prolong the APD of atrial myocytes by inhibition of I Kv1.5 and I hERG , which contributed to preventing the development and duration of AF. Copyright © 2017 Elsevier Inc. All rights reserved.

RNA interference targeting E637K mutation rescues hERG channel currents and restores its kinetic properties.

PubMed

Lu, Xiaoli; Yang, Xi; Huang, Xiaoyan; Huang, Chen; Sun, Huan Huan; Jin, Lihua; Xu, Weifeng; Mao, Haiyan; Guo, Junming; Zhou, Jianqing; Lian, Jiangfang

2013-01-01

Long QT syndrome (LQTS) is a monogenic proarrhythmic disorder that predisposes affected individuals to sudden death from tachyarrhythmia. As an inherited disease, LQTS cannot be completely cured by conventional treatment modalities. Individualized gene therapy is a promising therapeutic approach. The purpose of this study was to investigate the role of small interference RNA (siRNA) on expression of E637K-hERG (human ether-a-go-go-related gene) mutant and whether it can be used to rescue the mutant's dominant-negative suppressive effects on hERG protein channel function. Western blot was performed to select the most sensitive siRNAs to target E637K-hERG mutant knockdown. Confocal laser scanning microscope was performed to monitor cellular localization of wild-type (WT)-hERG and E637K-hERG with or without siRNA. Patch-clamp technique was used to assess the effect of siRNA on the electrophysiologic characteristics of the rapidly activating delayed rectifier K(+) current I(Kr) of the hERG protein channel. siRNA led to a significant decrease in the level of E637K-hERG protein but did not affect the level of WT-hERG protein. WT-hERG localization in cells coexpressing E637K-hERG mutant was restored to the membrane by siRNA. The siRNA-mediated inhibition of E637K-hERG mutant restored the maximum current and tail current amplitudes. Furthermore, siRNA treatment rescued the kinetic properties of WT/E637K-hERG protein channel to a level comparable to that of WT-hERG protein channel. Our findings illustrated that siRNA can effectively inhibit E637K-hERG protein expression and rescue the dominant-negative effect of this mutation by restoring the kinetic properties of hERG protein channel. It has potential clinical implications with regard to the possibility of using siRNA in the treatment of LQTS. Copyright © 2013 Heart Rhythm Society. All rights reserved.

Pharmacological and electrophysiological characterization of nine, single nucleotide polymorphisms of the hERG-encoded potassium channel

PubMed Central

Männikkö, R; Overend, G; Perrey, C; Gavaghan, CL; Valentin, J-P; Morten, J; Armstrong, M; Pollard, CE

2010-01-01

Background and purpose: Potencies of compounds blocking KV11.1 [human ether-ago-go-related gene (hERG)] are commonly assessed using cell lines expressing the Caucasian wild-type (WT) variant. Here we tested whether such potencies would be different for hERG single nucleotide polymorphisms (SNPs). Experimental approach: SNPs (R176W, R181Q, Del187-189, P347S, K897T, A915V, P917L, R1047L, A1116V) and a binding-site mutant (Y652A) were expressed in Tet-On CHO-K1 cells. Potencies [mean IC50; lower/upper 95% confidence limit (CL)] of 48 hERG blockers was estimated by automated electrophysiology [IonWorks™ HT (IW)]. In phase one, rapid potency comparison of each WT-SNP combination was made for each compound. In phase two, any compound-SNP combinations from phase one where the WT upper/lower CL did not overlap with those of the SNPs were re-examined. Electrophysiological WT and SNP parameters were determined using conventional electrophysiology. Key results: IW detected the expected sixfold potency decrease for propafenone in Y652A. In phase one, the WT lower/upper CL did not overlap with those of the SNPs for 77 compound-SNP combinations. In phase two, 62/77 cases no longer yielded IC50 values with non-overlapping CLs. For seven of the remaining 15 cases, there were non-overlapping CLs but in the opposite direction. For the eight compound-SNP combinations with non-overlapping CLs in the same direction as for phase 1, potencies were never more than twofold apart. The only statistically significant electrophysiological difference was the voltage dependence of activation of R1047L. Conclusion and implications: Potencies of hERG channel blockers defined using the Caucasian WT sequence, in this in vitro assay, were representative of potencies for common SNPs. This article is part of a themed section on QT safety. To view this issue visit http://www3.interscience.wiley.com/journal/121548564/issueyear?year=2010 PMID:19673885

Stabilization of the Activated hERG Channel Voltage Sensor by Depolarization Involves the S4-S5 Linker.

PubMed

Thouta, Samrat; Hull, Christina M; Shi, Yu Patrick; Sergeev, Valentine; Young, James; Cheng, Yen M; Claydon, Thomas W

2017-01-24

Slow deactivation of hERG channels is critical for preventing cardiac arrhythmia yet the mechanistic basis for the slow gating transition is unclear. Here, we characterized the temporal sequence of events leading to voltage sensor stabilization upon membrane depolarization. Progressive increase in step depolarization duration slowed voltage-sensor return in a biphasic manner (τ fast = 34 ms, τ slow  = 2.5 s). The faster phase of voltage-sensor return slowing correlated with the kinetics of pore opening. The slower component occurred over durations that exceeded channel activation and was consistent with voltage sensor relaxation. The S4-S5 linker mutation, G546L, impeded the faster phase of voltage sensor stabilization without attenuating the slower phase, suggesting that the S4-S5 linker is important for communications between the pore gate and the voltage sensor during deactivation. These data also demonstrate that the mechanisms of pore gate-opening-induced and relaxation-induced voltage-sensor stabilization are separable. Deletion of the distal N-terminus (Δ2-135) accelerated off-gating current, but did not influence the relative contribution of either mechanism of stabilization of the voltage sensor. Lastly, we characterized mode-shift behavior in hERG channels, which results from stabilization of activated channel states. The apparent mode-shift depended greatly on recording conditions. By measuring slow activation and deactivation at steady state we found the "true" mode-shift to be ∼15 mV. Interestingly, the "true" mode-shift of gating currents was ∼40 mV, much greater than that of the pore gate. This demonstrates that voltage sensor return is less energetically favorable upon repolarization than pore gate closure. We interpret this to indicate that stabilization of the activated voltage sensor limits the return of hERG channels to rest. The data suggest that this stabilization occurs as a result of reconfiguration of the pore gate upon opening by a mechanism that is influenced by the S4-S5 linker, and by a separable voltage-sensor intrinsic relaxation mechanism. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

PubMed

Mirams, Gary R; Davies, Mark R; Brough, Stephen J; Bridgland-Taylor, Matthew H; Cui, Yi; Gavaghan, David J; Abi-Gerges, Najah

2014-01-01

Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Pharmacological profile of DA-6886, a novel 5-HT4 receptor agonist to accelerate colonic motor activity in mice.

PubMed

Lee, Min Jung; Cho, Kang Hun; Park, Hyun Min; Sung, Hyun Jung; Choi, Sunghak; Im, Weonbin

2014-07-15

DA-6886, the gastrointestinal prokinetic benzamide derivative is a novel 5-HT4 receptor agonist being developed for the treatment of constipation-predominant irritable bowel syndrome (IBS-C). The purpose of this study was to characterize in vitro and in vivo pharmacological profile of DA-6886. We used various receptor binding assay, cAMP accumulation assay, organ bath experiment and colonic transit assay in normal and chemically constipated mice. DA-6886 exhibited high affinity and selectivity to human 5-HT4 receptor splice variants, with mean pKi of 7.1, 7.5, 7.9 for the human 5-HT4a, 5-HT4b and 5-HT4d, respectively. By contrast, DA-6886 did not show significant affinity for several receptors including dopamine D2 receptor, other 5-HT receptors except for 5-HT2B receptor (pKi value of 6.2). The affinity for 5-HT4 receptor was translated into functional agonist activity in Cos-7 cells expressing 5-HT4 receptor splice variants. Furthermore, DA-6886 induced relaxation of the rat oesophagus preparation (pEC50 value of 7.4) in a 5-HT4 receptor antagonist-sensitive manner. The evaluation of DA-6886 in CHO cells expressing hERG channels revealed that it inhibited hERG channel current with an pIC50 value of 4.3, indicating that the compound was 1000-fold more selective for the 5-HT4 receptor over hERG channels. In the normal ICR mice, oral administration of DA-6886 (0.4 and 2mg/kg) resulted in marked stimulation of colonic transit. Furthermore, in the loperamide-induced constipation mouse model, 2mg/kg of DA-6886 significantly improved the delay of colonic transit, similar to 10mg/kg of tegaserod. Taken together, DA-6886 is a highly potent and selective 5-HT4 receptor agonist to accelerate colonic transit in mice, which might be therapeutic agent having a favorable safety profile in the treatment of gastrointestinal motor disorders such as IBS-C and chronic constipation. Copyright © 2014 Elsevier B.V. All rights reserved.

QT prolongation and proarrhythmia by moxifloxacin: concordance of preclinical models in relation to clinical outcome

PubMed Central

Chen, Xian; Cass, Jessica D; Bradley, Jenifer A; Dahm, Corinn M; Sun, Zhuoqian; Kadyszewski, Edmund; Engwall, Michael J; Zhou, Jun

2005-01-01

Moxifloxacin, a fluoroquinolone antibiotic associated with QT prolongation, has been recommended as a positive control by regulatory authorities to evaluate the sensitivity of both clinical and preclinical studies to detect small but significant increases in QT interval measurements. In this study, we investigated effects of moxifloxacin on the hERG current in HEK-293 cells, electrocardiograms in conscious telemetered dogs, and repolarization parameters and arrhythmogenic potentials in the arterially perfused rabbit ventricular wedge model. Moxifloxacin inhibited the hERG current with an IC50 of 35.7 μM. In conscious telemetered dogs, moxifloxacin significantly prolonged QTc at 30 and 90 mg kg−1, with mean serum Cmax of 8.52 and 22.3 μg ml−1, respectively. In the wedge preparation, moxifloxacin produced a concentration-dependent prolongation of the action potential duration, QT interval, and the time between peak and end of the T wave, an indicator for transmural dispersion of repolarization. Phase 2 early after-depolarizations were observed in one of five experiments at 30 μM and five of five experiments at 100 μM. The arrhythmogenic potential was also concentration-dependent, and 100 μM (∼18-fold above the typical unbound Cmax exposure in clinical usage) appeared to have a high risk of inducing torsade de pointes (TdP). Our data indicated a good correlation among the concentration–response relationships in the three preclinical models and with the available clinical data. The lack of TdP report by moxifloxacin in patients without other risk factors might be attributable to its well-behaved pharmacokinetic profile and other dose-limiting effects. PMID:16158069

High-frequency autonomic modulation: a new model for analysis of autonomic cardiac control.

PubMed

Champéroux, Pascal; Fesler, Pierre; Judé, Sebastien; Richard, Serge; Le Guennec, Jean-Yves; Thireau, Jérôme

2018-05-03

Increase in high-frequency beat-to-beat heart rate oscillations by torsadogenic hERG blockers appears to be associated with signs of parasympathetic and sympathetic co-activation which cannot be assessed directly using classic methods of heart rate variability analysis. The present work aimed to find a translational model that would allow this particular state of the autonomic control of heart rate to be assessed. High-frequency heart rate and heart period oscillations were analysed within discrete 10 s intervals in a cohort of 200 healthy human subjects. Results were compared to data collected in non-human primates and beagle dogs during pharmacological challenges and torsadogenic hERG blockers exposure, in 127 genotyped LQT1 patients on/off β-blocker treatment and in subgroups of smoking and non-smoking subjects. Three states of autonomic modulation, S1 (parasympathetic predominance) to S3 (reciprocal parasympathetic withdrawal/sympathetic activation), were differentiated to build a new model of heart rate variability referred to as high-frequency autonomic modulation. The S2 state corresponded to a specific state during which both parasympathetic and sympathetic systems were coexisting or co-activated. S2 oscillations were proportionally increased by torsadogenic hERG-blocking drugs, whereas smoking caused an increase in S3 oscillations. The combined analysis of the magnitude of high-frequency heart rate and high-frequency heart period oscillations allows a refined assessment of heart rate autonomic modulation applicable to long-term ECG recordings and offers new approaches to assessment of the risk of sudden death both in terms of underlying mechanisms and sensitivity. © 2018 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.

NMR solution structure of the N-terminal domain of hERG and its interaction with the S4-S5 linker

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Qingxin; Gayen, Shovanlal; Chen, Angela Shuyi

Research highlights: {yields} The N-terminal domain (NTD, eag domain) containing 135 residues of hERG was expressed and purified from E. coli cells. {yields} Solution structure of NTD was determined with NMR spectroscopy. {yields} The alpha-helical region (residues 13-23) was demonstrated to possess the characteristics of an amphipathic helix. {yields} NMR titration confirmed the interaction between NTD and the peptide from the S4-S5 linker. -- Abstract: The human Ether-a-go-go Related Gene (hERG) potassium channel mediates the rapid delayed rectifier current (IKr) in the cardiac action potential. Mutations in the 135 amino acid residue N-terminal domain (NTD) cause channel dysfunction or mis-translocation.more » To study the structure of NTD, it was overexpressed and purified from Escherichia coli cells using affinity purification and gel filtration chromatography. The purified protein behaved as a monomer under purification conditions. Far- and near-UV, circular dichroism (CD) and solution nuclear magnetic resonance (NMR) studies showed that the purified protein was well-folded. The solution structure of NTD was obtained and the N-terminal residues 13-23 forming an amphipathic helix which may be important for the protein-protein or protein-membrane interactions. NMR titration experiment also demonstrated that residues from 88 to 94 in NTD are important for the molecular interaction with the peptide derived from the S4-S5 linker.« less

Toward Personalized Medicine: Using Cardiomyocytes Differentiated From Urine-Derived Pluripotent Stem Cells to Recapitulate Electrophysiological Characteristics of Type 2 Long QT Syndrome.

PubMed

Jouni, Mariam; Si-Tayeb, Karim; Es-Salah-Lamoureux, Zeineb; Latypova, Xenia; Champon, Benoite; Caillaud, Amandine; Rungoat, Anais; Charpentier, Flavien; Loussouarn, Gildas; Baró, Isabelle; Zibara, Kazem; Lemarchand, Patricia; Gaborit, Nathalie

2015-09-01

Human genetically inherited cardiac diseases have been studied mainly in heterologous systems or animal models, independent of patients' genetic backgrounds. Because sources of human cardiomyocytes (CMs) are extremely limited, the use of urine samples to generate induced pluripotent stem cell-derived CMs would be a noninvasive method to identify cardiac dysfunctions that lead to pathologies within patients' specific genetic backgrounds. The objective was to validate the use of CMs differentiated from urine-derived human induced pluripotent stem (UhiPS) cells as a new cellular model for studying patients' specific arrhythmia mechanisms. Cells obtained from urine samples of a patient with long QT syndrome who harbored the HERG A561P gene mutation and his asymptomatic noncarrier mother were reprogrammed using the episomal-based method. UhiPS cells were then differentiated into CMs using the matrix sandwich method.UhiPS-CMs showed proper expression of atrial and ventricular myofilament proteins and ion channels. They were electrically functional, with nodal-, atrial- and ventricular-like action potentials recorded using high-throughput optical and patch-clamp techniques. Comparison of HERG expression from the patient's UhiPS-CMs to the mother's UhiPS-CMs showed that the mutation led to a trafficking defect that resulted in reduced delayed rectifier K(+) current (IKr). This phenotype gave rise to action potential prolongation and arrhythmias. UhiPS cells from patients carrying ion channel mutations can be used as novel tools to differentiate functional CMs that recapitulate cardiac arrhythmia phenotypes. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

SuperPain—a resource on pain-relieving compounds targeting ion channels

PubMed Central

Gohlke, Björn O.; Preissner, Robert; Preissner, Saskia

2014-01-01

Pain is more than an unpleasant sensory experience associated with actual or potential tissue damage: it is the most common reason for physician consultation and often dramatically affects quality of life. The management of pain is often difficult and new targets are required for more effective and specific treatment. SuperPain (http://bioinformatics.charite.de/superpain/) is freely available database for pain-stimulating and pain-relieving compounds, which bind or potentially bind to ion channels that are involved in the transmission of pain signals to the central nervous system, such as TRPV1, TRPM8, TRPA1, TREK1, TRESK, hERG, ASIC, P2X and voltage-gated sodium channels. The database consists of ∼8700 ligands, which are characterized by experimentally measured binding affinities. Additionally, 100 000 putative ligands are included. Moreover, the database provides 3D structures of receptors and predicted ligand-binding poses. These binding poses and a structural classification scheme provide hints for the design of new analgesic compounds. A user-friendly graphical interface allows similarity searching, visualization of ligands docked into the receptor, etc. PMID:24271391

SuperPain--a resource on pain-relieving compounds targeting ion channels.

PubMed

Gohlke, Björn O; Preissner, Robert; Preissner, Saskia

2014-01-01

Pain is more than an unpleasant sensory experience associated with actual or potential tissue damage: it is the most common reason for physician consultation and often dramatically affects quality of life. The management of pain is often difficult and new targets are required for more effective and specific treatment. SuperPain (http://bioinformatics.charite.de/superpain/) is freely available database for pain-stimulating and pain-relieving compounds, which bind or potentially bind to ion channels that are involved in the transmission of pain signals to the central nervous system, such as TRPV1, TRPM8, TRPA1, TREK1, TRESK, hERG, ASIC, P2X and voltage-gated sodium channels. The database consists of ∼8700 ligands, which are characterized by experimentally measured binding affinities. Additionally, 100 000 putative ligands are included. Moreover, the database provides 3D structures of receptors and predicted ligand-binding poses. These binding poses and a structural classification scheme provide hints for the design of new analgesic compounds. A user-friendly graphical interface allows similarity searching, visualization of ligands docked into the receptor, etc.

Determination of myocardium to plasma concentration ratios of five antipsychotic drugs: comparison with their ability to induce arrhythmia and sudden death in clinical practice.

PubMed

Titier, Karine; Canal, Mireille; Déridet, Evelyne; Abouelfath, Abdelilah; Gromb, Sophie; Molimard, Mathieu; Moore, Nicholas

2004-08-15

Reviewing available data shows that most of antipsychotic drugs are associated with arrhythmia and sudden death. Experimental studies have shown a HERG channel blockade, a dose-dependent increase in duration of action potential or of QT interval, with various degrees of indicators of serious arrhythmogenicity. However, it seems difficult to relate these in vitro and in vivo preclinical models to clinical findings, in part, because the relationship between concentrations used and in vivo tissue concentrations during treatment in man is not known. Consequently, we established the myocardium to plasma concentration ratios for a series of antipsychotic drugs by intraperitoneal administration of different level doses to the guinea pig. Then, we compared these values to their ability to induce arrhythmia or torsade de pointes in clinical practice. The myocardium to plasma concentration ratios were 2.2 for clozapine, 2.7 for olanzapine, 3.1 for sertindole, 4.5 for risperidone, and 6.4 for haloperidol. These data suggest that when the ratio is higher than 4, arrhythmia and sudden death may be expected. On the contrary, when the ratio is less than 3, little effect may be predicted. These results underscore the importance of interpreting HERG channel data and electrophysiological data in the context of other pharmacokinetic parameters such as myocardium to plasma distribution.

Acute alteration of cardiac ECG, action potential, I{sub Kr} and the human ether-a-go-go-related gene (hERG) K{sup +} channel by PCB 126 and PCB 77

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, Mi-Hyeong; Park, Won Sun; Jo, Su-Hyun, E-mail: suhyunjo@kangwon.ac.kr

2012-07-01

Polychlorinated biphenyls (PCBs) have been known as serious persistent organic pollutants (POPs), causing developmental delays and motor dysfunction. We have investigated the effects of two PCB congeners, 3,3′,4,4′-tetrachlorobiphenyl (PCB 77) and 3,3′,4,4′,5-pentachlorobiphenyl (PCB 126) on ECG, action potential, and the rapidly activating delayed rectifier K{sup +} current (I{sub Kr}) of guinea pigs' hearts, and hERG K{sup +} current expressed in Xenopus oocytes. PCB 126 shortened the corrected QT interval (QTc) of ECG and decreased the action potential duration at 90% (APD{sub 90}), and 50% of repolarization (APD{sub 50}) (P < 0.05) without changing the action potential duration at 20% (APD{submore » 20}). PCB 77 decreased APD{sub 20} (P < 0.05) without affecting QTc, APD{sub 90}, and APD{sub 50}. The PCB 126 increased the I{sub Kr} in guinea-pig ventricular myocytes held at 36 °C and hERG K{sup +} current amplitude at the end of the voltage steps in voltage-dependent mode (P < 0.05); however, PCB 77 did not change the hERG K{sup +} current amplitude. The PCB 77 increased the diastolic Ca{sup 2+} and decreased Ca{sup 2+} transient amplitude (P < 0.05), however PCB 126 did not change. The results suggest that PCB 126 shortened the QTc and decreased the APD{sub 90} possibly by increasing I{sub Kr}, while PCB 77 decreased the APD{sub 20} possibly by other modulation related with intracellular Ca{sup 2+}. The present data indicate that the environmental toxicants, PCBs, can acutely affect cardiac electrophysiology including ECG, action potential, intracellular Ca{sup 2+}, and channel activity, resulting in toxic effects on the cardiac function in view of the possible accumulation of the PCBs in human body. -- Highlights: ► PCBs are known as serious environmental pollutants and developmental disruptors. ► PCB 126 shortened QT interval of ECG and action potential duration. ► PCB 126 increased human ether-a-go-go-related K{sup +} current and I{sub Kr}. ► PCB 77 decreased action potential duration and increased intracellular Ca{sup 2+} content. ► PCBs acutely change cardiac electrophysiology and rhythmicity.« less

Screening drug-induced arrhythmia [corrected] using human induced pluripotent stem cell-derived cardiomyocytes and low-impedance microelectrode arrays.

PubMed

Navarrete, Enrique G; Liang, Ping; Lan, Feng; Sanchez-Freire, Verónica; Simmons, Chelsey; Gong, Tingyu; Sharma, Arun; Burridge, Paul W; Patlolla, Bhagat; Lee, Andrew S; Wu, Haodi; Beygui, Ramin E; Wu, Sean M; Robbins, Robert C; Bers, Donald M; Wu, Joseph C

2013-09-10

Drug-induced arrhythmia is one of the most common causes of drug development failure and withdrawal from market. This study tested whether human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) combined with a low-impedance microelectrode array (MEA) system could improve on industry-standard preclinical cardiotoxicity screening methods, identify the effects of well-characterized drugs, and elucidate underlying risk factors for drug-induced arrhythmia. hiPSC-CMs may be advantageous over immortalized cell lines because they possess similar functional characteristics as primary human cardiomyocytes and can be generated in unlimited quantities. Pharmacological responses of beating embryoid bodies exposed to a comprehensive panel of drugs at 65 to 95 days postinduction were determined. Responses of hiPSC-CMs to drugs were qualitatively and quantitatively consistent with the reported drug effects in literature. Torsadogenic hERG blockers, such as sotalol and quinidine, produced statistically and physiologically significant effects, consistent with patch-clamp studies, on human embryonic stem cell-derived cardiomyocytes hESC-CMs. False-negative and false-positive hERG blockers were identified accurately. Consistent with published studies using animal models, early afterdepolarizations and ectopic beats were observed in 33% and 40% of embryoid bodies treated with sotalol and quinidine, respectively, compared with negligible early afterdepolarizations and ectopic beats in untreated controls. We found that drug-induced arrhythmias can be recapitulated in hiPSC-CMs and documented with low impedance MEA. Our data indicate that the MEA/hiPSC-CM assay is a sensitive, robust, and efficient platform for testing drug effectiveness and for arrhythmia screening. This system may hold great potential for reducing drug development costs and may provide significant advantages over current industry standard assays that use immortalized cell lines or animal models.

New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies.

PubMed

Farid, Ramy; Day, Tyler; Friesner, Richard A; Pearlstein, Robert A

2006-05-01

We created a homology model of the homo-tetrameric pore domain of HERG using the crystal structure of the bacterial potassium channel, KvAP, as a template. We docked a set of known blockers with well-characterized effects on channel function into the lumen of the pore between the selectivity filter and extracellular entrance using a novel docking and refinement procedure incorporating Glide and Prime. Key aromatic groups of the blockers are predicted to form multiple simultaneous ring stacking and hydrophobic interactions among the eight aromatic residues lining the pore. Furthermore, each blocker can achieve these interactions via multiple docking configurations. To further interpret the docking results, we mapped hydrophobic and hydrophilic potentials within the lumen of each refined docked complex. Hydrophilic iso-potential contours define a 'propeller-shaped' volume at the selectivity filter entrance. Hydrophobic contours define a hollow 'crown-shaped' volume located above the 'propeller', whose hydrophobic 'rim' extends along the pore axis between Tyr652 and Phe656. Blockers adopt conformations/binding orientations that closely mimic the shapes and properties of these contours. Blocker basic groups are localized in the hydrophilic 'propeller', forming electrostatic interactions with Ser624 rather than a generally accepted pi-cation interaction with Tyr652. Terfenadine, cisapride, sertindole, ibutilide, and clofilium adopt similar docked poses, in which their N-substituents bridge radially across the hollow interior of the 'crown' (analogous to the hub and spokes of a wheel), and project aromatic/hydrophobic portions into the hydrophobic 'rim'. MK-499 docks with its longitudinal axis parallel to the axis of the pore and 'crown', and its hydrophobic groups buried within the hydrophobic 'rim'.

The Large Area Radio Galaxy Evolution Spectroscopic Survey (LARGESS): survey design, data catalogue and GAMA/WiggleZ spectroscopy

NASA Astrophysics Data System (ADS)

Ching, John H. Y.; Sadler, Elaine M.; Croom, Scott M.; Johnston, Helen M.; Pracy, Michael B.; Couch, Warrick J.; Hopkins, A. M.; Jurek, Russell J.; Pimbblet, K. A.

2017-01-01

We present the Large Area Radio Galaxy Evolution Spectroscopic Survey (LARGESS), a spectroscopic catalogue of radio sources designed to include the full range of radio AGN populations out to redshift z ˜ 0.8. The catalogue covers ˜800 deg2 of sky, and provides optical identifications for 19 179 radio sources from the 1.4 GHz Faint Images of the Radio Sky at Twenty-cm (FIRST) survey down to an optical magnitude limit of Imod < 20.5 in Sloan Digital Sky Survey (SDSS) images. Both galaxies and point-like objects are included, and no colour cuts are applied. In collaboration with the WiggleZ and Galaxy And Mass Assembly (GAMA) spectroscopic survey teams, we have obtained new spectra for over 5000 objects in the LARGESS sample. Combining these new spectra with data from earlier surveys provides spectroscopic data for 12 329 radio sources in the survey area, of which 10 856 have reliable redshifts. 85 per cent of the LARGESS spectroscopic sample are radio AGN (median redshift z = 0.44), and 15 per cent are nearby star-forming galaxies (median z = 0.08). Low-excitation radio galaxies (LERGs) comprise the majority (83 per cent) of LARGESS radio AGN at z < 0.8, with 12 per cent being high-excitation radio galaxies (HERGs) and 5 per cent radio-loud QSOs. Unlike the more homogeneous LERG and QSO sub-populations, HERGs are a heterogeneous class of objects with relatively blue optical colours and a wide dispersion in mid-infrared colours. This is consistent with a picture in which most HERGs are hosted by galaxies with recent or ongoing star formation as well as a classical accretion disc.

A novel dendritic nanocarrier of polyamidoamine-polyethylene glycol-cyclic RGD for “smart” small interfering RNA delivery and in vitro antitumor effects by human ether-à-go-go-related gene silencing in anaplastic thyroid carcinoma cells

PubMed Central

Li, Guanhua; Hu, Zuojun; Yin, Henghui; Zhang, Yunjian; Huang, Xueling; Wang, Shenming; Li, Wen

2013-01-01

The application of RNA interference techniques is promising in gene therapeutic approaches, especially for cancers. To improve safety and efficiency of small interfering RNA (siRNA) delivery, a triblock dendritic nanocarrier, polyamidoamine-polyethylene glycol-cyclic RGD (PAMAM-PEG-cRGD), was developed and studied as an siRNA vector targeting the human ether-à-go-go-related gene (hERG) in human anaplastic thyroid carcinoma cells. Structure characterization, particle size, zeta potential, and gel retardation assay confirmed that complete triblock components were successfully synthesized with effective binding capacity of siRNA in this triblock nanocarrier. Cytotoxicity data indicated that conjugation of PEG significantly alleviated cytotoxicity when compared with unmodified PAMAM. PAMAM-PEG-cRGD exerted potent siRNA cellular internalization in which transfection efficiency measured by flow cytometry was up to 68% when the charge ratio (N/P ratio) was 3.5. Ligand-receptor affinity together with electrostatic interaction should be involved in the nano-siRNA endocytosis mechanism and we then proved that attachment of cRGD enhanced cellular uptake via RGD-integrin recognition. Gene silencing was evaluated by reverse transcription polymerase chain reaction and PAMAM-PEG-cRGD-siRNA complex downregulated the expression of hERG to 26.3% of the control value. Furthermore, gene knockdown of hERG elicited growth suppression as well as activated apoptosis by means of abolishing vascular endothelial growth factor secretion and triggering caspase-3 cascade in anaplastic thyroid carcinoma cells. Our study demonstrates that this novel triblock polymer, PAMAM-PEG-cRGD, exhibits negligible cytotoxicity, effective transfection, “smart” cancer targeting, and therefore is a promising siRNA nanocarrier. PMID:23569377

Functional characterization of a novel hERG variant in a family with recurrent sudden infant death syndrome: Retracting a genetic diagnosis.

PubMed

Sergeev, Valentine; Perry, Frances; Roston, Thomas M; Sanatani, Shubhayan; Tibbits, Glen F; Claydon, Thomas W

2018-03-01

Long QT syndrome (LQTS) is the most common cardiac ion channelopathy and has been found to be responsible for approximately 10% of sudden infant death syndrome (SIDS) cases. Despite increasing use of broad panels and now whole exome sequencing (WES) in the investigation of SIDS, the probability of identifying a pathogenic mutation in a SIDS victim is low. We report a family-based study who are afflicted by recurrent SIDS in which several members harbor a variant, p.Pro963Thr, in the C-terminal region of the human-ether-a-go-go (hERG) gene, published to be responsible for cases of LQTS type 2. Functional characterization was undertaken due to the variable phenotype in carriers, the discrepancy with published cases, and the importance of identifying a cause for recurrent deaths in a single family. Studies of the mutated ion channel in in vitro heterologous expression systems revealed that the mutation has no detectable impact on membrane surface expression, biophysical gating properties such as activation, deactivation and inactivation, or the amplitude of the protective current conducted by hERG channels during early repolarization. These observations suggest that the p.Pro963Thr mutation is not a monogenic disease-causing LQTS mutation despite evidence of co-segregation in two siblings affected by SIDS. Our findings demonstrate some of the potential pitfalls in post-mortem molecular testing and the importance of functional testing of gene variants in determining disease-causation, especially where the impacts of cascade screening can affect multiple generations. Copyright © 2017 Elsevier B.V. All rights reserved.

Synthesis and biological evaluation of pyrrolidine derivatives as novel and potent sodium channel blockers for the treatment of ischemic stroke.

PubMed

Seki, Maki; Tsuruta, Osamu; Tatsumi, Ryo; Soejima, Aki

2013-07-15

A novel series of pyrrolidine derivatives as Na(+) channel blockers was synthesized and evaluated for their inhibitory effects on neuronal Na(+) channels. Structure-activity relationship (SAR) studies of a pyrrolidine analogue 2 led to the discovery of 5e as a potent Na(+) channel blocker with a low inhibitory action against human ether-a-go-go-related gene (hERG) channels. Compound 5e showed remarkably neuroprotective activity in a rat transient middle cerebral artery occlusion (MCAO) model, suggesting that 5e would act as a neuroprotectant for ischemic stroke. Copyright © 2013 Elsevier Ltd. All rights reserved.

Novel Inhibitors of Staphyloxanthin Virulence Factor in Comparison with Linezolid and Vancomycin versus Methicillin-Resistant, Linezolid-Resistant, and Vancomycin-Intermediate Staphylococcus aureus Infections in Vivo.

PubMed

Ni, Shuaishuai; Wei, Hanwen; Li, Baoli; Chen, Feifei; Liu, Yifu; Chen, Wenhua; Xu, Yixiang; Qiu, Xiaoxia; Li, Xiaokang; Lu, Yanli; Liu, Wenwen; Hu, Linhao; Lin, Dazheng; Wang, Manjiong; Zheng, Xinyu; Mao, Fei; Zhu, Jin; Lan, Lefu; Li, Jian

2017-10-12

Our previous work ( Wang et al. J. Med. Chem. 2016 , 59 , 4831 - 4848 ) revealed that effective benzocycloalkane-derived staphyloxanthin inhibitors against methicillin-resistant Staphylococcus aureus (S. aureus) infections were accompanied by poor water solubility and high hERG inhibition and dosages (preadministration). In this study, 92 chroman and coumaran derivatives as novel inhibitors have been addressed for overcoming deficiencies above. Derivatives 69 and 105 displayed excellent pigment inhibitory activities and low hERG inhibition, along with improvement of solubility by salt type selection. The broad and significantly potent antibacterial spectra of 69 and 105 were displayed first with normal administration in the livers and hearts in mice against pigmented S. aureus Newman, Mu50 (vancomycin-intermediate S. aureus), and NRS271 (linezolid-resistant S. aureus), compared with linezolid and vancomycin. In summary, both 69 and 105 have the potential to be developed as good antibacterial candidates targeting virulence factors.

A novel predictive pharmacokinetic/pharmacodynamic model of repolarization prolongation derived from the effects of terfenadine, cisapride and E-4031 in the conscious chronic av node--ablated, His bundle-paced dog.

PubMed

Nolan, Emily R; Feng, Meihua Rose; Koup, Jeffrey R; Liu, Jing; Turluck, Daniel; Zhang, Yiqun; Paulissen, Jerome B; Olivier, N Bari; Miller, Teresa; Bailie, Marc B

2006-01-01

Terfenadine, cisapride, and E-4031, three drugs that prolong ventricular repolarization, were selected to evaluate the sensitivity of the conscious chronic atrioventricular node--ablated, His bundle-paced Dog for defining drug induced cardiac repolarization prolongation. A novel predictive pharmacokinetic/pharmacodynamic model of repolarization prolongation was generated from these data. Three male beagle dogs underwent radiofrequency AV nodal ablation, and placement of a His bundle-pacing lead and programmable pacemaker under anesthesia. Each dog was restrained in a sling for a series of increasing dose infusions of each drug while maintained at a constant heart rate of 80 beats/min. RT interval, a surrogate for QT interval in His bundle-paced dogs, was recorded throughout the experiment. E-4031 induced a statistically significant RT prolongation at the highest three doses. Cisapride resulted in a dose-dependent increase in RT interval, which was statistically significant at the two highest doses. Terfenadine induced a dose-dependent RT interval prolongation with a statistically significant change occurring only at the highest dose. The relationship between drug concentration and RT interval change was described by a sigmoid E(max) model with an effect site. Maximum RT change (E(max)), free drug concentration at half of the maximum effect (EC(50)), and free drug concentration associated with a 10 ms RT prolongation (EC(10 ms)) were estimated. A linear correlation between EC(10 ms) and HERG IC(50) values was identified. The conscious dog with His bundle-pacing detects delayed cardiac repolarization related to I(Kr) inhibition, and detects repolarization change induced by drugs with activity at multiple ion channels. A clinically relevant sensitivity and a linear correlation with in vitro HERG data make the conscious His bundle-paced dog a valuable tool for detecting repolarization effect of new chemical entities.

NS1643 Interacts around L529 of hERG to Alter Voltage Sensor Movement on the Path to Activation

PubMed Central

Guo, Jiqing; Cheng, Yen May; Lees-Miller, James P.; Perissinotti, Laura L.; Claydon, Tom W.; Hull, Christina M.; Thouta, Samrat; Roach, Daniel E.; Durdagi, Serdar; Noskov, Sergei Y.; Duff, Henry J.

2015-01-01

Activators of hERG1 such as NS1643 are being developed for congenital/acquired long QT syndrome. Previous studies identify the neighborhood of L529 around the voltage-sensor as a putative interacting site for NS1643. With NS1643, the V1/2 of activation of L529I (−34 ± 4 mV) is similar to wild-type (WT) (−37 ± 3 mV; P > 0.05). WT and L529I showed no difference in the slope factor in the absence of NS1643 (8 ± 0 vs. 9 ± 0) but showed a difference in the presence of NS1643 (9 ± 0.3 vs. 22 ± 1; P < 0.01). Voltage-clamp-fluorimetry studies also indicated that in L529I, NS1643 reduces the voltage-sensitivity of S4 movement. To further assess mechanism of NS1643 action, mutations were made in this neighborhood. NS1643 shifts the V1/2 of activation of both K525C and K525C/L529I to hyperpolarized potentials (−131 ± 4 mV for K525C and −120 ± 21 mV for K525C/L529I). Both K525C and K525C/K529I had similar slope factors in the absence of NS1643 (18 ± 2 vs. 34 ± 5, respectively) but with NS1643, the slope factor of K525C/L529I increased from 34 ± 5 to 71 ± 10 (P < 0.01) whereas for K525C the slope factor did not change (18 ± 2 at baseline and 16 ± 2 for NS1643). At baseline, K525R had a slope factor similar to WT (9 vs. 8) but in the presence of NS1643, the slope factor of K525R was increased to 24 ± 4 vs. 9 ± 0 mV for WT (P < 0.01). Molecular modeling indicates that L529I induces a kink in the S4 voltage-sensor helix, altering a salt-bridge involving K525. Moreover, docking studies indicate that NS1643 binds to the kinked structure induced by the mutation with a higher affinity. Combining biophysical, computational, and electrophysiological evidence, a mechanistic principle governing the action of some activators of hERG1 channels is proposed. PMID:25809253

Identification and characterization of amino-piperidinequinolones and quinazolinones as MCHr1 antagonists.

PubMed

Blackburn, Christopher; LaMarche, Matthew J; Brown, James; Che, Jennifer Lee; Cullis, Courtney A; Lai, Sujen; Maguire, Martin; Marsilje, Thomas; Geddes, Bradley; Govek, Elizabeth; Kadambi, Vivek; Doherty, Colleen; Dayton, Brian; Brodjian, Sevan; Marsh, Kennan C; Collins, Christine A; Kym, Philip R

2006-05-15

Several potent, functionally active MCHr1 antagonists derived from quinolin-2(1H)-ones and quinazoline-2(1H)-ones have been synthesized and evaluated. Pyridylmethyl substitution at the quinolone 1-position results in derivatives with low-nM binding potency and good selectivity with respect to hERG binding.

Molecular basis of slow activation of the human ether-á-go-go related gene potassium channel

PubMed Central

Subbiah, Rajesh N; Clarke, Catherine E; Smith, David J; Zhao, JingTing; Campbell, Terence J; Vandenberg, Jamie I

2004-01-01

The human ether-á-go-go related gene (HERG) encodes the pore forming α-subunit of the rapid delayed rectifier K+ channel which is central to the repolarization phase of the cardiac action potential. HERG K+ channels have unusual kinetics characterized by slow activation and deactivation, yet rapid inactivation. The fourth transmembrane domain (S4) of HERG, like other voltage-gated K+ channels, contains multiple positive charges and is the voltage sensor for activation. In this study, we mutated each of the positively charged residues in this region to glutamine (Q), expressed the mutant and wild-type (WT) channels in Xenopus laevis oocytes and studied them using two-electrode voltage clamp methods. K525Q channels activated at more hyperpolarized potentials than WT, whereas all the other mutant channels activated at more depolarized potentials. All mutants except for R531Q also had a reduction in apparent gating charge associated with activation. Mutation of K525 to cysteine (C) resulted in a less dramatic phenotype than K525Q. The addition of the positively charged MTSET to K525C altered the phenotype to one more similar to K525Q than to WT. Therefore it is not charge per se, but the specific lysine side chain at position 525, that is crucial for stabilizing the closed state. When rates of activation and deactivation for WT and mutant channels were compared at equivalent total (chemical + electrostatic) driving forces, K525Q and R528Q accelerated activation but had no effect on deactivation, R531Q slowed activation and deactivation, R534Q accelerated activation but slowed deactivation and R537Q accelerated deactivation but had no effect on activation. The main conclusions we can draw from these data are that in WT channels K525 stabilizes the closed state, R531 stabilizes the open state and R534 participates in interactions that stabilize pre-open closed states. PMID:15181157

Mitigating prolonged QT interval in cancer nanodrug development for accelerated clinical translation.

PubMed

Ranjan, Amalendu P; Mukerjee, Anindita; Helson, Lawrence; Vishwanatha, Jamboor K

2013-12-14

Cardiac toxicity is the foremost reason for drug discontinuation from development to clinical evaluation and post market surveillance [Fung 35:293-317, 2001; Piccini 158:317-326 2009]. The Food and Drug Administration (FDA) has rejected many potential pharmaceutical agents due to QT prolongation effects. Since drug development and FDA approval takes an enormous amount of time, money and effort with high failure rates, there is an increased focus on rescuing drugs that cause QT prolongation. If these otherwise safe and potent drugs were formulated in a unique way so as to mitigate the QT prolongation associated with them, these potent drugs may get FDA approval for clinical use. Rescuing these compounds not only benefit the patients who need them but also require much less time and money thus leading to faster clinical translation. In this study, we chose curcumin as our drug of choice since it has been shown to posses anti-tumor properties against various cancers with limited toxicity. The major limitations with this pharmacologically active drug are (a) its ability to prolong QT by inhibiting the hERG channel and (b) its low bioavailability. In our previous studies, we found that lipids have protective actions against hERG channel inhibition and therefore QT prolongation. Results of the manual patch clamp assay of HEK 293 cells clearly illustrated that our hybrid nanocurcumin formulation prevented the curcumin induced inhibition of hERG K+ channel at concentrations higher than the therapeutic concentrations of curcumin. Comparing the percent inhibition, the hybrid nanocurcumin limited inhibition to 24.8% at a high curcumin equivalent concentration of 18 μM. Liposomal curcumin could only decrease this inhibition upto 30% only at lower curcumin concentration of 6 μM but not at 18 μM concentration. Here we show a curcumin encapsulated lipopolymeric hybrid nanoparticle formulation which could protect against QT prolongation and also render increased bioavailability and stability thereby overcoming the limitations associated with curcumin.

Cardio-vascular safety beyond hERG: in silico modelling of a guinea pig right atrium assay

NASA Astrophysics Data System (ADS)

Fenu, Luca A.; Teisman, Ard; De Buck, Stefan S.; Sinha, Vikash K.; Gilissen, Ron A. H. J.; Nijsen, Marjoleen J. M. A.; Mackie, Claire E.; Sanderson, Wendy E.

2009-12-01

As chemists can easily produce large numbers of new potential drug candidates, there is growing demand for high capacity models that can help in driving the chemistry towards efficacious and safe candidates before progressing towards more complex models. Traditionally, the cardiovascular (CV) safety domain plays an important role in this process, as many preclinical CV biomarkers seem to have high prognostic value for the clinical outcome. Throughout the industry, traditional ion channel binding data are generated to drive the early selection process. Although this assay can generate data at high capacity, it has the disadvantage of producing high numbers of false negatives. Therefore, our company applies the isolated guinea pig right atrium (GPRA) assay early-on in discovery. This functional multi-channel/multi-receptor model seems much more predictive in identifying potential CV liabilities. Unfortunately however, its capacity is limited, and there is no room for full automation. We assessed the correlation between ion channel binding and the GPRA's Rate of Contraction (RC), Contractile Force (CF), and effective refractory frequency (ERF) measures assay using over six thousand different data points. Furthermore, the existing experimental knowledge base was used to develop a set of in silico classification models attempting to mimic the GPRA inhibitory activity. The Naïve Bayesian classifier was used to built several models, using the ion channel binding data or in silico computed properties and structural fingerprints as descriptors. The models were validated on an independent and diverse test set of 200 reference compounds. Performances were assessed on the bases of their overall accuracy, sensitivity and specificity in detecting both active and inactive molecules. Our data show that all in silico models are highly predictive of actual GPRA data, at a level equivalent or superior to the ion channel binding assays. Furthermore, the models were interpreted in terms of the descriptors used to highlight the undesirable areas in the explored chemical space, specifically regions of low polarity, high lipophilicity and high molecular weight. In conclusion, we developed a predictive in silico model of a complex physiological assay based on a large and high quality set of experimental data. This model allows high throughput in silico safety screening based on chemical structure within a given chemical space.

Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics.

PubMed

Beattie, Kylie A; Hill, Adam P; Bardenet, Rémi; Cui, Yi; Vandenberg, Jamie I; Gavaghan, David J; de Boer, Teun P; Mirams, Gary R

2018-03-24

Ion current kinetics are commonly represented by current-voltage relationships, time constant-voltage relationships and subsequently mathematical models fitted to these. These experiments take substantial time, which means they are rarely performed in the same cell. Rather than traditional square-wave voltage clamps, we fitted a model to the current evoked by a novel sum-of-sinusoids voltage clamp that was only 8 s long. Short protocols that can be performed multiple times within a single cell will offer many new opportunities to measure how ion current kinetics are affected by changing conditions. The new model predicts the current under traditional square-wave protocols well, with better predictions of underlying currents than literature models. The current under a novel physiologically relevant series of action potential clamps is predicted extremely well. The short sinusoidal protocols allow a model to be fully fitted to individual cells, allowing us to examine cell-cell variability in current kinetics for the first time. Understanding the roles of ion currents is crucial to predict the action of pharmaceuticals and mutations in different scenarios, and thereby to guide clinical interventions in the heart, brain and other electrophysiological systems. Our ability to predict how ion currents contribute to cellular electrophysiology is in turn critically dependent on our characterisation of ion channel kinetics - the voltage-dependent rates of transition between open, closed and inactivated channel states. We present a new method for rapidly exploring and characterising ion channel kinetics, applying it to the hERG potassium channel as an example, with the aim of generating a quantitatively predictive representation of the ion current. We fitted a mathematical model to currents evoked by a novel 8 second sinusoidal voltage clamp in CHO cells overexpressing hERG1a. The model was then used to predict over 5 minutes of recordings in the same cell in response to further protocols: a series of traditional square step voltage clamps, and also a novel voltage clamp comprising a collection of physiologically relevant action potentials. We demonstrate that we can make predictive cell-specific models that outperform the use of averaged data from a number of different cells, and thereby examine which changes in gating are responsible for cell-cell variability in current kinetics. Our technique allows rapid collection of consistent and high quality data, from single cells, and produces more predictive mathematical ion channel models than traditional approaches. © 2018 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.

Gaussian processes: a method for automatic QSAR modeling of ADME properties.

PubMed

Obrezanova, Olga; Csanyi, Gabor; Gola, Joelle M R; Segall, Matthew D

2007-01-01

In this article, we discuss the application of the Gaussian Process method for the prediction of absorption, distribution, metabolism, and excretion (ADME) properties. On the basis of a Bayesian probabilistic approach, the method is widely used in the field of machine learning but has rarely been applied in quantitative structure-activity relationship and ADME modeling. The method is suitable for modeling nonlinear relationships, does not require subjective determination of the model parameters, works for a large number of descriptors, and is inherently resistant to overtraining. The performance of Gaussian Processes compares well with and often exceeds that of artificial neural networks. Due to these features, the Gaussian Processes technique is eminently suitable for automatic model generation-one of the demands of modern drug discovery. Here, we describe the basic concept of the method in the context of regression problems and illustrate its application to the modeling of several ADME properties: blood-brain barrier, hERG inhibition, and aqueous solubility at pH 7.4. We also compare Gaussian Processes with other modeling techniques.

Discovery of Potent and Centrally Active 6-Substituted 5-Fluoro-1,3-dihydro-oxazine β-Secretase (BACE1) Inhibitors via Active Conformation Stabilization.

PubMed

Nakahara, Kenji; Fuchino, Kouki; Komano, Kazuo; Asada, Naoya; Tadano, Genta; Hasegawa, Tsuyoshi; Yamamoto, Takahiko; Sako, Yusuke; Ogawa, Masayoshi; Unemura, Chie; Hosono, Motoko; Ito, Hisanori; Sakaguchi, Gaku; Ando, Shigeru; Ohnishi, Shuichi; Kido, Yasuto; Fukushima, Tamio; Dhuyvetter, Deborah; Borghys, Herman; Gijsen, Harrie J M; Yamano, Yoshinori; Iso, Yasuyoshi; Kusakabe, Ken-Ichi

2018-06-14

β-Secretase (BACE1) has an essential role in the production of amyloid β peptides that accumulate in patients with Alzheimer's disease (AD). Thus, inhibition of BACE1 is considered to be a disease-modifying approach for the treatment of AD. Our hit-to-lead efforts led to a cellular potent 1,3-dihydro-oxazine 6, which however inhibited hERG and showed high P-gp efflux. The close analogue of 5-fluoro-oxazine 8 reduced P-gp efflux; further introduction of electron withdrawing groups at the 6-position improved potency and also mitigated P-gp efflux and hERG inhibition. Changing to a pyrazine followed by optimization of substituents on both the oxazine and the pyrazine culminated in 24 with robust Aβ reduction in vivo at low doses as well as reduced CYP2D6 inhibition. On the basis of the X-ray analysis and the QM calculation of given dihydro-oxazines, we reasoned that the substituents at the 6-position as well as the 5-fluorine on the oxazine would stabilize a bioactive conformation to increase potency.

Cardiovascular Safety Pharmacology of Sibutramine.

PubMed

Yun, Jaesuk; Chung, Eunyong; Choi, Ki Hwan; Cho, Dae Hyun; Song, Yun Jeong; Han, Kyoung Moon; Cha, Hey Jin; Shin, Ji Soon; Seong, Won-Keun; Kim, Young-Hoon; Kim, Hyung Soo

2015-07-01

Sibutramine is an anorectic that has been banned since 2010 due to cardiovascular safety issues. However, counterfeit drugs or slimming products that include sibutramine are still available in the market. It has been reported that illegal sibutramine-contained pharmaceutical products induce cardiovascular crisis. However, the mechanism underlying sibutramine-induced cardiovascular adverse effect has not been fully evaluated yet. In this study, we performed cardiovascular safety pharmacology studies of sibutramine systemically using by hERG channel inhibition, action potential duration, and telemetry assays. Sibutramine inhibited hERG channel current of HEK293 cells with an IC50 of 3.92 μM in patch clamp assay and increased the heart rate and blood pressure (76 Δbpm in heart rate and 51 ΔmmHg in blood pressure) in beagle dogs at a dose of 30 mg/kg (per oral), while it shortened action potential duration (at 10 μM and 30 μM, resulted in 15% and 29% decreases in APD50, and 9% and 17% decreases in APD90, respectively) in the Purkinje fibers of rabbits and had no effects on the QTc interval in beagle dogs. These results suggest that sibutramine has a considerable adverse effect on the cardiovascular system and may contribute to accurate drug safety regulation.

Multi-parameter in vitro toxicity testing of crizotinib, sunitinib, erlotinib, and nilotinib in human cardiomyocytes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Doherty, Kimberly R., E-mail: kimberly.doherty@quintiles.com; Wappel, Robert L.; Talbert, Dominique R.

2013-10-01

Tyrosine kinase inhibitors (TKi) have greatly improved the treatment and prognosis of multiple cancer types. However, unexpected cardiotoxicity has arisen in a subset of patients treated with these agents that was not wholly predicted by pre-clinical testing, which centers around animal toxicity studies and inhibition of the human Ether-à-go-go-Related Gene (hERG) channel. Therefore, we sought to determine whether a multi-parameter test panel assessing the effect of drug treatment on cellular, molecular, and electrophysiological endpoints could accurately predict cardiotoxicity. We examined how 4 FDA-approved TKi agents impacted cell viability, apoptosis, reactive oxygen species (ROS) generation, metabolic status, impedance, and ion channelmore » function in human cardiomyocytes. The 3 drugs clinically associated with severe cardiac adverse events (crizotinib, sunitinib, nilotinib) all proved to be cardiotoxic in our in vitro tests while the relatively cardiac-safe drug erlotinib showed only minor changes in cardiac cell health. Crizotinib, an ALK/MET inhibitor, led to increased ROS production, caspase activation, cholesterol accumulation, disruption in cardiac cell beat rate, and blockage of ion channels. The multi-targeted TKi sunitinib showed decreased cardiomyocyte viability, AMPK inhibition, increased lipid accumulation, disrupted beat pattern, and hERG block. Nilotinib, a second generation Bcr-Abl inhibitor, led to increased ROS generation, caspase activation, hERG block, and an arrhythmic beat pattern. Thus, each drug showed a unique toxicity profile that may reflect the multiple mechanisms leading to cardiotoxicity. This study demonstrates that a multi-parameter approach can provide a robust characterization of drug-induced cardiomyocyte damage that can be leveraged to improve drug safety during early phase development. - Highlights: • TKi with known adverse effects show unique cardiotoxicity profiles in this panel. • Crizotinib increases ROS, apoptosis, and cholesterol as well as alters beat rate. • Sunitinib inhibits AMPK, increases lipids and alters the cardiac beat pattern. • Nilotinib causes ROS and caspase activation, decreased lipids and arrhythmia. • Erlotinib did not impact ROS, caspase, or lipid levels or affect the beat pattern.« less

Characterization of the biological activity of a potent small molecule Hec1 inhibitor TAI-1

PubMed Central

2014-01-01

Background Hec1 (NDC80) is an integral part of the kinetochore and is overexpressed in a variety of human cancers, making it an attractive molecular target for the design of novel anticancer therapeutics. A highly potent first-in-class compound targeting Hec1, TAI-1, was identified and is characterized in this study to determine its potential as an anticancer agent for clinical utility. Methods The in vitro potency, cancer cell specificity, synergy activity, and markers for response of TAI-1 were evaluated with cell lines. Mechanism of action was confirmed with western blotting and immunofluorescent staining. The in vivo potency of TAI-1 was evaluated in three xenograft models in mice. Preliminary toxicity was evaluated in mice. Specificity to the target was tested with a kinase panel. Cardiac safety was evaluated with hERG assay. Clinical correlation was performed with human gene database. Results TAI-1 showed strong potency across a broad spectrum of tumor cells. TAI-1 disrupted Hec1-Nek2 protein interaction, led to Nek2 degradation, induced significant chromosomal misalignment in metaphase, and induced apoptotic cell death. TAI-1 was effective orally in in vivo animal models of triple negative breast cancer, colon cancer and liver cancer. Preliminary toxicity shows no effect on the body weights, organ weights, and blood indices at efficacious doses. TAI-1 shows high specificity to cancer cells and to target and had no effect on the cardiac channel hERG. TAI-1 is synergistic with doxorubicin, topotecan and paclitaxel in leukemia, breast and liver cancer cells. Sensitivity to TAI-1 was associated with the status of RB and P53 gene. Knockdown of RB and P53 in cancer cells increased sensitivity to TAI-1. Hec1-overexpressing molecular subtypes of human lung cancer were identified. Conclusions The excellent potency, safety and synergistic profiles of this potent first-in-class Hec1-targeted small molecule TAI-1 show its potential for clinically utility in anti-cancer treatment regimens. PMID:24401611

New-generation 5-HT4 receptor agonists: potential for treatment of gastrointestinal motility disorders.

PubMed

Manabe, Noriaki; Wong, Banny S; Camilleri, Michael

2010-06-01

Gastrointestinal (GI) dysmotility is an important mechanism in functional GI disorders (FGIDs) including constipation, irritable bowel syndrome, functional dyspepsia, and gastroparesis. 5-hydroxytryptamine(4) (5-HT(4)) receptors are targets for the treatment of GI motility disorders. However, older 5-HT(4) receptor agonists had limited clinical success because they were associated with changes in the function of the cardiac HERG potassium channel. We conducted a PubMed search using the following key words alone or in combination: 5-HT(4), safety, toxicity, pharmacokinetics, pharmacodynamics, clinical trial, cardiac, hERG, arrhythmia, potassium current, elderly, prucalopride, ATI-7505, and velusetrag (TD-5108), to review mechanisms of action, clinical efficacy, safety and tolerability of three new-generation 5-HT(4) receptor agonists. Prucalopride, ATI-7505, and velusetrag (TD-5108) are highly selective, high-affinity 5-HT(4) receptor agonists that are devoid of action on other receptors within their therapeutic range. Their efficacy has been demonstrated in pharmacodynamic studies which demonstrate acceleration of colonic transit and, to a variable degree, in clinical trials that significantly relieve chronic constipation. Currently available evidence shows that the new 5-HT(4) receptor agonists have safe cardiac profiles. New-generation 5-HT(4) receptor agonists and future drugs targeting organ-specific splice variants are promising approaches to treat GI dysmotility, particularly colonic diseases.

Fluoroethoxy-1,4-diphenethylpiperidine and piperazine derivatives: Potent and selective inhibitors of [3H]dopamine uptake at the vesicular monoamine transporter-2.

PubMed

Hankosky, Emily R; Joolakanti, Shyam R; Nickell, Justin R; Janganati, Venumadhav; Dwoskin, Linda P; Crooks, Peter A

2017-12-15

A small library of fluoroethoxy-1,4-diphenethyl piperidine and fluoroethoxy-1,4-diphenethyl piperazine derivatives were designed, synthesized and evaluated for their ability to inhibit [ 3 H]dopamine (DA) uptake at the vesicular monoamine transporter-2 (VMAT2) and dopamine transporter (DAT), [ 3 H]serotonin (5-HT) uptake at the serotonin transporter (SERT), and [ 3 H]dofetilide binding at the human-ether-a-go-go-related gene (hERG) channel. The majority of the compounds exhibited potent inhibition of [ 3 H]DA uptake at VMAT2, Ki changes in the nanomolar range (K i  = 0.014-0.073 µM). Compound 15d exhibited the highest affinity (K i  = 0.014 µM) at VMAT2, and had 160-, 5-, and 60-fold greater selectivity for VMAT2 vs. DAT, SERT and hERG, respectively. Compound 15b exhibited the greatest selectivity (>60-fold) for VMAT2 relative to all the other targets evaluated, and 15b had high affinity for VMAT2 (K i  = 0.073 µM). Compound 15b was considered the lead compound from this analog series due to its high affinity and selectivity for VMAT2. Copyright © 2017 Elsevier Ltd. All rights reserved.

Indole alkaloids from the Marquesan plant Rauvolfia nukuhivensis and their effects on ion channels.

PubMed

Martin, Nicolas J; Ferreiro, Sara F; Barbault, Florent; Nicolas, Mael; Lecellier, Gaël; Paetz, Christian; Gaysinski, Marc; Alonso, Eva; Thomas, Olivier P; Botana, Luis M; Raharivelomanana, Phila

2015-01-01

In addition to the already reported nukuhivensiums 1 and 2, 11 indole alkaloids were isolated from the bark of the plant Rauvolfia nukuhivensis, growing in the Marquesas archipelago. The known sandwicine (3), isosandwicine (4), spegatrine (8), lochneram (9), flavopereirine (13) have been found in this plant together with the norsandwicine (5), isonorsandwicine (6), Nb-methylisosandwicine (7), 10-methoxypanarine (10), nortueiaoine (11), tueiaoine (12). The structure elucidation was performed on the basis of a deep exploration of the NMR and HRESIMS data as well as comparison with literature data for similar compounds. Norsandwicine, 10-methoxypanarine, tueiaoine, and more importantly nukuhivensiums, were shown to significantly induce a reduction of IKr amplitude (HERG current). Molecular modelling through docking was performed in order to illustrate this result. Copyright © 2014 Elsevier Ltd. All rights reserved.

Design, synthesis, and evaluation of 4,6-diaminonicotinamide derivatives as novel and potent immunomodulators targeting JAK3.

PubMed

Nakajima, Yutaka; Aoyama, Naohiro; Takahashi, Fumie; Sasaki, Hiroshi; Hatanaka, Keiko; Moritomo, Ayako; Inami, Masamichi; Ito, Misato; Nakamura, Koji; Nakamori, Fumihiro; Inoue, Takayuki; Shirakami, Shohei

2016-10-01

In organ transplantation, T cell-mediated immune responses play a key role in the rejection of allografts. Janus kinase 3 (JAK3) is specifically expressed in hematopoietic cells and associated with regulation of T cell development via interleukin-2 signaling pathway. Here, we designed novel 4,6-diaminonicotinamide derivatives as immunomodulators targeting JAK3 for prevention of transplant rejection. Our optimization of C4- and C6-substituents and docking calculations to JAK3 protein confirmed that the 4,6-diaminonicotinamide scaffold resulted in potent inhibition of JAK3. We also investigated avoidance of human ether-a-go-go related gene (hERG) inhibitory activity. Selected compound 28 in combination with tacrolimus prevented allograft rejection in a rat heterotopic cardiac transplantation model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effect of microculture on cell metabolism and biochemistry: do cells get stressed in microchannels?

PubMed

Su, Xiaojing; Theberge, Ashleigh B; January, Craig T; Beebe, David J

2013-02-05

Microfluidics is emerging as a promising platform for cell culture, enabling increased microenvironment control and potential for integrated analysis compared to conventional macroculture systems such as well plates and Petri dishes. To advance the use of microfluidic devices for cell culture, it is necessary to better understand how miniaturization affects cell behavior. In particular, microfluidic devices have significantly higher surface-area-to-volume ratios than conventional platforms, resulting in lower volumes of media per cell, which can lead to cell stress. We investigated cell stress under a variety of culture conditions using three cell lines: parental HEK (human embryonic kidney) cells and transfected HEK cells that stably express wild-type (WT) and mutant (G601S) human ether-a-go-go related gene (hERG) potassium channel protein. These three cell lines provide a unique model system through which to study cell-type-specific responses in microculture because mutant hERG is known to be sensitive to environmental conditions, making its expression a particularly sensitive readout through which to compare macro- and microculture. While expression of WT-hERG was similar in microchannel and well culture, the expression of mutant G601S-hERG was reduced in microchannels. Expression of the endoplasmic reticulum (ER) stress marker immunoglobulin binding protein (BiP) was upregulated in all three cell lines in microculture. Using BiP expression, glucose consumption, and lactate accumulation as readouts we developed methods for reducing ER stress including properly increasing the frequency of media replacement, reducing cell seeding density, and adjusting the serum concentration and buffering capacity of culture medium. Indeed, increasing the buffering capacity of culture medium or frequency of media replacement partially restored the expression of the G601S-hERG in microculture. This work illuminates how biochemical properties of cells differ in macro- and microculture and suggests strategies that can be used to modify cell culture protocols for future studies involving miniaturized culture platforms.

In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk.

PubMed

Romero, Lucia; Cano, Jordi; Gomis-Tena, Julio; Trenor, Beatriz; Sanz, Ferran; Pastor, Manuel; Saiz, Javier

2018-04-23

Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models ( O'Hara , T. , PLos Comput. Biol. 2011 , 7 , e1002061 ) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current ( I Ks and I Kr , respectively) and the L-type calcium current ( I CaL ) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC 50 based test.

Drug Screening Using a Library of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes Reveals Disease Specific Patterns of Cardiotoxicity

PubMed Central

Liang, Ping; Lan, Feng; Lee, Andrew S.; Gong, Tingyu; Sanchez-Freire, Veronica; Wang, Yongming; Diecke, Sebastian; Sallam, Karim; Knowles, Joshua W.; Wang, Paul J.; Nguyen, Patricia K.; Bers, Donald M.; Robbins, Robert C.; Wu, Joseph C.

2013-01-01

Background Cardiotoxicity is a leading cause for drug attrition during pharmaceutical development and has resulted in numerous preventable patient deaths. Incidents of adverse cardiac drug reactions are more common in patients with pre-existing heart disease than the general population. Here we generated a library of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from patients with various hereditary cardiac disorders to model differences in cardiac drug toxicity susceptibility for patients of different genetic backgrounds. Methods and Results Action potential duration (APD) and drug-induced arrhythmia were measured at the single cell level in hiPSC-CMs derived from healthy subjects and patients with hereditary long QT syndrome (LQT), familial hypertrophic cardiomyopathy (HCM), and familial dilated cardiomyopathy (DCM). Disease phenotypes were verified in LQT, HCM, and DCM iPSC-CMs by immunostaining and single cell patch clamp. Human embryonic stem cell-derived cardiomyocytes (hESC-CMs) and the human ether-a-go-go-related gene (hERG) expressing human embryonic kidney (HEK293) cells were used as controls. Single cell PCR confirmed expression of all cardiac ion channels in patient-specific hiPSC-CMs as well as hESC-CMs, but not in HEK293 cells. Disease-specific hiPSC-CMs demonstrated increased susceptibility to known cardiotoxic drugs as measured by APD and quantification of drug-induced arrhythmias such as early after depolarizations (EADs) and delayed after depolarizations (DADs). Conclusions We have recapitulated drug-induced cardiotoxicity profiles for healthy subjects, LQT, HCM, and DCM patients at the single cell level for the first time. Our data indicate that healthy and diseased individuals exhibit different susceptibilities to cardiotoxic drugs and that use of disease-specific hiPSC-CMs may predict adverse drug responses more accurately than standard hERG test or healthy control hiPSC-CM/hESC-CM screening assays. PMID:23519760

APETx4, a Novel Sea Anemone Toxin and a Modulator of the Cancer-Relevant Potassium Channel KV10.1.

PubMed

Moreels, Lien; Peigneur, Steve; Galan, Diogo T; De Pauw, Edwin; Béress, Lászlo; Waelkens, Etienne; Pardo, Luis A; Quinton, Loïc; Tytgat, Jan

2017-09-13

The human ether-à-go-go channel (hEag1 or K V 10.1) is a cancer-relevant voltage-gated potassium channel that is overexpressed in a majority of human tumors. Peptides that are able to selectively inhibit this channel can be lead compounds in the search for new anticancer drugs. Here, we report the activity-guided purification and electrophysiological characterization of a novel K V 10.1 inhibitor from the sea anemone Anthopleura elegantissima . Purified sea anemone fractions were screened for inhibitory activity on K V 10.1 by measuring whole-cell currents as expressed in Xenopus laevis oocytes using the two-microelectrode voltage clamp technique. Fractions that showed activity on Kv10.1 were further purified by RP-HPLC. The amino acid sequence of the peptide was determined by a combination of MALDI- LIFT-TOF/TOF MS/MS and CID-ESI-FT-ICR MS/MS and showed a high similarity with APETx1 and APETx3 and was therefore named APETx4. Subsequently, the peptide was electrophysiologically characterized on K V 10.1. The selectivity of the toxin was investigated on an array of voltage-gated ion channels, including the cardiac human ether-à-go-go-related gene potassium channel (hERG or Kv11.1). The toxin inhibits K V 10.1 with an IC 50 value of 1.1 μM. In the presence of a similar toxin concentration, a shift of the activation curve towards more positive potentials was observed. Similar to the effect of the gating modifier toxin APETx1 on hERG, the inhibition of Kv10.1 by the isolated toxin is reduced at more positive voltages and the peptide seems to keep the channel in a closed state. Although the peptide also induces inhibitory effects on other K V and Na V channels, it exhibits no significant effect on hERG. Moreover, APETx4 induces a concentration-dependent cytotoxic and proapoptotic effect in various cancerous and noncancerous cell lines. This newly identified K V 10.1 inhibitor can be used as a tool to further characterize the oncogenic channel K V 10.1 or as a scaffold for the design and synthesis of more potent and safer anticancer drugs.

hERG1/Kv11.1 activation stimulates transcription of p21waf/cip in breast cancer cells via a calcineurin-dependent mechanism.

PubMed

Perez-Neut, Mathew; Rao, Vidhya R; Gentile, Saverio

2016-09-13

The function of Kv11.1 is emerging in breast cancer biology, as a growing body of evidence indicates that the hERG1/Kv11.1 potassium channel is aberrantly expressed in several cancer types including breast cancers.The biological effects of Kv11.1 channel blockers and their associated side effects are very well known but the potential use of Kv11.1 activators as an anticancer strategy are still unexplored. In our previous work, we have established that stimulation of the Kv11.1 potassium channel activates a senescent-like program that is characterized by a significant increase in tumor suppressor protein levels, such as p21waf/cip and p16INK4A. In this study we investigated the mechanism linking Kv11.1 stimulation to augmentation of p21waf/cip protein level. We have demonstrated that the Kv11.1 channel activator NS1643 activates a calcineurin-dependent transcription of p21waf/cip and that this event is fundamental for the inhibitory effect of NS1643 on cell proliferation. Our results reveal a novel mechanism by which stimulation of Kv11.1 channel leads to transcription of a potent tumor suppressor and suggest a potential therapeutic use for Kv11.1 channel activators.

hERG1/Kv11.1 activation stimulates transcription of p21waf/cip in breast cancer cells via a calcineurin-dependent mechanism

PubMed Central

Perez-Neut, Mathew; Rao, Vidhya R.; Gentile, Saverio

2016-01-01

The function of Kv11.1 is emerging in breast cancer biology, as a growing body of evidence indicates that the hERG1/Kv11.1 potassium channel is aberrantly expressed in several cancer types including breast cancers. The biological effects of Kv11.1 channel blockers and their associated side effects are very well known but the potential use of Kv11.1 activators as an anticancer strategy are still unexplored. In our previous work, we have established that stimulation of the Kv11.1 potassium channel activates a senescent-like program that is characterized by a significant increase in tumor suppressor protein levels, such as p21waf/cip and p16INK4A. In this study we investigated the mechanism linking Kv11.1 stimulation to augmentation of p21waf/cip protein level. We have demonstrated that the Kv11.1 channel activator NS1643 activates a calcineurin-dependent transcription of p21waf/cip and that this event is fundamental for the inhibitory effect of NS1643 on cell proliferation. Our results reveal a novel mechanism by which stimulation of Kv11.1 channel leads to transcription of a potent tumor suppressor and suggest a potential therapeutic use for Kv11.1 channel activators. PMID:25945833

[Effects of allitridum on rapidly delayed rectifier potassium current in HEK293 cell line].

PubMed

Zhang, Jiancheng; Lin, Kun; Wei, Zhixiong; Chen, Qian; Liu, Li; Zhao, Xiaojing; Zhao, Ying; Xu, Bin; Chen, Xi; Li, Yang

2015-08-01

To study the effect of allitridum on rapidly delayed rectifier potassium current (IKr) in HEK293 cell line. HEK293 cells were transiently transfected with HERG channel cDNA plasmid pcDNA3.1 via Lipofectamine. Allitridum was added to the extracellular solution by partial perfusion after giga seal at the final concentration of 30 µmol/L. Whole-cell patch clamp technique was used to record the HERG currents and gating kinetics before and after allitridum exposure at room temperature. The amplitude and density of IHERG were both suppressed by allitridum in a voltage-dependent manner. In the presence of allitridum, the peak current of IHERG was reduced from 73.5∓4.3 pA/pF to 42.1∓3.6 pA/pF at the test potential of +50 mV (P<0.01). Allitridum also concentration-dependently decreased the density of the IHERG. The IC50 of allitridum was 34.74 µmol/L with a Hill coefficient of 1.01. Allitridum at 30 µmol/L caused a significant positive shift of the steady-state activation curve of IHERG and a markedly negative shift of the steady-state inactivation of IHERG, and significantly shortened the slow time constants of IHERG deactivation. Allitridum can potently block IHERG in HEK293 cells, which might be the electrophysiological basis for its anti-arrhythmic action.

Discovery and Optimization of 5-Amino-1,2,3-triazole-4-carboxamide Series against Trypanosoma cruzi.

PubMed

Brand, Stephen; Ko, Eun Jung; Viayna, Elisabet; Thompson, Stephen; Spinks, Daniel; Thomas, Michael; Sandberg, Lars; Francisco, Amanda F; Jayawardhana, Shiromani; Smith, Victoria C; Jansen, Chimed; De Rycker, Manu; Thomas, John; MacLean, Lorna; Osuna-Cabello, Maria; Riley, Jennifer; Scullion, Paul; Stojanovski, Laste; Simeons, Frederick R C; Epemolu, Ola; Shishikura, Yoko; Crouch, Sabrinia D; Bakshi, Tania S; Nixon, Christopher J; Reid, Iain H; Hill, Alan P; Underwood, Tim Z; Hindley, Sean J; Robinson, Sharon A; Kelly, John M; Fiandor, Jose M; Wyatt, Paul G; Marco, Maria; Miles, Timothy J; Read, Kevin D; Gilbert, Ian H

2017-09-14

Chagas' disease, caused by the protozoan parasite Trypanosoma cruzi, is the most common cause of cardiac-related deaths in endemic regions of Latin America. There is an urgent need for new safer treatments because current standard therapeutic options, benznidazole and nifurtimox, have significant side effects and are only effective in the acute phase of the infection with limited efficacy in the chronic phase. Phenotypic high content screening against the intracellular parasite in infected VERO cells was used to identify a novel hit series of 5-amino-1,2,3-triazole-4-carboxamides (ATC). Optimization of the ATC series gave improvements in potency, aqueous solubility, and metabolic stability, which combined to give significant improvements in oral exposure. Mitigation of a potential Ames and hERG liability ultimately led to two promising compounds, one of which demonstrated significant suppression of parasite burden in a mouse model of Chagas' disease.

Human Neural Cell-Based Biosensor

DTIC Science & Technology

2010-04-26

SNAP25 (SNAP25), GluR1 (GRIA1) glutamate receptor , ionotropic , AMPA1, Nav1.2 (SCN2A), Nav1.6 (SCN8A), CaV 2.1 (CACNA1A), HERG (KCNH2), and KCC2...transitions to mesenchymal progenitor cells." Tissue Eng Part A 15(8): 1897-907. Haltiwanger, R. S. and P. Stanley (2002). "Modulation of receptor ...cytometry studies previously conducted by the Stice lab identified ciliary neurotrophic factor receptor alpha (CNTFRα) as a novel cell surface marker to

Preclinical Torsades-de-Pointes screens: advantages and limitations of surrogate and direct approaches in evaluating proarrhythmic risk.

PubMed

Gintant, Gary A

2008-08-01

The successful development of novel drugs requires the ability to detect (and avoid) compounds that may provoke Torsades-de-Pointes (TdeP) arrhythmia while endorsing those compounds with minimal torsadogenic risk. As TdeP is a rare arrhythmia not readily observed during clinical or post-marketing studies, numerous preclinical models are employed to assess delayed or altered ventricular repolarization (surrogate markers linked to enhanced proarrhythmic risk). This review evaluates the advantages and limitations of selected preclinical models (ranging from the simplest cellular hERG current assay to the more complex in vitro perfused ventricular wedge and Langendorff heart preparations and in vivo chronic atrio-ventricular (AV)-node block model). Specific attention is paid to the utility of concentration-response relationships and "risk signatures" derived from these studies, with the intention of moving beyond predicting clinical QT prolongation and towards prediction of TdeP risk. While the more complex proarrhythmia models may be suited to addressing questionable or conflicting proarrhythmic signals obtained with simpler preclinical assays, further benchmarking of proarrhythmia models is required for their use in the robust evaluation of safety margins. In the future, these models may be able to reduce unwarranted attrition of evolving compounds while becoming pivotal in the balanced integrated risk assessment of advancing compounds.

A new serotonin 5-HT6 receptor antagonist with procognitive activity - Importance of a halogen bond interaction to stabilize the binding

NASA Astrophysics Data System (ADS)

González-Vera, Juan A.; Medina, Rocío A.; Martín-Fontecha, Mar; Gonzalez, Angel; de La Fuente, Tania; Vázquez-Villa, Henar; García-Cárceles, Javier; Botta, Joaquín; McCormick, Peter J.; Benhamú, Bellinda; Pardo, Leonardo; López-Rodríguez, María L.

2017-01-01

Serotonin 5-HT6 receptor has been proposed as a promising therapeutic target for cognition enhancement though the development of new antagonists is still needed to validate these molecules as a drug class for the treatment of Alzheimer’s disease and other pathologies associated with memory deficiency. As part of our efforts to target the 5-HT6 receptor, new benzimidazole-based compounds have been designed and synthesized. Site-directed mutagenesis and homology models show the importance of a halogen bond interaction between a chlorine atom of the new class of 5-HT6 receptor antagonists identified herein and a backbone carbonyl group in transmembrane domain 4. In vitro pharmacological characterization of 5-HT6 receptor antagonist 7 indicates high affinity and selectivity over a panel of receptors including 5-HT2B subtype and hERG channel, which suggests no major cardiac issues. Compound 7 exhibited in vivo procognitive activity (1 mg/kg, ip) in the novel object recognition task as a model of memory deficit.

ECG telemetry in conscious guinea pigs.

PubMed

Ruppert, Sabine; Vormberge, Thomas; Igl, Bernd-Wolfgang; Hoffmann, Michael

2016-01-01

During preclinical drug development, monitoring of the electrocardiogram (ECG) is an important part of cardiac safety assessment. To detect potential pro-arrhythmic liabilities of a drug candidate and for internal decision-making during early stage drug development an in vivo model in small animals with translatability to human cardiac function is required. Over the last years, modifications/improvements regarding animal housing, ECG electrode placement, and data evaluation have been introduced into an established model for ECG recordings using telemetry in conscious, freely moving guinea pigs. Pharmacological validation using selected reference compounds affecting different mechanisms relevant for cardiac electrophysiology (quinidine, flecainide, atenolol, dl-sotalol, dofetilide, nifedipine, moxifloxacin) was conducted and findings were compared with results obtained in telemetered Beagle dogs. Under standardized conditions, reliable ECG data with low variability allowing largely automated evaluation were obtained from the telemetered guinea pig model. The model is sensitive to compounds blocking cardiac sodium channels, hERG K(+) channels and calcium channels, and appears to be even more sensitive to β-blockers as observed in dogs at rest. QT interval correction according to Bazett and Sarma appears to be appropriate methods in conscious guinea pigs. Overall, the telemetered guinea pig is a suitable model for the conduct of early stage preclinical ECG assessment. Copyright © 2016 Elsevier Inc. All rights reserved.

Sildenafil (Viagra) prolongs cardiac repolarization by blocking the rapid component of the delayed rectifier potassium current.

PubMed

Geelen, P; Drolet, B; Rail, J; Bérubé, J; Daleau, P; Rousseau, G; Cardinal, R; O'Hara, G E; Turgeon, J

2000-07-18

BACKGROUND-Several cases of unexpected death have been reported with sildenafil in patients predisposed to ischemic cardiac events. Although acute episodes of ischemia could account for some of these deaths, we hypothesized that sildenafil may have unsuspected electrophysiological effects predisposing some patients to proarrhythmia. METHODS AND RESULTS-Studies were undertaken in 10 isolated guinea pig hearts that demonstrated prolongation of cardiac repolarization in a reverse use-dependent manner by sildenafil 30 mcmol/L. Action potential duration increased 15% from baseline 117+/-3 to 134+/-2 ms with sildenafil during pacing at 250 ms cycle length, whereas a 6% increase from 99+/-2 to 105+/-2 ms was seen with pacing at 150 ms cycle length. Experiments in human ether-a-go-go-related gene (HERG)-transfected HEK293 cells (n=30) demonstrated concentration-dependent block of the rapid component (I(Kr)) of the delayed rectifier potassium current: activating current was 50% decreased at 100 mcmol/L. This effect was confirmed using HERG-transfected Chinese hamster ovary (CHO) cells, which exhibit no endogenous I(K)-like current. CONCLUSIONS-Sildenafil possesses direct cardiac electrophysiological effects similar to class III antiarrhythmic drugs. These effects are observed at concentrations that may be found in conditions of impaired drug elimination such as renal or hepatic insufficiency, during coadministration of another CYP3A substrate/inhibitor, or after drug overdose and offer a new potential explanation for sudden death during sildenafil treatment.

Rational Design of Novel 1,3-Oxazine Based β-Secretase (BACE1) Inhibitors: Incorporation of a Double Bond To Reduce P-gp Efflux Leading to Robust Aβ Reduction in the Brain.

PubMed

Fuchino, Kouki; Mitsuoka, Yasunori; Masui, Moriyasu; Kurose, Noriyuki; Yoshida, Shuhei; Komano, Kazuo; Yamamoto, Takahiko; Ogawa, Masayoshi; Unemura, Chie; Hosono, Motoko; Ito, Hisanori; Sakaguchi, Gaku; Ando, Shigeru; Ohnishi, Shuichi; Kido, Yasuto; Fukushima, Tamio; Miyajima, Hirofumi; Hiroyama, Shuichi; Koyabu, Kiyotaka; Dhuyvetter, Deborah; Borghys, Herman; Gijsen, Harrie J M; Yamano, Yoshinori; Iso, Yasuyoshi; Kusakabe, Ken-Ichi

2018-05-23

Accumulation of Aβ peptides is a hallmark of Alzheimer's disease (AD) and is considered a causal factor in the pathogenesis of AD. β-Secretase (BACE1) is a key enzyme responsible for producing Aβ peptides, and thus agents that inhibit BACE1 should be beneficial for disease-modifying treatment of AD. Here we describe the discovery and optimization of novel oxazine-based BACE1 inhibitors by lowering amidine basicity with the incorporation of a double bond to improve brain penetration. Starting from a 1,3-dihydrooxazine lead 6 identified by a hit-to-lead SAR following HTS, we adopted a p K a lowering strategy to reduce the P-gp efflux and the high hERG potential leading to the discovery of 15 that produced significant Aβ reduction with long duration in pharmacodynamic models and exhibited wide safety margins in cardiovascular safety models. This compound improved the brain-to-plasma ratio relative to 6 by reducing P-gp recognition, which was demonstrated by a P-gp knockout mouse model.

Lessons from the evaluation of the UK's NHS R&D Implementation Methods Programme

PubMed Central

Soper, Bryony; Hanney, Stephen R

2007-01-01

Background Concern about the effective use of research was a major factor behind the creation of the NHS R&D Programme in 1991. In 1994, an advisory group was established to identify research priorities in research implementation. The Implementation Methods Programme (IMP) flowed from this, and its commissioning group funded 36 projects. In 2000 responsibility for the programme passed to the National Co-ordinating Centre for NHS Service Delivery and Organisation R&D, which asked the Health Economics Research Group (HERG), Brunel University, to conduct an evaluation in 2002. By then most projects had been completed. This evaluation was intended to cover: the quality of outputs, lessons to be learnt about the communication strategy and the commissioning process, and the benefits from the projects. Methods We adopted a wide range of quantitative and qualitative methods. They included: documentary analysis, interviews with key actors, questionnaires to the funded lead researchers, questionnaires to potential users, and desk analysis. Results Quantitative assessment of outputs and dissemination revealed that the IMP funded useful research projects, some of which had considerable impact against the various categories in the HERG payback model, such as publications, further research, research training, impact on health policy, and clinical practice. Qualitative findings from interviews with advisory and commissioning group members indicated that when the IMP was established, implementation research was a relatively unexplored field. This was reflected in the understanding brought to their roles by members of the advisory and commissioning groups, in the way priorities for research were chosen and developed, and in how the research projects were commissioned. The ideological and methodological debates associated with these decisions have continued among those working in this field. The need for an effective communication strategy for the programme as a whole was particularly important. However, such a strategy was never developed, making it difficult to establish the general influence of the IMP as a programme. Conclusion Our findings about the impact of the work funded, and the difficulties faced by those developing the IMP, have implications for the development of strategic programmes of research in general, as well as for the development of more effective research in this field. PMID:17309803

Design, synthesis and evaluation of MCH receptor 1 antagonists--Part III: Discovery of pre-clinical development candidate BI 186908.

PubMed

Oost, Thorsten; Heckel, Armin; Kley, Jörg T; Lehmann, Thorsten; Müller, Stephan; Roth, Gerald J; Rudolf, Klaus; Arndt, Kirsten; Budzinski, Ralph; Lenter, Martin; Lotz, Ralf R H; Maier, Gerd-Michael; Markert, Michael; Thomas, Leo; Stenkamp, Dirk

2015-08-15

Although overweight and obesity are highly prevalent conditions, options to treat them are still very limited. As part of our search for safe and effective MCH-R1 antagonists for the treatment of obesity, two series of pyridones and pyridazinones were evaluated. Optimization was aimed at improving DMPK properties by increasing metabolic stability and improving the safety profile by reducing inhibition of the hERG channel and reducing the potential to induce phospholipidosis. Steric shielding of a labile keto moiety with an ortho-methyl group and fine-tuning of the polarity in several parts of the molecule resulted in BI 186908 (11 g), a potent and selective MCH-R1 antagonist with favorable DMPK and CMC properties. Chronic administration of BI 186908 resulted in significant body weight reduction comparable to sibutramine in a 4 week diet-induced obesity model in rats. Based on its favorable safety profile, BI 186908 was advanced to pre-clinical development. Copyright © 2015 Elsevier Ltd. All rights reserved.

Creation of Novel Cores for β-Secretase (BACE-1) Inhibitors: A Multiparameter Lead Generation Strategy

PubMed Central

2014-01-01

In order to find optimal core structures as starting points for lead optimization, a multiparameter lead generation workflow was designed with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer’s disease. De novo design of core fragments was connected with three predictive in silico models addressing target affinity, permeability, and hERG activity, in order to guide synthesis. Taking advantage of an additive SAR, the prioritized cores were decorated with a few, well-characterized substituents from known BACE-1 inhibitors in order to allow for core-to-core comparisons. Prediction methods and analyses of how physicochemical properties of the core structures correlate to in vitro data are described. The syntheses and in vitro data of the test compounds are reported in a separate paper by Ginman et al. [J. Med. Chem.2013, 56, 4181–4205]. The affinity predictions are described in detail by Roos et al. [J. Chem. Inf.2014, DOI: 10.1021/ci400374z]. PMID:24900855

Creation of Novel Cores for β-Secretase (BACE-1) Inhibitors: A Multiparameter Lead Generation Strategy.

PubMed

Viklund, Jenny; Kolmodin, Karin; Nordvall, Gunnar; Swahn, Britt-Marie; Svensson, Mats; Gravenfors, Ylva; Rahm, Fredrik

2014-04-10

In order to find optimal core structures as starting points for lead optimization, a multiparameter lead generation workflow was designed with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer's disease. De novo design of core fragments was connected with three predictive in silico models addressing target affinity, permeability, and hERG activity, in order to guide synthesis. Taking advantage of an additive SAR, the prioritized cores were decorated with a few, well-characterized substituents from known BACE-1 inhibitors in order to allow for core-to-core comparisons. Prediction methods and analyses of how physicochemical properties of the core structures correlate to in vitro data are described. The syntheses and in vitro data of the test compounds are reported in a separate paper by Ginman et al. [J. Med. Chem. 2013, 56, 4181-4205]. The affinity predictions are described in detail by Roos et al. [J. Chem. Inf. 2014, DOI: 10.1021/ci400374z].

Changes in the mRNA levels of delayed rectifier potassium channels in human atrial fibrillation.

PubMed

Lai, L P; Su, M J; Lin, J L; Lin, F Y; Tsai, C H; Chen, Y S; Tseng, Y Z; Lien, W P; Huang, S K

1999-01-01

We measured mRNA levels of delayed rectifier potassium channels in human atrial tissue to investigate the mechanism of the shortening of the atrial effective refractory period and the loss of rate-adaptive shortening of the atrial effective refractory period in human atrial fibrillation. A total of 34 patients undergoing open heart surgery were included. Atrial tissue was obtained from the right atrial free wall, right atrial appendage, left atrial free wall and left atrial appendage, respectively. The mRNA amounts of KVLQT1 (IKs), minK (beta-subunit of IKs), HERG (IKr), and KV1.5 (IKur) were measured by reverse transcription-polymerase chain reaction and normalized to the mRNA amount of GAPDH. We found that the mRNA levels of KV1.5, HERG and KVLQT1 were all significantly decreased in patients with persistent atrial fibrillation for more than 3 months. In contrast, the mRNA level of minK was significantly increased in patients with persistent atrial fibrillation for more than 3 months. We further showed that these changes were independent of the underlying cardiac disease, atrial filling pressure, gender and age. We also found that there was no spatial dispersion of mRNA levels among the four atrial sampling sites. Because the decrease in potassium currents results in a prolonged action potential, the shortening of the atrial effective refractory period in atrial fibrillation should be attributed to other factors. However, the decrease in IKs might contribute, at least in part, to the loss of rate-adaptive shortening of the atrial refractory period.

Short-term variability in QT interval and ventricular arrhythmias induced by dofetilide are dependent on high-frequency autonomic oscillations

PubMed Central

Champeroux, P; Thireau, J; Judé, S; Laigot-Barbé, C; Maurin, A; Sola, M L; Fowler, J S L; Richard, S; Le Guennec, J Y

2015-01-01

Background and Purpose The present study was undertaken to investigate an effect of dofetilide, a potent arrhythmic blocker of the voltage-gated K+ channel, hERG, on cardiac autonomic control. Combined with effects on ardiomyocytes, these properties could influence its arrhythmic potency. Experimental Approach The short-term variability of beat-to-beat QT interval (STVQT), induced by dofetilide is a strong surrogate of Torsades de pointes liability. Involvement of autonomic modulation in STVQT was investigated in healthy cynomolgus monkeys and beagle dogs by power spectral analysis under conditions of autonomic blockade with hexamethonium. Key Results Increase in STVQT induced by dofetilide in monkeys and dogs was closely associated with an enhancement of endogenous heart rate and QT interval high-frequency (HF) oscillations. These effects were fully suppressed under conditions of autonomic blockade with hexamethonium. Ventricular arrhythmias, including Torsades de pointes in monkeys, were prevented in both species when HF oscillations were suppressed by autonomic blockade. Similar enhancements of heart rate HF oscillations were found in dogs with other hERG blockers described as causing Torsades de pointes in humans. Conclusions and Implications These results demonstrate for the first time that beat-to-beat ventricular repolarization variability and ventricular arrhythmias induced by dofetilide are dependent on endogenous HF autonomic oscillations in heart rate. When combined with evidence of hERG-blocking properties, enhancement of endogenous HF oscillations in heart rate could constitute an earlier and more sensitive biomarker than STVQT for Torsades de pointes liability, applicable to preclinical regulatory studies conducted in healthy animals. PMID:25625756

Modulation of hERG potassium channel gating normalizes action potential duration prolonged by dysfunctional KCNQ1 potassium channel

PubMed Central

Zhang, Hongkang; Zou, Beiyan; Yu, Haibo; Moretti, Alessandra; Wang, Xiaoying; Yan, Wei; Babcock, Joseph J.; Bellin, Milena; McManus, Owen B.; Tomaselli, Gordon; Nan, Fajun; Laugwitz, Karl-Ludwig; Li, Min

2012-01-01

Long QT syndrome (LQTS) is a genetic disease characterized by a prolonged QT interval in an electrocardiogram (ECG), leading to higher risk of sudden cardiac death. Among the 12 identified genes causal to heritable LQTS, ∼90% of affected individuals harbor mutations in either KCNQ1 or human ether-a-go-go related genes (hERG), which encode two repolarizing potassium currents known as IKs and IKr. The ability to quantitatively assess contributions of different current components is therefore important for investigating disease phenotypes and testing effectiveness of pharmacological modulation. Here we report a quantitative analysis by simulating cardiac action potentials of cultured human cardiomyocytes to match the experimental waveforms of both healthy control and LQT syndrome type 1 (LQT1) action potentials. The quantitative evaluation suggests that elevation of IKr by reducing voltage sensitivity of inactivation, not via slowing of deactivation, could more effectively restore normal QT duration if IKs is reduced. Using a unique specific chemical activator for IKr that has a primary effect of causing a right shift of V1/2 for inactivation, we then examined the duration changes of autonomous action potentials from differentiated human cardiomyocytes. Indeed, this activator causes dose-dependent shortening of the action potential durations and is able to normalize action potentials of cells of patients with LQT1. In contrast, an IKr chemical activator of primary effects in slowing channel deactivation was not effective in modulating action potential durations. Our studies provide both the theoretical basis and experimental support for compensatory normalization of action potential duration by a pharmacological agent. PMID:22745159

Short-term variability in QT interval and ventricular arrhythmias induced by dofetilide are dependent on high-frequency autonomic oscillations.

PubMed

Champeroux, P; Thireau, J; Judé, S; Laigot-Barbé, C; Maurin, A; Sola, M L; Fowler, J S L; Richard, S; Le Guennec, J Y

2015-06-01

The present study was undertaken to investigate an effect of dofetilide, a potent arrhythmic blocker of the voltage-gated K(+) channel, hERG, on cardiac autonomic control. Combined with effects on ardiomyocytes, these properties could influence its arrhythmic potency. The short-term variability of beat-to-beat QT interval (STVQT ), induced by dofetilide is a strong surrogate of Torsades de pointes liability. Involvement of autonomic modulation in STVQT was investigated in healthy cynomolgus monkeys and beagle dogs by power spectral analysis under conditions of autonomic blockade with hexamethonium. Increase in STVQT induced by dofetilide in monkeys and dogs was closely associated with an enhancement of endogenous heart rate and QT interval high-frequency (HF) oscillations. These effects were fully suppressed under conditions of autonomic blockade with hexamethonium. Ventricular arrhythmias, including Torsades de pointes in monkeys, were prevented in both species when HF oscillations were suppressed by autonomic blockade. Similar enhancements of heart rate HF oscillations were found in dogs with other hERG blockers described as causing Torsades de pointes in humans. These results demonstrate for the first time that beat-to-beat ventricular repolarization variability and ventricular arrhythmias induced by dofetilide are dependent on endogenous HF autonomic oscillations in heart rate. When combined with evidence of hERG-blocking properties, enhancement of endogenous HF oscillations in heart rate could constitute an earlier and more sensitive biomarker than STVQT for Torsades de pointes liability, applicable to preclinical regulatory studies conducted in healthy animals. © 2015 The British Pharmacological Society.

Patients With Long-QT Syndrome Caused by Impaired hERG-Encoded Kv11.1 Potassium Channel Have Exaggerated Endocrine Pancreatic and Incretin Function Associated With Reactive Hypoglycemia

PubMed Central

Hyltén-Cavallius, Louise; Iepsen, Eva W.; Wewer Albrechtsen, Nicolai J.; Svendstrup, Mathilde; Lubberding, Anniek F.; Hartmann, Bolette; Jespersen, Thomas; Linneberg, Allan; Christiansen, Michael; Vestergaard, Henrik; Pedersen, Oluf; Holst, Jens J.; Kanters, Jørgen K.

2017-01-01

Background: Loss-of-function mutations in hERG (encoding the Kv11.1 voltage-gated potassium channel) cause long-QT syndrome type 2 (LQT2) because of prolonged cardiac repolarization. However, Kv11.1 is also present in pancreatic α and β cells and intestinal L and K cells, secreting glucagon, insulin, and the incretins glucagon-like peptide-1 (GLP-1) and GIP (glucose-dependent insulinotropic polypeptide), respectively. These hormones are crucial for glucose regulation, and long-QT syndrome may cause disturbed glucose regulation. We measured secretion of these hormones and cardiac repolarization in response to glucose ingestion in LQT2 patients with functional mutations in hERG and matched healthy participants, testing the hypothesis that LQT2 patients have increased incretin and β-cell function and decreased α-cell function, and thus lower glucose levels. Methods: Eleven patients with LQT2 and 22 sex-, age-, and body mass index–matched control participants underwent a 6-hour 75-g oral glucose tolerance test with ECG recording and blood sampling for measurements of glucose, insulin, C-peptide, glucagon, GLP-1, and GIP. Results: In comparison with matched control participants, LQT2 patients had 56% to 78% increased serum insulin, serum C-peptide, plasma GLP-1, and plasma GIP responses (P=0.03–0.001) and decreased plasma glucose levels after glucose ingestion (P=0.02) with more symptoms of hypoglycemia (P=0.04). Sixty-three percent of LQT2 patients developed hypoglycemic plasma glucose levels (<70 mg/dL) versus 36% control participants (P=0.16), and 18% patients developed serious hypoglycemia (<50 mg/dL) versus none of the controls. LQT2 patients had defective glucagon responses to low glucose, P=0.008. β-Cell function (Insulin Secretion Sensitivity Index-2) was 2-fold higher in LQT2 patients than in controls (4398 [95% confidence interval, 2259–8562] versus 2156 [1961–3201], P=0.03). Pharmacological Kv11.1 blockade (dofetilide) in rats had similar effect, and small interfering RNA inhibition of hERG in β and L cells increased insulin and GLP-1 secretion up to 50%. Glucose ingestion caused cardiac repolarization disturbances with increased QTc intervals in both patients and controls, but with a 122% greater increase in QTcF interval in LQT2 patients (P=0.004). Conclusions: Besides a prolonged cardiac repolarization phase, LQT2 patients display increased GLP-1, GIP, and insulin secretion and defective glucagon secretion, causing decreased plasma glucose and thus increased risk of hypoglycemia. Furthermore, glucose ingestion increased QT interval and aggravated the cardiac repolarization disturbances in LQT2 patients. Clinical Trial Registration: URL: http://clinicaltrials.gov. Unique identifier: NCT02775513. PMID:28235848

Gating mechanism of Kv11.1 (hERG) K+ channels without covalent connection between voltage sensor and pore domains.

PubMed

de la Peña, Pilar; Domínguez, Pedro; Barros, Francisco

2018-03-01

Kv11.1 (hERG, KCNH2) is a voltage-gated potassium channel crucial in setting the cardiac rhythm and the electrical behaviour of several non-cardiac cell types. Voltage-dependent gating of Kv11.1 can be reconstructed from non-covalently linked voltage sensing and pore modules (split channels), challenging classical views of voltage-dependent channel activation based on a S4-S5 linker acting as a rigid mechanical lever to open the gate. Progressive displacement of the split position from the end to the beginning of the S4-S5 linker induces an increasing negative shift in activation voltage dependence, a reduced z g value and a more negative ΔG 0 for current activation, an almost complete abolition of the activation time course sigmoid shape and a slowing of the voltage-dependent deactivation. Channels disconnected at the S4-S5 linker near the S4 helix show a destabilization of the closed state(s). Furthermore, the isochronal ion current mode shift magnitude is clearly reduced in the different splits. Interestingly, the progressive modifications of voltage dependence activation gating by changing the split position are accompanied by a shift in the voltage-dependent availability to a methanethiosulfonate reagent of a Cys introduced at the upper S4 helix. Our data demonstrate for the first time that alterations in the covalent connection between the voltage sensor and the pore domains impact on the structural reorganizations of the voltage sensor domain. Also, they support the hypothesis that the S4-S5 linker integrates signals coming from other cytoplasmic domains that constitute either an important component or a crucial regulator of the gating machinery in Kv11.1 and other KCNH channels.

Assessment of drug-induced arrhythmic risk using limit cycle and autocorrelation analysis of human iPSC-cardiomyocyte contractility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirby, R. Jason

2016-08-15

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicitymore » library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation. - Highlights: • Impedance-based monitoring of human iPSC-derived cardiomyocyte contractility • Limit cycle analysis of impedance data identifies aberrant oscillation patterns. • Nonlinear autocorrelation function quantifies beat irregularity. • Identification of hERG and non-hERG inhibitors with known risk of arrhythmia • Automated software processes limit cycle and autocorrelation analyses of 96w data.« less

Systematic review: cardiovascular safety profile of 5-HT(4) agonists developed for gastrointestinal disorders.

PubMed

Tack, J; Camilleri, M; Chang, L; Chey, W D; Galligan, J J; Lacy, B E; Müller-Lissner, S; Quigley, E M M; Schuurkes, J; De Maeyer, J H; Stanghellini, V

2012-04-01

The nonselective 5-HT(4) receptor agonists, cisapride and tegaserod have been associated with cardiovascular adverse events (AEs). To perform a systematic review of the safety profile, particularly cardiovascular, of 5-HT(4) agonists developed for gastrointestinal disorders, and a nonsystematic summary of their pharmacology and clinical efficacy. Articles reporting data on cisapride, clebopride, prucalopride, mosapride, renzapride, tegaserod, TD-5108 (velusetrag) and ATI-7505 (naronapride) were identified through a systematic search of the Cochrane Library, Medline, Embase and Toxfile. Abstracts from UEGW 2006-2008 and DDW 2008-2010 were searched for these drug names, and pharmaceutical companies approached to provide unpublished data. Retrieved articles on pharmacokinetics, human pharmacodynamics and clinical data with these 5-HT(4) agonists, are reviewed and summarised nonsystematically. Articles relating to cardiac safety and tolerability of these agents, including any relevant case reports, are reported systematically. Two nonselective 5-HT(4) agonists had reports of cardiovascular AEs: cisapride (QT prolongation) and tegaserod (ischaemia). Interactions with, respectively, the hERG cardiac potassium channel and 5-HT(1) receptor subtypes have been suggested to account for these effects. No cardiovascular safety concerns were reported for the newer, selective 5-HT(4) agonists prucalopride, velusetrag, naronapride, or for nonselective 5-HT(4) agonists with no hERG or 5-HT(1) affinity (renzapride, clebopride, mosapride). 5-HT(4) agonists for GI disorders differ in chemical structure and selectivity for 5-HT(4) receptors. Selectivity for 5-HT(4) over non-5-HT(4) receptors may influence the agent's safety and overall risk-benefit profile. Based on available evidence, highly selective 5-HT(4) agonists may offer improved safety to treat patients with impaired GI motility. © 2012 Blackwell Publishing Ltd.

Systematic review: cardiovascular safety profile of 5-HT4 agonists developed for gastrointestinal disorders

PubMed Central

Tack, J; Camilleri, M; Chang, L; Chey, W D; Galligan, J J; Lacy, B E; Müller-Lissner, S; Quigley, E M M; Schuurkes, J; Maeyer, J H; Stanghellini, V

2012-01-01

Summary Background The nonselective 5-HT4 receptor agonists, cisapride and tegaserod have been associated with cardiovascular adverse events (AEs). Aim To perform a systematic review of the safety profile, particularly cardiovascular, of 5-HT4 agonists developed for gastrointestinal disorders, and a nonsystematic summary of their pharmacology and clinical efficacy. Methods Articles reporting data on cisapride, clebopride, prucalopride, mosapride, renzapride, tegaserod, TD-5108 (velusetrag) and ATI-7505 (naronapride) were identified through a systematic search of the Cochrane Library, Medline, Embase and Toxfile. Abstracts from UEGW 2006–2008 and DDW 2008–2010 were searched for these drug names, and pharmaceutical companies approached to provide unpublished data. Results Retrieved articles on pharmacokinetics, human pharmacodynamics and clinical data with these 5-HT4 agonists, are reviewed and summarised nonsystematically. Articles relating to cardiac safety and tolerability of these agents, including any relevant case reports, are reported systematically. Two nonselective 5-HT4 agonists had reports of cardiovascular AEs: cisapride (QT prolongation) and tegaserod (ischaemia). Interactions with, respectively, the hERG cardiac potassium channel and 5-HT1 receptor subtypes have been suggested to account for these effects. No cardiovascular safety concerns were reported for the newer, selective 5-HT4 agonists prucalopride, velusetrag, naronapride, or for nonselective 5-HT4 agonists with no hERG or 5-HT1 affinity (renzapride, clebopride, mosapride). Conclusions 5-HT4 agonists for GI disorders differ in chemical structure and selectivity for 5-HT4 receptors. Selectivity for 5-HT4 over non-5-HT4 receptors may influence the agent's safety and overall risk–benefit profile. Based on available evidence, highly selective 5-HT4 agonists may offer improved safety to treat patients with impaired GI motility. PMID:22356640

Functional characterization of Kv11.1 (hERG) potassium channels split in the voltage-sensing domain.

PubMed

de la Peña, Pilar; Domínguez, Pedro; Barros, Francisco

2018-03-23

Voltage-dependent KCNH family potassium channel functionality can be reconstructed using non-covalently linked voltage-sensing domain (VSD) and pore modules (split channels). However, the necessity of a covalent continuity for channel function has not been evaluated at other points within the two functionally independent channel modules. We find here that by cutting Kv11.1 (hERG, KCNH2) channels at the different loops linking the transmembrane spans of the channel core, not only channels split at the S4-S5 linker level, but also those split at the intracellular S2-S3 and the extracellular S3-S4 loops, yield fully functional channel proteins. Our data indicate that albeit less markedly, channels split after residue 482 in the S2-S3 linker resemble the uncoupled gating phenotype of those split at the C-terminal end of the VSD S4 transmembrane segment. Channels split after residues 514 and 518 in the S3-S4 linker show gating characteristics similar to those of the continuous wild-type channel. However, breaking the covalent link at this level strongly accelerates the voltage-dependent accessibility of a membrane impermeable methanethiosulfonate reagent to an engineered cysteine at the N-terminal region of the S4 transmembrane helix. Thus, besides that of the S4-S5 linker, structural integrity of the intracellular S2-S3 linker seems to constitute an important factor for proper transduction of VSD rearrangements to opening and closing the cytoplasmic gate. Furthermore, our data suggest that the short and probably rigid characteristics of the extracellular S3-S4 linker are not an essential component of the Kv11.1 voltage sensing machinery.

Kv11.1 (hERG)-induced cardiotoxicity: a molecular insight from a binding kinetics study of prototypical Kv11.1 (hERG) inhibitors

PubMed Central

Yu, Z; IJzerman, A P; Heitman, L H

2015-01-01

Background and Purpose Drug-induced arrhythmia due to blockade of the Kv11.1 channel (also known as the hERG K+ channel) is a frequent side effect. Previous studies have primarily focused on equilibrium parameters, i.e. affinity or potency, of drug candidates at the channel. The aim of this study was to determine the kinetics of the interaction with the channel for a number of known Kv11.1 blockers and to explore a possible correlation with the affinity or physicochemical properties of these compounds. Experimental Approach The affinity and kinetic parameters of 15 prototypical Kv11.1 inhibitors were evaluated in a number of [3H]-dofetilide binding assays. The lipophilicity (logKW-C8) and membrane partitioning (logKW-IAM) of these compounds were determined by means of HPLC analysis. Key Results A novel [3H]-dofetilide competition association assay was set up and validated, which allowed us to determine the binding kinetics of the Kv11.1 blockers used in this study. Interestingly, the compounds' affinities (Ki values) were correlated to their association rates rather than dissociation rates. Overall lipophilicity or membrane partitioning of the compounds were not correlated to their affinity or rate constants for the channel. Conclusions and Implications A compound's affinity for the Kv11.1 channel is determined by its rate of association with the channel, while overall lipophilicity and membrane affinity are not. In more general terms, our findings provide novel insights into the mechanism of action for a compound's activity at the Kv11.1 channel. This may help to elucidate how Kv11.1-induced cardiotoxicity is governed and how it can be circumvented in the future. PMID:25296617

Oxidative Inactivation of the Lipid Phosphatase Phosphatase and Tensin Homolog on Chromosome Ten (PTEN) as a Novel Mechanism of Acquired Long QT Syndrome*

PubMed Central

Wan, Xiaoping; Dennis, Adrienne T.; Obejero-Paz, Carlos; Overholt, Jeffrey L.; Heredia-Moya, Jorge; Kirk, Kenneth L.; Ficker, Eckhard

2011-01-01

The most common cause of cardiac side effects of pharmaco-therapy is acquired long QT syndrome, which is characterized by abnormal cardiac repolarization and most often caused by direct blockade of the cardiac potassium channel human ether a-go-go-related gene (hERG). However, little is known about therapeutic compounds that target ion channels other than hERG. We have discovered that arsenic trioxide (As2O3), a very potent antineoplastic compound for the treatment of acute promyelocytic leukemia, is proarrhythmic via two separate mechanisms: a well characterized inhibition of hERG/IKr trafficking and a poorly understood increase of cardiac calcium currents. We have analyzed the latter mechanism in the present study using biochemical and electrophysiological methods. We find that oxidative inactivation of the lipid phosphatase PTEN by As2O3 enhances cardiac calcium currents in the therapeutic concentration range via a PI3Kα-dependent increase in phosphatidylinositol 3,4,5-triphosphate (PIP3) production. In guinea pig ventricular myocytes, even a modest reduction in PTEN activity is sufficient to increase cellular PIP3 levels. Under control conditions, PIP3 levels are kept low by PTEN and do not affect calcium current amplitudes. Based on pharmacological experiments and intracellular infusion of PIP3, we propose that in guinea pig ventricular myocytes, PIP3 regulates calcium currents independently of the protein kinase Akt along a pathway that includes a secondary oxidation-sensitive target. Overall, our report describes a novel form of acquired long QT syndrome where the target modified by As2O3 is an intracellular signaling cascade. PMID:21097842

Reproducible model development in the cardiac electrophysiology Web Lab.

PubMed

Daly, Aidan C; Clerx, Michael; Beattie, Kylie A; Cooper, Jonathan; Gavaghan, David J; Mirams, Gary R

2018-05-26

The modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

PubMed

Algamal, Z Y; Lee, M H

2017-01-01

A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

PubMed

Rajagopal, Rekha; Ranganathan, Vidhyapriya

2018-06-05

Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram.

PubMed

Patel, Nikunjkumar; Wiśniowska, Barbara; Jamei, Masoud; Polak, Sebastian

2017-11-27

A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a 'virtual twin' of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (I Kr , I Ks , I CaL ); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment.

Electrocardiographic Biomarkers for Detection of Drug-Induced Late Sodium Current Block

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vicente, Jose; Johannesen, Lars; Hosseini, Meisam

Drugs that prolong the heart rate corrected QT interval (QTc) on the electrocardiogram (ECG) by blocking the hERG potassium channel and also block inward currents (late sodium or L-type calcium) are not associated with torsade de pointes (e.g. ranolazine and verapamil). Furthermore, identifying ECG signs of late sodium current block could aid in the determination of proarrhythmic risk for new drugs. A new cardiac safety paradigm for drug development (the "CiPA" initiative) will involve the preclinical assessment of multiple human cardiac ion channels and ECG biomarkers are needed to determine if there are unexpected ion channel effects in humans.

Electrocardiographic Biomarkers for Detection of Drug-Induced Late Sodium Current Block

DOE PAGES

Vicente, Jose; Johannesen, Lars; Hosseini, Meisam; ...

2016-12-30

Drugs that prolong the heart rate corrected QT interval (QTc) on the electrocardiogram (ECG) by blocking the hERG potassium channel and also block inward currents (late sodium or L-type calcium) are not associated with torsade de pointes (e.g. ranolazine and verapamil). Furthermore, identifying ECG signs of late sodium current block could aid in the determination of proarrhythmic risk for new drugs. A new cardiac safety paradigm for drug development (the "CiPA" initiative) will involve the preclinical assessment of multiple human cardiac ion channels and ECG biomarkers are needed to determine if there are unexpected ion channel effects in humans.

Anti-addiction drug ibogaine inhibits hERG channels: a cardiac arrhythmia risk!

PubMed Central

Boehm, Stefan; Sandtner, Walter; Hilber, Karlheinz

2016-01-01

Ibogaine, an alkaloid derived from the African shrub Tabernanthe iboga, has shown promising anti-addictive properties in animals. Anecdotal evidence suggests that ibogaine is also anti-addictive in humans. Thus, it alleviates drug craving and impedes relapse of drug use. Although not licensed as therapeutic drug, and despite evidence that ibogaine may disturb the rhythm of the heart, this alkaloid is currently used as an anti-addiction drug in alternative medicine. Here we report that therapeutic concentrations of ibogaine reduce currents through human ERG potassium channels. Thereby, we provide a mechanism by which ibogaine may generate life-threatening cardiac arrhythmias. PMID:22458604

Indolcarboxamide is a preclinical candidate for treating multidrug-resistant tuberculosis.

PubMed

Rao, Srinivasa P S; Lakshminarayana, Suresh B; Kondreddi, Ravinder R; Herve, Maxime; Camacho, Luis R; Bifani, Pablo; Kalapala, Sarath K; Jiricek, Jan; Ma, Ng L; Tan, Bee H; Ng, Seow H; Nanjundappa, Mahesh; Ravindran, Sindhu; Seah, Peck G; Thayalan, Pamela; Lim, Siao H; Lee, Boon H; Goh, Anne; Barnes, Whitney S; Chen, Zhong; Gagaring, Kerstin; Chatterjee, Arnab K; Pethe, Kevin; Kuhen, Kelli; Walker, John; Feng, Gu; Babu, Sreehari; Zhang, Lijun; Blasco, Francesca; Beer, David; Weaver, Margaret; Dartois, Veronique; Glynne, Richard; Dick, Thomas; Smith, Paul W; Diagana, Thierry T; Manjunatha, Ujjini H

2013-12-04

New chemotherapeutic compounds against multidrug-resistant Mycobacterium tuberculosis (Mtb) are urgently needed to combat drug resistance in tuberculosis (TB). We have identified and characterized the indolcarboxamides as a new class of antitubercular bactericidal agent. Genetic and lipid profiling studies identified the likely molecular target of indolcarboxamides as MmpL3, a transporter of trehalose monomycolate that is essential for mycobacterial cell wall biosynthesis. Two lead candidates, NITD-304 and NITD-349, showed potent activity against both drug-sensitive and multidrug-resistant clinical isolates of Mtb. Promising pharmacokinetic profiles of both compounds after oral dosing in several species enabled further evaluation for efficacy and safety. NITD-304 and NITD-349 were efficacious in treating both acute and chronic Mtb infections in mouse efficacy models. Furthermore, dosing of NITD-304 and NITD-349 for 2 weeks in exploratory rat toxicology studies revealed a promising safety margin. Finally, neither compound inhibited the activity of major cytochrome P-450 enzymes or the hERG (human ether-a-go-go related gene) channel. These results suggest that NITD-304 and NITD-349 should undergo further development as a potential treatment for multidrug-resistant TB.

Optimized S-Trityl-l-cysteine-Based Inhibitors of Kinesin Spindle Protein with Potent in Vivo Antitumor Activity in Lung Cancer Xenograft Models

PubMed Central

2013-01-01

The mitotic kinesin Eg5 is critical for the assembly of the mitotic spindle and is a promising chemotherapy target. Previously, we identified S-trityl-l-cysteine as a selective inhibitor of Eg5 and developed triphenylbutanamine analogues with improved potency, favorable drug-like properties, but moderate in vivo activity. We report here their further optimization to produce extremely potent inhibitors of Eg5 (Kiapp < 10 nM) with broad-spectrum activity against cancer cell lines comparable to the Phase II drug candidates ispinesib and SB-743921. They have good oral bioavailability and pharmacokinetics and induced complete tumor regression in nude mice explanted with lung cancer patient xenografts. Furthermore, they display fewer liabilities with CYP-metabolizing enzymes and hERG compared with ispinesib and SB-743921, which is important given the likely application of Eg5 inhibitors in combination therapies. We present the case for this preclinical series to be investigated in single and combination chemotherapies, especially targeting hematological malignancies. PMID:23394180

Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

NASA Astrophysics Data System (ADS)

Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.

2018-02-01

The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.

A Model Assessment and Classification System for Men and Women in Correctional Institutions.

ERIC Educational Resources Information Center

Hellervik, Lowell W.; And Others

The report describes a manpower assessment and classification system for criminal offenders directed towards making practical training and job classification decisions. The model is not concerned with custody classifications except as they affect occupational/training possibilities. The model combines traditional procedures of vocational…

Discovery of potent and selective cytotoxic activity of new quinazoline-ureas against TMZ-resistant glioblastoma multiforme (GBM).

PubMed

Elkamhawy, Ahmed; Viswanath, Ambily Nath Indu; Pae, Ae Nim; Kim, Hyeon Young; Heo, Jin-Chul; Park, Woo-Kyu; Lee, Chong-Ock; Yang, Heekyoung; Kim, Kang Ho; Nam, Do-Hyun; Seol, Ho Jun; Cho, Heeyeong; Roh, Eun Joo

2015-10-20

Herein, we report new quinazoline-urea based compounds with potent cytotoxic activities against TMZ-resistant glioblastoma multiforme (GBM) cells. Low micromolar IC₅₀ values were exhibited over a panel of three primary GBM patient-derived cell cultures belonging to proneural (GBM-1), mesenchymal (GBM-2), and classical (GBM-3) subtypes. Eight compounds showed excellent selectivity indices for GBM cells comparing to a normal astrocyte cell line. In JC-1 assay, analogues 11, 12, 20, 22, and 24 exerted promising rates of mPTP opening induction towards proneural GBM subtype. Compounds 11, 20, and 24 bound to the translocator protein 18 kDa (TSPO) in submicromolar range using [(3)H] PK-11195 binding affinity assay. A homology model was built and docked models of 11, 12, 20, 22 and 24 were generated for describing their plausible binding modes in TSPO. In 3D clonogenic assay, compound 20 manifested potent tumoricidal effects on TMZ-resistant GBM cells even at submicromolar concentrations. In addition, CYP450 and hERG assays presented a safe toxicity profile of 20. Taken as a whole, this report presents compound 20 as a potent, selective and safe GBM cytotoxic agent which constitutes a promising direction against TMZ-resistant GBM. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Evolutionary Strategies for Protein Folding

NASA Astrophysics Data System (ADS)

Murthy Gopal, Srinivasa; Wenzel, Wolfgang

2006-03-01

The free energy approach for predicting the protein tertiary structure describes the native state of a protein as the global minimum of an appropriate free-energy forcefield. The low-energy region of the free-energy landscape of a protein is extremely rugged. Efficient optimization methods must therefore speed up the search for the global optimum by avoiding high energy transition states, adapt large scale moves or accept unphysical intermediates. Here we investigate an evolutionary strategies(ES) for optimizing a protein conformation in our all-atom free-energy force field([1],[2]). A set of random conformations is evolved using an ES to get a diverse population containing low energy structure. The ES is shown to balance energy improvement and yet maintain diversity in structures. The ES is implemented as a master-client model for distributed computing. Starting from random structures and by using this optimization technique, we were able to fold a 20 amino-acid helical protein and 16 amino-acid beta hairpin[3]. We compare ES to basin hopping method. [1]T. Herges and W. Wenzel,Biophys.J. 87,3100(2004) [2] A. Verma and W. Wenzel Stabilization and folding of beta-sheet and alpha-helical proteins in an all-atom free energy model(submitted)(2005) [3] S. M. Gopal and W. Wenzel Evolutionary Strategies for Protein Folding (in preparation)

Compensatory neurofuzzy model for discrete data classification in biomedical

NASA Astrophysics Data System (ADS)

Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

The research on medical image classification algorithm based on PLSA-BOW model.

PubMed

Cao, C H; Cao, H L

2016-04-29

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

Pharmacokinetics of hERG Channel Blocking Voacangine in Wistar Rats Applying a Validated LC-ESI-MS/MS Method.

PubMed

Mair, Christina E; de Miranda Silva, Carolina; Grienke, Ulrike; Kratz, Jadel M; Carreño, Fernando; Zimmermann, Estevan Sonego; de Araújo, Bibiana Verlindo; Dalla Costa, Teresa; Rollinger, Judith M

2016-07-01

Herbal preparations from Voacanga africana are used in West and Central African folk medicine and are also becoming increasingly popular as a legal high in Europe. Recently, the main alkaloid voacangine was found to be a potent human ether-à-go-go-related gene channel blocker in vitro. Blockage of this channel might imply possible cardiotoxicity. Therefore, the aim of this study was to characterise voacangine in vivo to assess its pharmacokinetics and to estimate if further studies to investigate its cardiotoxic risk are required. Male Wistar rats received different doses of voacangine as a pure compound and as a hydro-ethanolic extract of V. africana root bark with a quantified amount of 9.71 % voacangine. For the obtained data, a simultaneous population pharmacokinetics model was successfully developed, comprising a two-compartment model for i. v. dosing and a one-compartmental model with two first-order absorption rates for oral dosing. The absolute bioavailability of voacangine was determined to be 11-13 %. Model analysis showed significant differences in the first absorption rate constant for voacangine administered as a pure compound and voacangine from the extract of V. africana. Taking into account the obtained low bioavailability of voacangine, its cardiotoxic risk might be neglectable in healthy consumers, but may have a serious impact in light of drug/drug interactions and impaired health conditions. Georg Thieme Verlag KG Stuttgart · New York.

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

Looking at the ICF and human communication through the lens of classification theory.

PubMed

Walsh, Regina

2011-08-01

This paper explores the insights that classification theory can provide about the application of the International Classification of Functioning, Disability and Health (ICF) to communication. It first considers the relationship between conceptual models and classification systems, highlighting that classification systems in speech-language pathology (SLP) have not historically been based on conceptual models of human communication. It then overviews the key concepts and criteria of classification theory. Applying classification theory to the ICF and communication raises a number of issues, some previously highlighted through clinical application. Six focus questions from classification theory are used to explore these issues, and to propose the creation of an ICF-related conceptual model of communicating for the field of communication disability, which would address some of the issues raised. Developing a conceptual model of communication for SLP purposes closely articulated with the ICF would foster productive intra-professional discourse, while at the same time allow the profession to continue to use the ICF for purposes in inter-disciplinary discourse. The paper concludes by suggesting the insights of classification theory can assist professionals to apply the ICF to communication with the necessary rigour, and to work further in developing a conceptual model of human communication.

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds.

PubMed

Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M; Bloom, Peter H; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

2017-01-01

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

PubMed Central

Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

2017-01-01

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data. PMID:28403159

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

USGS Publications Warehouse

Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael J.; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

2017-01-01

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

Molecular Basis of Cardiac Delayed Rectifier Potassium Channel Function and Pharmacology.

PubMed

Wu, Wei; Sanguinetti, Michael C

2016-06-01

Human cardiomyocytes express 3 distinct types of delayed rectifier potassium channels. Human ether-a-go-go-related gene (hERG) channels conduct the rapidly activating current IKr; KCNQ1/KCNE1 channels conduct the slowly activating current IKs; and Kv1.5 channels conduct an ultrarapid activating current IKur. Here the authors provide a general overview of the mechanistic and structural basis of ion selectivity, gating, and pharmacology of the 3 types of cardiac delayed rectifier potassium ion channels. Most blockers bind to S6 residues that line the central cavity of the channel, whereas activators interact with the channel at 4 symmetric binding sites outside the cavity. Copyright © 2016 Elsevier Inc. All rights reserved.

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

USGS Publications Warehouse

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model.

PubMed

Madison, Matthew J; Bradshaw, Laine P

2015-06-01

Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other multidimensional measurement models. A priori specifications of which latent characteristics or attributes are measured by each item are a core element of the diagnostic assessment design. This item-attribute alignment, expressed in a Q-matrix, precedes and supports any inference resulting from the application of the diagnostic classification model. This study investigates the effects of Q-matrix design on classification accuracy for the log-linear cognitive diagnosis model. Results indicate that classification accuracy, reliability, and convergence rates improve when the Q-matrix contains isolated information from each measured attribute.

Emotion models for textual emotion classification

NASA Astrophysics Data System (ADS)

Bruna, O.; Avetisyan, H.; Holub, J.

2016-11-01

This paper deals with textual emotion classification which gained attention in recent years. Emotion classification is used in user experience, product evaluation, national security, and tutoring applications. It attempts to detect the emotional content in the input text and based on different approaches establish what kind of emotional content is present, if any. Textual emotion classification is the most difficult to handle, since it relies mainly on linguistic resources and it introduces many challenges to assignment of text to emotion represented by a proper model. A crucial part of each emotion detector is emotion model. Focus of this paper is to introduce emotion models used for classification. Categorical and dimensional models of emotion are explained and some more advanced approaches are mentioned.

Influence of Texture and Colour in Breast TMA Classification

PubMed Central

Fernández-Carrobles, M. Milagro; Bueno, Gloria; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial; González-López, Lucía

2015-01-01

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors. PMID:26513238

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

[The electronic use of the NANDA-, NOC- and NIC- classifications and implications for nursing practice].

PubMed

Bernhart-Just, Alexandra; Hillewerth, Kathrin; Holzer-Pruss, Christina; Paprotny, Monika; Zimmermann Heinrich, Heidi

2009-12-01

The data model developed on behalf of the Nursing Service Commission of the Canton of Zurich (Pflegedienstkommission des Kantons Zürich) is based on the NANDA nursing diagnoses, the Nursing Outcome Classification, and the Nursing Intervention Classification (NNN Classifications). It also includes integrated functions for cost-centered accounting, service recording, and the Swiss Nursing Minimum Data Set. The data model uses the NNN classifications to map a possible form of the nursing process in the electronic patient health record, where the nurse can choose nursing diagnoses, outcomes, and interventions relevant to the patient situation. The nurses' choice is guided both by the different classifications and their linkages, and the use of specific text components pre-defined for each classification and accessible through the respective linkages. This article describes the developed data model and illustrates its clinical application in a specific patient's situation. Preparatory work required for the implementation of NNN classifications in practical nursing such as content filtering and the creation of linkages between the NNN classifications are described. Against the background of documentation of the nursing process based on the DAPEP(1) data model, possible changes and requirements are deduced. The article provides a contribution to the discussion of a change in documentation of the nursing process by implementing nursing classifications in electronic patient records.

Classification of wines according to their production regions with the contained trace elements using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Tian, Ye; Yan, Chunhua; Zhang, Tianlong; Tang, Hongsheng; Li, Hua; Yu, Jialu; Bernard, Jérôme; Chen, Li; Martin, Serge; Delepine-Gilon, Nicole; Bocková, Jana; Veis, Pavel; Chen, Yanping; Yu, Jin

2017-09-01

Laser-induced breakdown spectroscopy (LIBS) has been applied to classify French wines according to their production regions. The use of the surface-assisted (or surface-enhanced) sample preparation method enabled a sub-ppm limit of detection (LOD), which led to the detection and identification of at least 22 metal and nonmetal elements in a typical wine sample including majors, minors and traces. An ensemble of 29 bottles of French wines, either red or white wines, from five production regions, Alsace, Bourgogne, Beaujolais, Bordeaux and Languedoc, was analyzed together with a wine from California, considered as an outlier. A non-supervised classification model based on principal component analysis (PCA) was first developed for the classification. The results showed a limited separation power of the model, which however allowed, in a step by step approach, to understand the physical reasons behind each step of sample separation and especially to observe the influence of the matrix effect in the sample classification. A supervised classification model was then developed based on random forest (RF), which is in addition a nonlinear algorithm. The obtained classification results were satisfactory with, when the parameters of the model were optimized, a classification accuracy of 100% for the tested samples. We especially discuss in the paper, the effect of spectrum normalization with an internal reference, the choice of input variables for the classification models and the optimization of parameters for the developed classification models.

Classification/Categorization Model of Instruction for Learning Disabled Students.

ERIC Educational Resources Information Center

Freund, Lisa A.

1987-01-01

Learning-disabled students deficient in classification and categorization require specific instruction in these skills. Use of a classification/categorization instructional model improved the questioning strategies of 60 learning-disabled students, aged 10 to 12. The use of similar models is discussed as a basis for instruction in science, social…

Using Unidimensional IRT Models for Dichotomous Classification via Computerized Classification Testing with Multidimensional Data.

ERIC Educational Resources Information Center

Lau, Che-Ming Allen; And Others

This study focused on the robustness of unidimensional item response theory (UIRT) models in computerized classification testing against violation of the unidimensionality assumption. The study addressed whether UIRT models remain acceptable under various testing conditions and dimensionality strengths. Monte Carlo simulation techniques were used…

Evidentiary Reasoning in Diagnostic Classification Models

ERIC Educational Resources Information Center

Levy, Roy

2009-01-01

In "Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art," Rupp and Templin (2008) undertake the ambitious task of providing a thorough portrait of the current state of diagnostic classification models (DCM). In this commentary, the author applauds Rupp and Templin for their…

The bitter pill: clinical drugs that activate the human bitter taste receptor TAS2R14.

PubMed

Levit, Anat; Nowak, Stefanie; Peters, Maximilian; Wiener, Ayana; Meyerhof, Wolfgang; Behrens, Maik; Niv, Masha Y

2014-03-01

Bitter taste receptors (TAS2Rs) mediate aversive response to toxic food, which is often bitter. These G-protein-coupled receptors are also expressed in extraoral tissues, and emerge as novel targets for therapeutic indications such as asthma and infection. Our goal was to identify ligands of the broadly tuned TAS2R14 among clinical drugs. Molecular properties of known human bitter taste receptor TAS2R14 agonists were incorporated into pharmacophore- and shape-based models and used to computationally predict additional ligands. Predictions were tested by calcium imaging of TAS2R14-transfected HEK293 cells. In vitro testing of the virtual screening predictions resulted in 30-80% success rates, and 15 clinical drugs were found to activate the TAS2R14. hERG potassium channel, which is predominantly expressed in the heart, emerged as a common off-target of bitter drugs. Despite immense chemical diversity of known TAS2R14 ligands, novel ligands and previously unknown polypharmacology of drugs were unraveled by in vitro screening of computational predictions. This enables rational repurposing of traditional and standard drugs for bitter taste signaling modulation for therapeutic indications.

Synthesis and pharmacological characterization of novel N-(trans-4-(2-(4-(benzo[d]isothiazol-3-yl)piperazin-1-yl)ethyl)cyclohexyl)amides as potential multireceptor atypical antipsychotics.

PubMed

Chen, Xiao-Wen; Sun, Yuan-Yuan; Fu, Lei; Li, Jian-Qi

2016-11-10

A series of novel benzisothiazolylpiperazine derivatives combining potent dopamine D2 and D3, and serotonin 5-HT1A and 5-HT2A receptor properties were synthesized and evaluated for their potential antipsychotic properties. The most-promising derivative was 9j. The unique pharmacological features of 9j were a high affinity for D2, D3, 5-HT1A, and 5-HT2A receptors, together with a 20-fold selectivity for the D3 versus D2 subtype, and a low affinity for muscarinic M1 (reducing the risk of anticholinergic side effects), and for hERG channels (reducing incidence of QT interval prolongation). In animal behavioral models, 9j inhibited the locomotor-stimulating effects of phencyclidine, blocked conditioned avoidance response, and improved the cognitive deficit in the novel object recognition tests in rats. 9j exhibited a low potential for catalepsy, consistent with results with risperidone. In addition, favorable brain penetration of 9j in rats was detected. These studies have demonstrated that 9j is a potential atypical antipsychotic candidate. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Blocking Alcoholic Steatosis in Mice with a Peripherally Restricted Purine Antagonist of the Type 1 Cannabinoid Receptor.

PubMed

Amato, George S; Manke, Amruta; Harris, Danni L; Wiethe, Robert W; Vasukuttan, Vineetha; Snyder, Rodney W; Lefever, Timothy W; Cortes, Ricardo; Zhang, Yanan; Wang, Shaobin; Runyon, Scott P; Maitra, Rangan

2018-05-24

Type 1 cannabinoid receptor (CB1) antagonists have demonstrated promise for the treatment of obesity, liver disease, metabolic syndrome, and dyslipidemias. However, the inhibition of CB1 receptors in the central nervous system can produce adverse effects, including depression, anxiety, and suicidal ideation. Efforts are now underway to produce peripherally restricted CB1 antagonists to circumvent CNS-associated undesirable effects. In this study, a series of analogues were explored in which the 4-aminopiperidine group of compound 2 was replaced with aryl- and heteroaryl-substituted piperazine groups both with and without a spacer. This resulted in mildly basic, potent antagonists of human CB1 (hCB1). The 2-chlorobenzyl piperazine, 25, was found to be potent ( K i = 8 nM); to be >1000-fold selective for hCB1 over hCB2; to have no hERG liability; and to possess favorable ADME properties including high oral absorption and negligible CNS penetration. Compound 25 was tested in a mouse model of alcohol-induced liver steatosis and found to be efficacious. Taken together, 25 represents an exciting lead compound for further clinical development or refinement.

Privacy-Preserving Evaluation of Generalization Error and Its Application to Model and Attribute Selection

NASA Astrophysics Data System (ADS)

Sakuma, Jun; Wright, Rebecca N.

Privacy-preserving classification is the task of learning or training a classifier on the union of privately distributed datasets without sharing the datasets. The emphasis of existing studies in privacy-preserving classification has primarily been put on the design of privacy-preserving versions of particular data mining algorithms, However, in classification problems, preprocessing and postprocessing— such as model selection or attribute selection—play a prominent role in achieving higher classification accuracy. In this paper, we show generalization error of classifiers in privacy-preserving classification can be securely evaluated without sharing prediction results. Our main technical contribution is a new generalized Hamming distance protocol that is universally applicable to preprocessing and postprocessing of various privacy-preserving classification problems, such as model selection in support vector machine and attribute selection in naive Bayes classification.

CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.

PubMed

Cestarelli, Valerio; Fiscon, Giulia; Felici, Giovanni; Bertolazzi, Paola; Weitschek, Emanuel

2016-03-01

Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class. We propose CAMUR, a new method that extracts multiple and equivalent classification models. CAMUR iteratively computes a rule-based classification model, calculates the power set of the genes present in the rules, iteratively eliminates those combinations from the data set, and performs again the classification procedure until a stopping criterion is verified. CAMUR includes an ad-hoc knowledge repository (database) and a querying tool.We analyze three different types of RNA-seq data sets (Breast, Head and Neck, and Stomach Cancer) from The Cancer Genome Atlas (TCGA) and we validate CAMUR and its models also on non-TCGA data. Our experimental results show the efficacy of CAMUR: we obtain several reliable equivalent classification models, from which the most frequent genes, their relationships, and the relation with a particular cancer are deduced. dmb.iasi.cnr.it/camur.php emanuel@iasi.cnr.it Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

The Impact of Model Misspecification on Parameter Estimation and Item-Fit Assessment in Log-Linear Diagnostic Classification Models

ERIC Educational Resources Information Center

Kunina-Habenicht, Olga; Rupp, Andre A.; Wilhelm, Oliver

2012-01-01

Using a complex simulation study we investigated parameter recovery, classification accuracy, and performance of two item-fit statistics for correct and misspecified diagnostic classification models within a log-linear modeling framework. The basic manipulated test design factors included the number of respondents (1,000 vs. 10,000), attributes (3…

A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

PubMed

Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

2014-06-01

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified). © 2013 Elsevier B.V. All rights reserved.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

PubMed

Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

PubMed Central

Yates, Katherine L.; Mellin, Camille; Caley, M. Julian; Radford, Ben T.; Meeuwig, Jessica J.

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability. PMID:27333202

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

A decision support model for investment on P2P lending platform.

PubMed

Zeng, Xiangxiang; Liu, Li; Leung, Stephen; Du, Jiangze; Wang, Xun; Li, Tao

2017-01-01

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace-Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone.

A decision support model for investment on P2P lending platform

PubMed Central

Liu, Li; Leung, Stephen; Du, Jiangze; Wang, Xun; Li, Tao

2017-01-01

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace—Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone. PMID:28877234

Anti-addiction drug ibogaine inhibits hERG channels: a cardiac arrhythmia risk.

PubMed

Koenig, Xaver; Kovar, Michael; Boehm, Stefan; Sandtner, Walter; Hilber, Karlheinz

2014-03-01

Ibogaine, an alkaloid derived from the African shrub Tabernanthe iboga, has shown promising anti-addictive properties in animals. Anecdotal evidence suggests that ibogaine is also anti-addictive in humans. Thus, it alleviates drug craving and impedes relapse of drug use. Although not licensed as therapeutic drug, and despite evidence that ibogaine may disturb the rhythm of the heart, this alkaloid is currently used as an anti-addiction drug in alternative medicine. Here, we report that therapeutic concentrations of ibogaine reduce currents through human ether-a-go-go-related gene potassium channels. Thereby, we provide a mechanism by which ibogaine may generate life-threatening cardiac arrhythmias. © 2012 The Authors, Addiction Biology © 2012 Society for the Study of Addiction.

Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art

ERIC Educational Resources Information Center

Rupp, Andre A.; Templin, Jonathan L.

2008-01-01

"Diagnostic classification models" (DCM) are frequently promoted by psychometricians as important modelling alternatives for analyzing response data in situations where multivariate classifications of respondents are made on the basis of multiple postulated latent skills. In this review paper, a definitional boundary of the space of DCM…

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Assessing three fuel classification systems and their maps using Forest Inventory and Analysis (FIA) surface fuel measurements

Treesearch

Robert E. Keane; Jason M. Herynk; Chris Toney; Shawn P. Urbanski; Duncan C. Lutes; Roger D. Ottmar

2015-01-01

Fuel classifications are integral tools in fire management and planning because they are used as inputs to fire behavior and effects simulation models. Fuel Loading Models (FLMs) and Fuel Characteristic Classification System (FCCSs) fuelbeds are the most popular classifications used throughout wildland fire science and management, but they have yet to be thoroughly...

A Classification Methodology and Retrieval Model to Support Software Reuse

DTIC Science & Technology

1988-01-01

Dewey Decimal Classification ( DDC 18), an enumerative scheme, occupies 40 pages [Buchanan 19791. Langridge [19731 states that the facets listed in the...sense of historical importance or wide spread use. The schemes are: Dewey Decimal Classification ( DDC ), Universal Decimal Classification (UDC...Classification Systems ..... ..... 2.3.3 Library Classification__- .52 23.3.1 Dewey Decimal Classification -53 2.33.2 Universal Decimal Classification 55 2333

Learning classification models with soft-label information.

PubMed

Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

2014-01-01

Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

Polarimetric SAR image classification based on discriminative dictionary learning model

NASA Astrophysics Data System (ADS)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities

PubMed Central

Cramer, Richard D.

2015-01-01

The possible applicability of the new template CoMFA methodology to the prediction of unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding affinities were used as training and/or prediction sets, making these 3D-QSAR models the most structurally diverse and among the largest ever. For six of the targets, X-ray crystallographic structures provided the aligned templates required as input (BACE, cdk1, chk2, carbonic anhydrase-II, factor Xa, PTP1B). For all targets including the other six (hERG, cyp3A4 binding, endocrine receptor, COX2, D2, and GABAa), six modeling protocols applied to only three familiar ligands provided six alternate sets of aligned templates. The statistical qualities of the six or seven models thus resulting for each individual target were remarkably similar. Also, perhaps unexpectedly, the standard deviations of the errors of cross-validation predictions accompanying model derivations were indistinguishable from the standard deviations of the errors of truly prospective predictions. These standard deviations of prediction ranged from 0.70 to 1.14 log units and averaged 0.89 (8x in concentration units) over the twelve targets, representing an average reduction of almost 50% in uncertainty, compared to the null hypothesis of “predicting” an unknown affinity to be the average of known affinities. These errors of prediction are similar to those from Tanimoto coefficients of fragment occurrence frequencies, the predominant approach to side effect prediction, which template CoMFA can augment by identifying additional active structural classes, by improving Tanimoto-only predictions, by yielding quantitative predictions of potency, and by providing interpretable guidance for avoiding or enhancing any specific target response. PMID:26065424

MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

EPA Science Inventory

Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

Chinese Sentence Classification Based on Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

Multilingual Twitter Sentiment Classification: The Role of Human Annotators

PubMed Central

Mozetič, Igor; Grčar, Miha; Smailović, Jasmina

2016-01-01

What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered. PMID:27149621

Vesicular stomatitis forecasting based on Google Trends

PubMed Central

Lu, Yi; Zhou, GuangYa; Chen, Qin

2018-01-01

Background Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. Methods American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. Results For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. Conclusion This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast. PMID:29385198

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

PubMed Central

Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

2017-01-01

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results. PMID:28467468

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

PubMed

Rogiers, Bart; Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

2017-01-01

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients With Lumbar Impairments.

PubMed

Werneke, Mark W; Edmond, Susan; Deutscher, Daniel; Ward, Jason; Grigsby, David; Young, Michelle; McGill, Troy; McClenahan, Brian; Weinberg, Jon; Davidow, Amy L

2016-09-01

Study Design Retrospective cohort. Background Patient-classification subgroupings may be important prognostic factors explaining outcomes. Objectives To determine effects of adding classification variables (McKenzie syndrome and pain patterns, including centralization and directional preference; Symptom Checklist Back Pain Prediction Model [SCL BPPM]; and the Fear-Avoidance Beliefs Questionnaire subscales of work and physical activity) to a baseline risk-adjusted model predicting functional status (FS) outcomes. Methods Consecutive patients completed a battery of questionnaires that gathered information on 11 risk-adjustment variables. Physical therapists trained in Mechanical Diagnosis and Therapy methods classified each patient by McKenzie syndromes and pain pattern. Functional status was assessed at discharge by patient-reported outcomes. Only patients with complete data were included. Risk of selection bias was assessed. Prediction of discharge FS was assessed using linear stepwise regression models, allowing 13 variables to enter the model. Significant variables were retained in subsequent models. Model power (R(2)) and beta coefficients for model variables were estimated. Results Two thousand sixty-six patients with lumbar impairments were evaluated. Of those, 994 (48%), 10 (<1%), and 601 (29%) were excluded due to incomplete psychosocial data, McKenzie classification data, and missing FS at discharge, respectively. The final sample for analyses was 723 (35%). Overall R(2) for the baseline prediction FS model was 0.40. Adding classification variables to the baseline model did not result in significant increases in R(2). McKenzie syndrome or pain pattern explained 2.8% and 3.0% of the variance, respectively. When pain pattern and SCL BPPM were added simultaneously, overall model R(2) increased to 0.44. Although none of these increases in R(2) were significant, some classification variables were stronger predictors compared with some other variables included in the baseline model. Conclusion The small added prognostic capabilities identified when combining McKenzie or pain-pattern classifications with the SCL BPPM classification did not significantly improve prediction of FS outcomes in this study. Additional research is warranted to investigate the importance of classification variables compared with those used in the baseline model to maximize predictive power. Level of Evidence Prognosis, level 4. J Orthop Sports Phys Ther 2016;46(9):726-741. Epub 31 Jul 2016. doi:10.2519/jospt.2016.6266.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

PubMed

Yu, Kezi; Quirk, J Gerald; Djurić, Petar M

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models

PubMed Central

Yu, Kezi; Quirk, J. Gerald

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting. PMID:28953927

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Seo, Young Wook; Yoon, Seung Chul; Park, Bosoon; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.

2013-05-01

Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficient=0.88) on the validation set.

A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

Applications of Diagnostic Classification Models: A Literature Review and Critical Commentary

ERIC Educational Resources Information Center

Sessoms, John; Henson, Robert A.

2018-01-01

Diagnostic classification models (DCMs) classify examinees based on the skills they have mastered given their test performance. This classification enables targeted feedback that can inform remedial instruction. Unfortunately, applications of DCMs have been criticized (e.g., no validity support). Generally, these evaluations have been brief and…

A fuzzy hill-climbing algorithm for the development of a compact associative classifier

NASA Astrophysics Data System (ADS)

Mitra, Soumyaroop; Lam, Sarah S.

2012-02-01

Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

A Generalized Mixture Framework for Multi-label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input–output and output–output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods. PMID:26613069

Acidic and basic drugs in medicinal chemistry: a perspective.

PubMed

Charifson, Paul S; Walters, W Patrick

2014-12-11

The acid/base properties of a molecule are among the most fundamental for drug action. However, they are often overlooked in a prospective design manner unless it has been established that a certain ionization state (e.g., quaternary base or presence of a carboxylic acid) appears to be required for activity. In medicinal chemistry optimization programs it is relatively common to attenuate basicity to circumvent undesired effects such as lack of biological selectivity or safety risks such as hERG or phospholipidosis. However, teams may not prospectively explore a range of carefully chosen compound pKa values as part of an overall chemistry strategy or design hypothesis. This review summarizes the potential advantages and disadvantages of both acidic and basic drugs and provides some new analyses based on recently available public data.

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Gastric precancerous diseases classification using CNN with a concise model.

PubMed

Zhang, Xu; Hu, Weiling; Chen, Fei; Liu, Jiquan; Yang, Yuanhang; Wang, Liangjing; Duan, Huilong; Si, Jianmin

2017-01-01

Gastric precancerous diseases (GPD) may deteriorate into early gastric cancer if misdiagnosed, so it is important to help doctors recognize GPD accurately and quickly. In this paper, we realize the classification of 3-class GPD, namely, polyp, erosion, and ulcer using convolutional neural networks (CNN) with a concise model called the Gastric Precancerous Disease Network (GPDNet). GPDNet introduces fire modules from SqueezeNet to reduce the model size and parameters about 10 times while improving speed for quick classification. To maintain classification accuracy with fewer parameters, we propose an innovative method called iterative reinforced learning (IRL). After training GPDNet from scratch, we apply IRL to fine-tune the parameters whose values are close to 0, and then we take the modified model as a pretrained model for the next training. The result shows that IRL can improve the accuracy about 9% after 6 iterations. The final classification accuracy of our GPDNet was 88.90%, which is promising for clinical GPD recognition.

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

NASA Astrophysics Data System (ADS)

Yu, Lean; Wang, Shouyang; Lai, K. K.

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

PubMed

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Lean waste classification model to support the sustainable operational practice

NASA Astrophysics Data System (ADS)

Sutrisno, A.; Vanany, I.; Gunawan, I.; Asjad, M.

2018-04-01

Driven by growing pressure for a more sustainable operational practice, improvement on the classification of non-value added (waste) is one of the prerequisites to realize sustainability of a firm. While the use of the 7 (seven) types of the Ohno model now becoming a versatile tool to reveal the lean waste occurrence. In many recent investigations, the use of the Seven Waste model of Ohno is insufficient to cope with the types of waste occurred in industrial practices at various application levels. Intended to a narrowing down this limitation, this paper presented an improved waste classification model based on survey to recent studies discussing on waste at various operational stages. Implications on the waste classification model to the body of knowledge and industrial practices are provided.

Incremental Validity of Multidimensional Proficiency Scores from Diagnostic Classification Models: An Illustration for Elementary School Mathematics

ERIC Educational Resources Information Center

Kunina-Habenicht, Olga; Rupp, André A.; Wilhelm, Oliver

2017-01-01

Diagnostic classification models (DCMs) hold great potential for applications in summative and formative assessment by providing discrete multivariate proficiency scores that yield statistically driven classifications of students. Using data from a newly developed diagnostic arithmetic assessment that was administered to 2032 fourth-grade students…

Identification the Relation between Active Basketball Classification Referees' Empathetic Tendencies and Their Problem Solving Abilities

ERIC Educational Resources Information Center

Karaçam, Aydin; Pulur, Atilla

2016-01-01

This study aims to determine the relation between basketball classification referees' problem solving ability and empathetic tendencies. Research model of the study is relational screening model. Sampling of the study is constituted by 124 male and 18 female basketball classification referees who made active refereeing within Turkish Basketball…

Classification of complex networks based on similarity of topological network features

NASA Astrophysics Data System (ADS)

Attar, Niousha; Aliakbary, Sadegh

2017-09-01

Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.

An Evaluation of Item Response Theory Classification Accuracy and Consistency Indices

ERIC Educational Resources Information Center

Wyse, Adam E.; Hao, Shiqi

2012-01-01

This article introduces two new classification consistency indices that can be used when item response theory (IRT) models have been applied. The new indices are shown to be related to Rudner's classification accuracy index and Guo's classification accuracy index. The Rudner- and Guo-based classification accuracy and consistency indices are…

A model-based test for treatment effects with probabilistic classifications.

PubMed

Cavagnaro, Daniel R; Davis-Stober, Clintin P

2018-05-21

Within modern psychology, computational and statistical models play an important role in describing a wide variety of human behavior. Model selection analyses are typically used to classify individuals according to the model(s) that best describe their behavior. These classifications are inherently probabilistic, which presents challenges for performing group-level analyses, such as quantifying the effect of an experimental manipulation. We answer this challenge by presenting a method for quantifying treatment effects in terms of distributional changes in model-based (i.e., probabilistic) classifications across treatment conditions. The method uses hierarchical Bayesian mixture modeling to incorporate classification uncertainty at the individual level into the test for a treatment effect at the group level. We illustrate the method with several worked examples, including a reanalysis of the data from Kellen, Mata, and Davis-Stober (2017), and analyze its performance more generally through simulation studies. Our simulations show that the method is both more powerful and less prone to type-1 errors than Fisher's exact test when classifications are uncertain. In the special case where classifications are deterministic, we find a near-perfect power-law relationship between the Bayes factor, derived from our method, and the p value obtained from Fisher's exact test. We provide code in an online supplement that allows researchers to apply the method to their own data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Assimilation of a knowledge base and physical models to reduce errors in passive-microwave classifications of sea ice

NASA Technical Reports Server (NTRS)

Maslanik, J. A.; Key, J.

1992-01-01

An expert system framework has been developed to classify sea ice types using satellite passive microwave data, an operational classification algorithm, spatial and temporal information, ice types estimated from a dynamic-thermodynamic model, output from a neural network that detects the onset of melt, and knowledge about season and region. The rule base imposes boundary conditions upon the ice classification, modifies parameters in the ice algorithm, determines a `confidence' measure for the classified data, and under certain conditions, replaces the algorithm output with model output. Results demonstrate the potential power of such a system for minimizing overall error in the classification and for providing non-expert data users with a means of assessing the usefulness of the classification results for their applications.

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

NASA Technical Reports Server (NTRS)

Abbey, Craig K.; Eckstein, Miguel P.

2002-01-01

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

Model-based Clustering of High-Dimensional Data in Astrophysics

NASA Astrophysics Data System (ADS)

Bouveyron, C.

2016-05-01

The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.

Visual attention based bag-of-words model for image classification

NASA Astrophysics Data System (ADS)

Wang, Qiwei; Wan, Shouhong; Yue, Lihua; Wang, Che

2014-04-01

Bag-of-words is a classical method for image classification. The core problem is how to count the frequency of the visual words and what visual words to select. In this paper, we propose a visual attention based bag-of-words model (VABOW model) for image classification task. The VABOW model utilizes visual attention method to generate a saliency map, and uses the saliency map as a weighted matrix to instruct the statistic process for the frequency of the visual words. On the other hand, the VABOW model combines shape, color and texture cues and uses L1 regularization logistic regression method to select the most relevant and most efficient features. We compare our approach with traditional bag-of-words based method on two datasets, and the result shows that our VABOW model outperforms the state-of-the-art method for image classification.

Diagnostic Classification Models and Multidimensional Adaptive Testing: A Commentary on Rupp and Templin

ERIC Educational Resources Information Center

Frey, Andreas; Carstensen, Claus H.

2009-01-01

On a general level, the objective of diagnostic classifications models (DCMs) lies in a classification of individuals regarding multiple latent skills. In this article, the authors show that this objective can be achieved by multidimensional adaptive testing (MAT) as well. The authors discuss whether or not the restricted applicability of DCMs can…

Discovery of 1-(3-(benzyloxy)pyridin-2-yl)-3-(2-(piperazin-1-yl)ethyl)urea: A new modulator for amyloid beta-induced mitochondrial dysfunction.

PubMed

Elkamhawy, Ahmed; Park, Jung-Eun; Hassan, Ahmed H E; Ra, Hyunhwa; Pae, Ae Nim; Lee, Jiyoun; Park, Beoung-Geon; Moon, Bongjin; Park, Hyun-Mee; Roh, Eun Joo

2017-03-10

Herein, we report a new series of aliphatic substituted pyridyl-urea small molecules synthesized as potential modulators for amyloid beta (Aβ) induced mitochondrial dysfunction. Their blocking activities against Aβ-induced mitochondrial permeability transition pore (mPTP) opening were evaluated by JC-1 assay which measures the change of mitochondrial membrane potential (ΔΨm). The inhibitory activity of sixteen compounds against Aβ-induced mPTP opening was superior or almost similar to that of the standard Cyclosporin A (CsA). Among them, 1-(3-(benzyloxy)pyridin-2-yl)-3-(2-(piperazin-1-yl)ethyl)urea (5x) effectively maintained mitochondrial function and cell viabilities on ATP assay, MTT assay, and ROS assay. Using CDocker algorithm, a molecular docking model presented a plausible binding mode for 5x with cyclophilin D (CypD) receptor as a major component of mPTP. Moreover, hERG and BBB-PAMPA assays presented safe cardiotoxicity and high CNS bioavailability profiles for 5x. Taken as a whole, this report presents compound 5x as a new nonpeptidyl mPTP blocker may hold a promise for further development of Alzheimer's disease (AD) therapeutics. Copyright © 2016. Published by Elsevier Masson SAS.

Oxabicyclooctane-Linked Novel Bacterial Topoisomerase Inhibitors as Broad Spectrum Antibacterial Agents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Sheo B.; Kaelin, David E.; Wu, Jin

Bacterial resistance is eroding the clinical utility of existing antibiotics necessitating the discovery of new agents. Bacterial type II topoisomerase is a clinically validated, highly effective, and proven drug target. This target is amenable to inhibition by diverse classes of inhibitors with alternative and distinct binding sites to quinolone antibiotics, thus enabling the development of agents that lack cross-resistance to quinolones. Described here are novel bacterial topoisomerase inhibitors (NBTIs), which are a new class of gyrase and topo IV inhibitors and consist of three distinct structural moieties. The substitution of the linker moiety led to discovery of potent broad-spectrum NBTIsmore » with reduced off-target activity (hERG IC50 > 18 μM) and improved physical properties. AM8191 is bactericidal and selectively inhibits DNA synthesis and Staphylococcus aureus gyrase (IC50 = 1.02 μM) and topo IV (IC50 = 10.4 μM). AM8191 showed parenteral and oral efficacy (ED50) at less than 2.5 mg/kg doses in a S. aureus murine infection model. A cocrystal structure of AM8191 bound to S. aureus DNA-gyrase showed binding interactions similar to that reported for GSK299423, displaying a key contact of Asp83 with the basic amine at position-7 of the linker.« less

BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.

PubMed

Guo, Yang; Liu, Shuhui; Li, Zhanhuai; Shang, Xuequn

2018-04-11

The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes by using deep learning on high-dimensional and small-scale biology data.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Logical-rule models of classification response times: a synthesis of mental-architecture, random-walk, and decision-bound approaches.

PubMed

Fific, Mario; Little, Daniel R; Nosofsky, Robert M

2010-04-01

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT-distribution data associated with individual stimuli in tasks of speeded perceptual classification. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Multi-element analysis of wines by ICP-MS and ICP-OES and their classification according to geographical origin in Slovenia.

PubMed

Selih, Vid S; Sala, Martin; Drgan, Viktor

2014-06-15

Inductively coupled plasma mass spectrometry and optical emission were used to determine the multi-element composition of 272 bottled Slovenian wines. To achieve geographical classification of the wines by their elemental composition, principal component analysis (PCA) and counter-propagation artificial neural networks (CPANN) have been used. From 49 elements measured, 19 were used to build the final classification models. CPANN was used for the final predictions because of its superior results. The best model gave 82% correct predictions for external set of the white wine samples. Taking into account the small size of whole Slovenian wine growing regions, we consider the classification results were very good. For the red wines, which were mostly represented from one region, even-sub region classification was possible with great precision. From the level maps of the CPANN model, some of the most important elements for classification were identified. Copyright © 2013 Elsevier Ltd. All rights reserved.

Histone Deacetylase Inhibitors Prolong Cardiac Repolarization through Transcriptional Mechanisms.

PubMed

Spence, Stan; Deurinck, Mark; Ju, Haisong; Traebert, Martin; McLean, LeeAnne; Marlowe, Jennifer; Emotte, Corinne; Tritto, Elaine; Tseng, Min; Shultz, Michael; Friedrichs, Gregory S

2016-09-01

Histone deacetylase (HDAC) inhibitors are an emerging class of anticancer agents that modify gene expression by altering the acetylation status of lysine residues of histone proteins, thereby inducing transcription, cell cycle arrest, differentiation, and cell death or apoptosis of cancer cells. In the clinical setting, treatment with HDAC inhibitors has been associated with delayed cardiac repolarization and in rare instances a lethal ventricular tachyarrhythmia known as torsades de pointes. The mechanism(s) of HDAC inhibitor-induced effects on cardiac repolarization is unknown. We demonstrate that administration of structurally diverse HDAC inhibitors to dogs causes delayed but persistent increases in the heart rate corrected QT interval (QTc), an in vivo measure of cardiac repolarization, at timepoints far removed from the Tmax for parent drug and metabolites. Transcriptional profiling of ventricular myocardium from dogs treated with various HDAC inhibitors demonstrated effects on genes involved in protein trafficking, scaffolding and insertion of various ion channels into the cell membrane as well as genes for specific ion channel subunits involved in cardiac repolarization. Extensive in vitro ion channel profiling of various structural classes of HDAC inhibitors (and their major metabolites) by binding and acute patch clamp assays failed to show any consistent correlations with direct ion channel blockade. Drug-induced rescue of an intracellular trafficking-deficient mutant potassium ion channel, hERG (G601S), and decreased maturation (glycosylation) of wild-type hERG expressed by CHO cells in vitro correlated with prolongation of QTc intervals observed in vivo The results suggest that HDAC inhibitor-induced prolongation of cardiac repolarization may be mediated in part by transcriptional changes of genes required for ion channel trafficking and localization to the sarcolemma. These data have broad implications for the development of these drug classes and suggest that the optimal time to assess potentially transcriptionally mediated physiologic effects will be delayed relative to an epigenetic drug's Tmax/Cmax. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Evaluation of Optogenetic Electrophysiology Tools in Human Stem Cell-Derived Cardiomyocytes.

PubMed

Björk, Susann; Ojala, Elina A; Nordström, Tommy; Ahola, Antti; Liljeström, Mikko; Hyttinen, Jari; Kankuri, Esko; Mervaala, Eero

2017-01-01

Current cardiac drug safety assessments focus on hERG channel block and QT prolongation for evaluating arrhythmic risks, whereas the optogenetic approach focuses on the action potential (AP) waveform generated by a monolayer of human cardiomyocytes beating synchronously, thus assessing the contribution of several ion channels on the overall drug effect. This novel tool provides arrhythmogenic sensitizing by light-induced pacing in combination with non-invasive, all-optical measurements of cardiomyocyte APs and will improve assessment of drug-induced electrophysiological aberrancies. With the help of patch clamp electrophysiology measurements, we aimed to investigate whether the optogenetic modifications alter human cardiomyocytes' electrophysiology and how well the optogenetic analyses perform against this gold standard. Patch clamp electrophysiology measurements of non-transduced stem cell-derived cardiomyocytes compared to cells expressing the commercially available optogenetic constructs Optopatch and CaViar revealed no significant changes in action potential duration (APD) parameters. Thus, inserting the optogenetic constructs into cardiomyocytes does not significantly affect the cardiomyocyte's electrophysiological properties. When comparing the two methods against each other (patch clamp vs. optogenetic imaging) we found no significant differences in APD parameters for the Optopatch transduced cells, whereas the CaViar transduced cells exhibited modest increases in APD-values measured with optogenetic imaging. Thus, to broaden the screen, we combined optogenetic measurements of membrane potential and calcium transients with contractile motion measured by video motion tracking. Furthermore, to assess how optogenetic measurements can predict changes in membrane potential, or early afterdepolarizations (EADs), cells were exposed to cumulating doses of E-4031, a hERG potassium channel blocker, and drug effects were measured at both spontaneous and paced beating rates (1, 2 Hz). Cumulating doses of E-4031 produced prolonged APDs, followed by EADs and drug-induced quiescence. These observations were corroborated by patch clamp and contractility measurements. Similar responses, although more modest were seen with the I Ks potassium channel blocker JNJ-303. In conclusion, optogenetic measurements of AP waveforms combined with optical pacing compare well with the patch clamp gold standard. Combined with video motion contractile measurements, optogenetic imaging provides an appealing alternative for electrophysiological screening of human cardiomyocyte responses in pharmacological efficacy and safety testings.

Evaluation of Optogenetic Electrophysiology Tools in Human Stem Cell-Derived Cardiomyocytes

PubMed Central

Björk, Susann; Ojala, Elina A.; Nordström, Tommy; Ahola, Antti; Liljeström, Mikko; Hyttinen, Jari; Kankuri, Esko; Mervaala, Eero

2017-01-01

Current cardiac drug safety assessments focus on hERG channel block and QT prolongation for evaluating arrhythmic risks, whereas the optogenetic approach focuses on the action potential (AP) waveform generated by a monolayer of human cardiomyocytes beating synchronously, thus assessing the contribution of several ion channels on the overall drug effect. This novel tool provides arrhythmogenic sensitizing by light-induced pacing in combination with non-invasive, all-optical measurements of cardiomyocyte APs and will improve assessment of drug-induced electrophysiological aberrancies. With the help of patch clamp electrophysiology measurements, we aimed to investigate whether the optogenetic modifications alter human cardiomyocytes' electrophysiology and how well the optogenetic analyses perform against this gold standard. Patch clamp electrophysiology measurements of non-transduced stem cell-derived cardiomyocytes compared to cells expressing the commercially available optogenetic constructs Optopatch and CaViar revealed no significant changes in action potential duration (APD) parameters. Thus, inserting the optogenetic constructs into cardiomyocytes does not significantly affect the cardiomyocyte's electrophysiological properties. When comparing the two methods against each other (patch clamp vs. optogenetic imaging) we found no significant differences in APD parameters for the Optopatch transduced cells, whereas the CaViar transduced cells exhibited modest increases in APD-values measured with optogenetic imaging. Thus, to broaden the screen, we combined optogenetic measurements of membrane potential and calcium transients with contractile motion measured by video motion tracking. Furthermore, to assess how optogenetic measurements can predict changes in membrane potential, or early afterdepolarizations (EADs), cells were exposed to cumulating doses of E-4031, a hERG potassium channel blocker, and drug effects were measured at both spontaneous and paced beating rates (1, 2 Hz). Cumulating doses of E-4031 produced prolonged APDs, followed by EADs and drug-induced quiescence. These observations were corroborated by patch clamp and contractility measurements. Similar responses, although more modest were seen with the IKs potassium channel blocker JNJ-303. In conclusion, optogenetic measurements of AP waveforms combined with optical pacing compare well with the patch clamp gold standard. Combined with video motion contractile measurements, optogenetic imaging provides an appealing alternative for electrophysiological screening of human cardiomyocyte responses in pharmacological efficacy and safety testings. PMID:29163220

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models.

PubMed

Lewicke, Aaron; Sazonov, Edward; Corwin, Michael J; Neuman, Michael; Schuckers, Stephanie

2008-01-01

Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.

Estimating workload using EEG spectral power and ERPs in the n-back task

NASA Astrophysics Data System (ADS)

Brouwer, Anne-Marie; Hogervorst, Maarten A.; van Erp, Jan B. F.; Heffelaar, Tobias; Zimmerman, Patrick H.; Oostenveld, Robert

2012-08-01

Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.

Effective classification of the prevalence of Schistosoma mansoni.

PubMed

Mitchell, Shira A; Pagano, Marcello

2012-12-01

To present an effective classification method based on the prevalence of Schistosoma mansoni in the community. We created decision rules (defined by cut-offs for number of positive slides), which account for imperfect sensitivity, both with a simple adjustment of fixed sensitivity and with a more complex adjustment of changing sensitivity with prevalence. To reduce screening costs while maintaining accuracy, we propose a pooled classification method. To estimate sensitivity, we use the De Vlas model for worm and egg distributions. We compare the proposed method with the standard method to investigate differences in efficiency, measured by number of slides read, and accuracy, measured by probability of correct classification. Modelling varying sensitivity lowers the lower cut-off more significantly than the upper cut-off, correctly classifying regions as moderate rather than lower, thus receiving life-saving treatment. The classification method goes directly to classification on the basis of positive pools, avoiding having to know sensitivity to estimate prevalence. For model parameter values describing worm and egg distributions among children, the pooled method with 25 slides achieves an expected 89.9% probability of correct classification, whereas the standard method with 50 slides achieves 88.7%. Among children, it is more efficient and more accurate to use the pooled method for classification of S. mansoni prevalence than the current standard method. © 2012 Blackwell Publishing Ltd.

Image Analysis and Classification Based on Soil Strength

DTIC Science & Technology

2016-08-01

Satellite imagery classification is useful for a variety of commonly used ap- plications, such as land use classification, agriculture , wetland...required use of a coinci- dent digital elevation model (DEM) and a high-resolution orthophoto- graph collected by the National Agriculture Imagery Program...14. ABSTRACT Satellite imagery classification is useful for a variety of commonly used applications, such as land use classification, agriculture

Assessing the Accuracy and Consistency of Language Proficiency Classification under Competing Measurement Models

ERIC Educational Resources Information Center

Zhang, Bo

2010-01-01

This article investigates how measurement models and statistical procedures can be applied to estimate the accuracy of proficiency classification in language testing. The paper starts with a concise introduction of four measurement models: the classical test theory (CTT) model, the dichotomous item response theory (IRT) model, the testlet response…

Bayesian Model Comparison for the Order Restricted RC Association Model

ERIC Educational Resources Information Center

Iliopoulos, G.; Kateri, M.; Ntzoufras, I.

2009-01-01

Association models constitute an attractive alternative to the usual log-linear models for modeling the dependence between classification variables. They impose special structure on the underlying association by assigning scores on the levels of each classification variable, which can be fixed or parametric. Under the general row-column (RC)…

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

A Wavelet Polarization Decomposition Net Model for Polarimetric SAR Image Classification

NASA Astrophysics Data System (ADS)

He, Chu; Ou, Dan; Yang, Teng; Wu, Kun; Liao, Mingsheng; Chen, Erxue

2014-11-01

In this paper, a deep model based on wavelet texture has been proposed for Polarimetric Synthetic Aperture Radar (PolSAR) image classification inspired by recent successful deep learning method. Our model is supposed to learn powerful and informative representations to improve the generalization ability for the complex scene classification tasks. Given the influence of speckle noise in Polarimetric SAR image, wavelet polarization decomposition is applied first to obtain basic and discriminative texture features which are then embedded into a Deep Neural Network (DNN) in order to compose multi-layer higher representations. We demonstrate that the model can produce a powerful representation which can capture some untraceable information from Polarimetric SAR images and show a promising achievement in comparison with other traditional SAR image classification methods for the SAR image dataset.

SYMPLECTIC INVARIANTS AND FLOWERS' CLASSIFICATION OF SHELL MODEL STATES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Helmers, K.

1961-01-01

Flowers has given a classification of shell model states in j-j coupling for a fixed number of nucleons in a shell with respect to a symplectic group. The relation between these classifications for the various nucleon numbers is studied and is found to be governed by another symplectic group, the transformations of which in general change the nucleon number. (auth)

[Analysis of binary classification repeated measurement data with GEE and GLMMs using SPSS software].

PubMed

An, Shengli; Zhang, Yanhong; Chen, Zheng

2012-12-01

To analyze binary classification repeated measurement data with generalized estimating equations (GEE) and generalized linear mixed models (GLMMs) using SPSS19.0. GEE and GLMMs models were tested using binary classification repeated measurement data sample using SPSS19.0. Compared with SAS, SPSS19.0 allowed convenient analysis of categorical repeated measurement data using GEE and GLMMs.

Effects of uncertainty and variability on population declines and IUCN Red List classifications.

PubMed

Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

2018-01-22

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories. © 2018 Society for Conservation Biology.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.

PubMed

Saa, Jaime F Delgado; Çetin, Müjdat

2012-04-01

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on autoregressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for the classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy.

Estimation and Q-Matrix Validation for Diagnostic Classification Models

ERIC Educational Resources Information Center

Feng, Yuling

2013-01-01

Diagnostic classification models (DCMs) are structured latent class models widely discussed in the field of psychometrics. They model subjects' underlying attribute patterns and classify subjects into unobservable groups based on their mastery of attributes required to answer the items correctly. The effective implementation of DCMs depends…

Surveillance system and method having an operating mode partitioned fault classification model

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2005-01-01

A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

NASA Astrophysics Data System (ADS)

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification.

PubMed

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

NASA Astrophysics Data System (ADS)

Rußwurm, M.; Körner, M.

2017-05-01

Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

PubMed Central

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-01-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Spectral-spatial classification of hyperspectral imagery with cooperative game

NASA Astrophysics Data System (ADS)

Zhao, Ji; Zhong, Yanfei; Jia, Tianyi; Wang, Xinyu; Xu, Yao; Shu, Hong; Zhang, Liangpei

2018-01-01

Spectral-spatial classification is known to be an effective way to improve classification performance by integrating spectral information and spatial cues for hyperspectral imagery. In this paper, a game-theoretic spectral-spatial classification algorithm (GTA) using a conditional random field (CRF) model is presented, in which CRF is used to model the image considering the spatial contextual information, and a cooperative game is designed to obtain the labels. The algorithm establishes a one-to-one correspondence between image classification and game theory. The pixels of the image are considered as the players, and the labels are considered as the strategies in a game. Similar to the idea of soft classification, the uncertainty is considered to build the expected energy model in the first step. The local expected energy can be quickly calculated, based on a mixed strategy for the pixels, to establish the foundation for a cooperative game. Coalitions can then be formed by the designed merge rule based on the local expected energy, so that a majority game can be performed to make a coalition decision to obtain the label of each pixel. The experimental results on three hyperspectral data sets demonstrate the effectiveness of the proposed classification algorithm.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

A classification model of Hyperion image base on SAM combined decision tree

NASA Astrophysics Data System (ADS)

Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

2009-10-01

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model heightens 9.9%.

A neural network detection model of spilled oil based on the texture analysis of SAR image

NASA Astrophysics Data System (ADS)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Nonlinear programming for classification problems in machine learning

NASA Astrophysics Data System (ADS)

Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

2016-10-01

We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

ECG signal analysis through hidden Markov models.

PubMed

Andreão, Rodrigo V; Dorizzi, Bernadette; Boudy, Jérôme

2006-08-01

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.

Workshop on Algorithms for Time-Series Analysis

NASA Astrophysics Data System (ADS)

Protopapas, Pavlos

2012-04-01

abstract-type="normal">SummaryThis Workshop covered the four major subjects listed below in two 90-minute sessions. Each talk or tutorial allowed questions, and concluded with a discussion. Classification: Automatic classification using machine-learning methods is becoming a standard in surveys that generate large datasets. Ashish Mahabal (Caltech) reviewed various methods, and presented examples of several applications. Time-Series Modelling: Suzanne Aigrain (Oxford University) discussed autoregressive models and multivariate approaches such as Gaussian Processes. Meta-classification/mixture of expert models: Karim Pichara (Pontificia Universidad Católica, Chile) described the substantial promise which machine-learning classification methods are now showing in automatic classification, and discussed how the various methods can be combined together. Event Detection: Pavlos Protopapas (Harvard) addressed methods of fast identification of events with low signal-to-noise ratios, enlarging on the characterization and statistical issues of low signal-to-noise ratios and rare events.

Active Learning of Classification Models with Likert-Scale Feedback.

PubMed

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

Active Learning of Classification Models with Likert-Scale Feedback

PubMed Central

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone. PMID:28979827

Development of a definition, classification system, and model for cultural geology

NASA Astrophysics Data System (ADS)

Mitchell, Lloyd W., III

The concept for this study is based upon a personal interest by the author, an American Indian, in promoting cultural perspectives in undergraduate college teaching and learning environments. Most academicians recognize that merged fields can enhance undergraduate curricula. However, conflict may occur when instructors attempt to merge social science fields such as history or philosophy with geoscience fields such as mining and geomorphology. For example, ideologies of Earth structures derived from scientific methodologies may conflict with historical and spiritual understandings of Earth structures held by American Indians. Specifically, this study addresses the problem of how to combine cultural studies with the geosciences into a new merged academic discipline called cultural geology. This study further attempts to develop the merged field of cultural geology using an approach consisting of three research foci: a definition, a classification system, and a model. Literature reviews were conducted for all three foci. Additionally, to better understand merged fields, a literature review was conducted specifically for academic fields that merged social and physical sciences. Methodologies concentrated on the three research foci: definition, classification system, and model. The definition was derived via a two-step process. The first step, developing keyword hierarchical ranking structures, was followed by creating and analyzing semantic word meaning lists. The classification system was developed by reviewing 102 classification systems and incorporating selected components into a system framework. The cultural geology model was created also utilizing a two-step process. A literature review of scientific models was conducted. Then, the definition and classification system were incorporated into a model felt to reflect the realm of cultural geology. A course syllabus was then developed that incorporated the resulting definition, classification system, and model. This study concludes that cultural geology can be introduced as a merged discipline by using a three-foci framework consisting of a definition, classification system, and model. Additionally, this study reveals that cultural beliefs, attitudes, and behaviors, can be incorporated into a geology course during the curriculum development process, using an approach known as 'learner-centered'. This study further concludes that cultural beliefs, derived from class members, are an important source of curriculum materials.

ICD-11 and DSM-5 personality trait domains capture categorical personality disorders: Finding a common ground.

PubMed

Bach, Bo; Sellbom, Martin; Skjernov, Mathias; Simonsen, Erik

2018-05-01

The five personality disorder trait domains in the proposed International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition are comparable in terms of Negative Affectivity, Detachment, Antagonism/Dissociality and Disinhibition. However, the International Classification of Diseases, 11th edition model includes a separate domain of Anankastia, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model includes an additional domain of Psychoticism. This study examined associations of International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domains, simultaneously, with categorical personality disorders. Psychiatric outpatients ( N = 226) were administered the Structured Clinical Interview for DSM-IV Axis II Personality Disorders Interview and the Personality Inventory for DSM-5. International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domain scores were obtained using pertinent scoring algorithms for the Personality Inventory for DSM-5. Associations between categorical personality disorders and trait domains were examined using correlation and multiple regression analyses. Both the International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models showed relevant continuity with categorical personality disorders and captured a substantial amount of their information. As expected, the International Classification of Diseases, 11th edition model was superior in capturing obsessive-compulsive personality disorder, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model was superior in capturing schizotypal personality disorder. These preliminary findings suggest that little information is 'lost' in a transition to trait domain models and potentially adds to narrowing the gap between Diagnostic and Statistical Manual of Mental Disorders, 5th edition and the proposed International Classification of Diseases, 11th edition model. Accordingly, the International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models may be used to delineate one another as well as features of familiar categorical personality disorder types. A preliminary category-to-domain 'cross walk' is provided in the article.

Automated connectionist-geostatistical classification as an approach to identify sea ice and land ice types, properties and provinces

NASA Astrophysics Data System (ADS)

Goetz-Weiss, L. R.; Herzfeld, U. C.; Trantow, T.; Hunke, E. C.; Maslanik, J. A.; Crocker, R. I.

2016-12-01

An important problem in model-data comparison is the identification of parameters that can be extracted from observational data as well as used in numerical models, which are typically based on idealized physical processes. Here, we present a suite of approaches to characterization and classification of sea ice and land ice types, properties and provinces based on several types of remote-sensing data. Applications will be given to not only illustrate the approach, but employ it in model evaluation and understanding of physical processes. (1) In a geostatistical characterization, spatial sea-ice properties in the Chukchi and Beaufort Sea and in Elsoon Lagoon are derived from analysis of RADARSAT and ERS-2 SAR data. (2) The analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification, which facilitates classification of different sea-ice types. (3) Characteristic sea-ice parameters, as resultant from the classification, can then be applied in model evaluation, as demonstrated for the ridging scheme of the Los Alamos sea ice model, CICE, using high-resolution altimeter and image data collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE). The characteristic parameters chosen in this application are directly related to deformation processes, which also underly the ridging scheme. (4) The method that is capable of the most complex classification tasks is the connectionist-geostatistical classification method. This approach has been developed to identify currently up to 18 different crevasse types in order to map progression of the surge through the complex Bering-Bagley Glacier System, Alaska, in 2011-2014. The analysis utilizes airborne altimeter data and video image data and satellite image data. Results of the crevasse classification are compare to fracture modeling and found to match.

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%

A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI).

PubMed

Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

2017-06-01

Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research.

Comparison analysis for classification algorithm in data mining and the study of model use

NASA Astrophysics Data System (ADS)

Chen, Junde; Zhang, Defu

2018-04-01

As a key technique in data mining, classification algorithm was received extensive attention. Through an experiment of classification algorithm in UCI data set, we gave a comparison analysis method for the different algorithms and the statistical test was used here. Than that, an adaptive diagnosis model for preventive electricity stealing and leakage was given as a specific case in the paper.

Use of DAVID algorithms for gene functional classification in a non-model organism, rainbow trout

USDA-ARS?s Scientific Manuscript database

Gene functional clustering is essential in transcriptome data analysis but software programs are not always suitable for use with non-model species. The DAVID Gene Functional Classification Tool has been widely used for soft clustering in model species, but requires adaptations for use in non-model ...

The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model

ERIC Educational Resources Information Center

Madison, Matthew J.; Bradshaw, Laine P.

2015-01-01

Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other…

Diagnostic Classification Models: Thoughts and Future Directions

ERIC Educational Resources Information Center

Henson, Robert A.

2009-01-01

The paper by Drs. Rupp and Templin provides a much needed step toward the general application of diagnostic classification modeling (DCMs). The authors have provided a summary of many of the concepts that one must consider to properly apply a DCM (which ranges from model selection and estimation, to assessing the appropriateness of the model using…

Identification and classification of similar looking food grains

NASA Astrophysics Data System (ADS)

Anami, B. S.; Biradar, Sunanda D.; Savakar, D. G.; Kulkarni, P. V.

2013-01-01

This paper describes the comparative study of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers by taking a case study of identification and classification of four pairs of similar looking food grains namely, Finger Millet, Mustard, Soyabean, Pigeon Pea, Aniseed, Cumin-seeds, Split Greengram and Split Blackgram. Algorithms are developed to acquire and process color images of these grains samples. The developed algorithms are used to extract 18 colors-Hue Saturation Value (HSV), and 42 wavelet based texture features. Back Propagation Neural Network (BPNN)-based classifier is designed using three feature sets namely color - HSV, wavelet-texture and their combined model. SVM model for color- HSV model is designed for the same set of samples. The classification accuracies ranging from 93% to 96% for color-HSV, ranging from 78% to 94% for wavelet texture model and from 92% to 97% for combined model are obtained for ANN based models. The classification accuracy ranging from 80% to 90% is obtained for color-HSV based SVM model. Training time required for the SVM based model is substantially lesser than ANN for the same set of images.

Classification Model for Damage Localization in a Plate Structure

NASA Astrophysics Data System (ADS)

Janeliukstis, R.; Ruchevskis, S.; Chate, A.

2018-01-01

The present study is devoted to the problem of damage localization by means of data classification. The commercial ANSYS finite-elements program was used to make a model of a cantilevered composite plate equipped with numerous strain sensors. The plate was divided into zones, and, for data classification purposes, each of them housed several points to which a point mass of magnitude 5 and 10% of plate mass was applied. At each of these points, a numerical modal analysis was performed, from which the first few natural frequencies and strain readings were extracted. The strain data for every point were the input for a classification procedure involving k nearest neighbors and decision trees. The classification model was trained and optimized by finetuning the key parameters of both algorithms. Finally, two new query points were simulated and subjected to a classification in terms of assigning a label to one of the zones of the plate, thus localizing these points. Damage localization results were compared for both algorithms and were found to be in good agreement with the actual application positions of point load.

Optimizing spectral CT parameters for material classification tasks

NASA Astrophysics Data System (ADS)

Rigie, D. S.; La Rivière, P. J.

2016-06-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies.

Optimizing Spectral CT Parameters for Material Classification Tasks

PubMed Central

Rigie, D. S.; La Rivière, P. J.

2017-01-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies. PMID:27227430

Two notions of conspicuity and the classification of phyllotaxis.

PubMed

Reick, Christian H

2002-04-07

Invoking cylindrical Bravais lattices, Adler (1974, 1977) proposed a mathematically precise definition for the botanical classification of phyllotaxis. It is based on opposed pairs of parastichy families, that are conspicuous and visible. Jean (1988) generalized this concept to non-opposed pairs of parastichy families. In the present paper it is shown that this generalization implies a notion of conspicuity different from Adler's. This is made obvious by redefining the key terms of the two approaches. Both classifications are well defined. For Adler's, this is shown by presenting a general proof for his conjecture that conspicuous (in the sense of Adler) opposed pairs of parastichy families are visible. There are indications that in applications to models of phyllotaxis (van Iterson model, inhibitor models) their solutions are better characterized by Jean's classification. The differences between Adler's and Jean's classification show up only in very rare cases, so that the practice of pattern determination is only insignificantly touched by the present results. It turns out that the widely used contact parastichy method to determine phyllotactic patterns gives results according to Jean's classification rather than Adler's. Copyright 2002 Elsevier Science Ltd. All rights reserved.

Computer discrimination procedures applicable to aerial and ERTS multispectral data

NASA Technical Reports Server (NTRS)

Richardson, A. J.; Torline, R. J.; Allen, W. A.

1970-01-01

Two statistical models are compared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.

Histopathological classification criteria of rat model of chronic prostatitis/chronic pelvic pain syndrome.

PubMed

Wang, Xianjin; Zhong, Shan; Xu, Tianyuan; Xia, Leilei; Zhang, Xiaohua; Zhu, Zhaowei; Zhang, Minguang; Shen, Zhoujun

2015-02-01

A variety of murine models of experimental prostatitis that mimic the phenotype of human chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) have been developed. However, there is still a lack of explicit diagnosis criteria about those animal model. Our study is to establish histopathological classification criteria, which will be conducive to evaluate the animal models. We firstly established a rat model of experimental autoimmune prostatitis that is considered a valid model for CP/CPPS. For modelling, male Sprague-Dawley rats were immunized with autologous prostate tissue homogenate supernatant emulsified with complete Freund's adjuvant by subcutaneous injection into abdominal flank and simultaneously immunized with pertussis-diphtheria-tetanus vaccine by intraperitoneal injection. Three immunizations were administered semimonthly. At the 45th day, animals were killed, and prostate tissues were examined for morphology. Histologically, the prostate tissues were characterized by lymphoproliferation, atrophy of acini, and chronic inflammatory cells infiltration in the stromal connective tissue around the acini or ducts. Finally, we built histopathological classification criteria incorporating inflammation locations (mesenchyme, glands, periglandular tissues), ranges (focal, multifocal, diffuse), and grades (grade I-IV). To verify the effectiveness and practicability of the histopathological classification criteria, we conducted the treatment study with one of the alpha blockers, tamsulosin. The histopathological classification criteria of rat model of CP/CPPS will serve for further research of the pathogenesis and treatment strategies of the disease.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Nonparametric Approach to Estimate Classification Accuracy and Consistency

ERIC Educational Resources Information Center

Lathrop, Quinn N.; Cheng, Ying

2014-01-01

When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…

Naïve Bayes classification in R.

PubMed

Zhang, Zhongheng

2016-06-01

Naïve Bayes classification is a kind of simple probabilistic classification methods based on Bayes' theorem with the assumption of independence between features. The model is trained on training dataset to make predictions by predict() function. This article introduces two functions naiveBayes() and train() for the performance of Naïve Bayes classification.

Classification of standard-like heterotic-string vacua

NASA Astrophysics Data System (ADS)

Faraggi, Alon E.; Rizos, John; Sonmez, Hasan

2018-02-01

We extend the free fermionic classification methodology to the class of standard-like heterotic-string vacua, in which the SO (10) GUT symmetry is broken at the string level to SU (3) × SU (2) × U(1) 2. The space of GGSO free phase configurations in this case is vastly enlarged compared to the corresponding SO (6) × SO (4) and SU (5) × U (1) vacua. Extracting substantial numbers of phenomenologically viable models therefore requires a modification of the classification methods. This is achieved by identifying conditions on the GGSO projection coefficients, which are satisfied at the SO (10) level by random phase configurations, and that lead to three generation models with the SO (10) symmetry broken to the SU (3) × SU (2) × U(1) 2 subgroup. Around each of these fertile SO (10) configurations, we perform a complete classification of standard-like models, by adding the SO (10) symmetry breaking basis vectors, and scanning all the associated GGSO phases. Following this methodology we are able to generate some 107 three generation Standard-like Models. We present the results of the classification and one exemplary model with distinct phenomenological properties, compared to previous SLM constructions.

Impact of Information based Classification on Network Epidemics

PubMed Central

Mishra, Bimal Kumar; Haldar, Kaushik; Sinha, Durgesh Nandini

2016-01-01

Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results. PMID:27329348

Identification of Putative Cardiovascular System Developmental Toxicants using a Classification Model based on Signaling Pathway-Adverse Outcome Pathways

EPA Science Inventory

An important challenge for an integrative approach to developmental systems toxicology is associating putative molecular initiating events (MIEs), cell signaling pathways, cell function and modeled fetal exposure kinetics. We have developed a chemical classification model based o...

Classifying GRB 170817A/GW170817 in a Fermi duration-hardness plane

NASA Astrophysics Data System (ADS)

Horváth, I.; Tóth, B. G.; Hakkila, J.; Tóth, L. V.; Balázs, L. G.; Rácz, I. I.; Pintér, S.; Bagoly, Z.

2018-03-01

GRB 170817A, associated with the LIGO-Virgo GW170817 neutron-star merger event, lacks the short duration and hard spectrum of a Short gamma-ray burst (GRB) expected from long-standing classification models. Correctly identifying the class to which this burst belongs requires comparison with other GRBs detected by the Fermi GBM. The aim of our analysis is to classify Fermi GRBs and to test whether or not GRB 170817A belongs—as suggested—to the Short GRB class. The Fermi GBM catalog provides a large database with many measured variables that can be used to explore gamma-ray burst classification. We use statistical techniques to look for clustering in a sample of 1298 gamma-ray bursts described by duration and spectral hardness. Classification of the detected bursts shows that GRB 170817A most likely belongs to the Intermediate, rather than the Short GRB class. We discuss this result in light of theoretical neutron-star merger models and existing GRB classification schemes. It appears that GRB classification schemes may not yet be linked to appropriate theoretical models, and that theoretical models may not yet adequately account for known GRB class properties. We conclude that GRB 170817A may not fit into a simple phenomenological classification scheme.

Protein classification using modified n-grams and skip-grams.

PubMed

Islam, S M Ashiqul; Heil, Benjamin J; Kearney, Christopher Michel; Baker, Erich J

2018-05-01

Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. erich_baker@baylor.edu. Supplementary data are available at Bioinformatics online.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

A Mixtures-of-Trees Framework for Multi-Label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods. PMID:25927011

Multiclass classification of microarray data samples with a reduced number of genes

PubMed Central

2011-01-01

Background Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. Results A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples. Conclusions A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples. PMID:21342522

Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding.

PubMed

Xu, Yun; Muhamadali, Howbeer; Sayqal, Ali; Dixon, Neil; Goodacre, Royston

2016-10-28

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a "pure" regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding.

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

NASA Astrophysics Data System (ADS)

Wutsqa, D. U.; Marwah, M.

2017-06-01

In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments

ERIC Educational Resources Information Center

Amershi, Saleema; Conati, Cristina

2009-01-01

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…

Logical-Rule Models of Classification Response Times: A Synthesis of Mental-Architecture, Random-Walk, and Decision-Bound Approaches

ERIC Educational Resources Information Center

Fific, Mario; Little, Daniel R.; Nosofsky, Robert M.

2010-01-01

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli…

Modeling ready biodegradability of fragrance materials.

PubMed

Ceriani, Lidia; Papa, Ester; Kovarich, Simona; Boethling, Robert; Gramatica, Paola

2015-06-01

In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal ≥80%; external ≥68%), specificity (internal ≥80%; external 73%), and overall accuracy (≥75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials. © 2015 SETAC.

Latent Partially Ordered Classification Models and Normal Mixtures

ERIC Educational Resources Information Center

Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith

2013-01-01

Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for…

An Illustration of Diagnostic Classification Modeling in Student Learning Outcomes Assessment

ERIC Educational Resources Information Center

Jurich, Daniel P.; Bradshaw, Laine P.

2014-01-01

The assessment of higher-education student learning outcomes is an important component in understanding the strengths and weaknesses of academic and general education programs. This study illustrates the application of diagnostic classification models, a burgeoning set of statistical models, in assessing student learning outcomes. To facilitate…

A Note on Comparing Examinee Classification Methods for Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Huebner, Alan; Wang, Chun

2011-01-01

Cognitive diagnosis models have received much attention in the recent psychometric literature because of their potential to provide examinees with information regarding multiple fine-grained discretely defined skills, or attributes. This article discusses the issue of methods of examinee classification for cognitive diagnosis models, which are…

Classification Based on Hierarchical Linear Models: The Need for Incorporation of Social Contexts in Classification Analysis

ERIC Educational Resources Information Center

Vaughn, Brandon K.; Wang, Qui

2009-01-01

Many areas in educational and psychological research involve the use of classification statistical analysis. For example, school districts might be interested in attaining variables that provide optimal prediction of school dropouts. In psychology, a researcher might be interested in the classification of a subject into a particular psychological…

An evaluation of the transferability of cross classification trip generation models.

DOT National Transportation Integrated Search

1978-01-01

This report describes the results of the application in Virginia of the trip generation procedures described in the Federal Highway Administration report entitled Trip Generation Analysis and published in 1975. Cross classification models, disaggrega...

Global Optimization Ensemble Model for Classification Methods

PubMed Central

Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

2014-01-01

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review.

PubMed

Pombo, Nuno; Garcia, Nuno; Bousson, Kouamana

2017-03-01

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Vulnerable land ecosystems classification using spatial context and spectral indices

NASA Astrophysics Data System (ADS)

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

2017-10-01

Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

Method of Grassland Information Extraction Based on Multi-Level Segmentation and Cart Model

NASA Astrophysics Data System (ADS)

Qiao, Y.; Chen, T.; He, J.; Wen, Q.; Liu, F.; Wang, Z.

2018-04-01

It is difficult to extract grassland accurately by traditional classification methods, such as supervised method based on pixels or objects. This paper proposed a new method combing the multi-level segmentation with CART (classification and regression tree) model. The multi-level segmentation which combined the multi-resolution segmentation and the spectral difference segmentation could avoid the over and insufficient segmentation seen in the single segmentation mode. The CART model was established based on the spectral characteristics and texture feature which were excavated from training sample data. Xilinhaote City in Inner Mongolia Autonomous Region was chosen as the typical study area and the proposed method was verified by using visual interpretation results as approximate truth value. Meanwhile, the comparison with the nearest neighbor supervised classification method was obtained. The experimental results showed that the total precision of classification and the Kappa coefficient of the proposed method was 95 % and 0.9, respectively. However, the total precision of classification and the Kappa coefficient of the nearest neighbor supervised classification method was 80 % and 0.56, respectively. The result suggested that the accuracy of classification proposed in this paper was higher than the nearest neighbor supervised classification method. The experiment certificated that the proposed method was an effective extraction method of grassland information, which could enhance the boundary of grassland classification and avoid the restriction of grassland distribution scale. This method was also applicable to the extraction of grassland information in other regions with complicated spatial features, which could avoid the interference of woodland, arable land and water body effectively.

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Classification framework for partially observed dynamical systems

NASA Astrophysics Data System (ADS)

Shen, Yuan; Tino, Peter; Tsaneva-Atanasova, Krasimira

2017-04-01

We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space. In contrast to the existing approaches using point estimates of model parameters to represent individual data items, we employ posterior distributions over model parameters, thus taking into account in a principled manner the uncertainty due to both the generative (observational and/or dynamic noise) and observation (sampling in time) processes. We evaluate the framework on two test beds: a biological pathway model and a stochastic double-well system. Crucially, we show that the classification performance is not impaired when the model structure used for inferring posterior distributions is much more simple than the observation-generating model structure, provided the reduced-complexity inferential model structure captures the essential characteristics needed for the given classification task.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

PubMed Central

2012-01-01

Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting. PMID:22417403

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

PubMed

Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

2012-03-14

Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

Multi-Agent Information Classification Using Dynamic Acquaintance Lists.

ERIC Educational Resources Information Center

Mukhopadhyay, Snehasis; Peng, Shengquan; Raje, Rajeev; Palakal, Mathew; Mostafa, Javed

2003-01-01

Discussion of automated information services focuses on information classification and collaborative agents, i.e. intelligent computer programs. Highlights include multi-agent systems; distributed artificial intelligence; thesauri; document representation and classification; agent modeling; acquaintances, or remote agents discovered through…

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

PubMed

de Moura, Karina de O A; Balbinot, Alexandre

2018-05-01

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

PubMed Central

Balbinot, Alexandre

2018-01-01

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior. PMID:29723994

A Pruning Neural Network Model in Credit Classification Analysis

PubMed Central

Tang, Yajiao; Ji, Junkai; Dai, Hongwei; Yu, Yang; Todo, Yuki

2018-01-01

Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN) and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs) to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency. PMID:29606961

The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges.

PubMed

Phillips, Charles D

2015-01-01

Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges.

Discriminative least squares regression for multiclass classification and feature selection.

PubMed

Xiang, Shiming; Nie, Feiping; Meng, Gaofeng; Pan, Chunhong; Zhang, Changshui

2012-11-01

This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.

The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges

PubMed Central

Phillips, Charles D.

2015-01-01

Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges. PMID:26740744

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

PubMed

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-03

Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

Classification of Birds and Bats Using Flight Tracks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cullinan, Valerie I.; Matzner, Shari; Duberstein, Corey A.

Classification of birds and bats that use areas targeted for offshore wind farm development and the inference of their behavior is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea involves transect surveys using trained individuals in boats or airplanes or using high-resolution imagery. These approaches are costly and have safety concerns. Based on a limited annotated library extracted from a single-camera thermal video, we provide a framework for building models that classify birds and bats and their associated behaviors. As an example, we developed a discriminant modelmore » for theoretical flight paths and applied it to data (N = 64 tracks) extracted from 5-min video clips. The agreement between model- and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and path types were combined. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of bats and terns (N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. Model classification of gulls and swallows (N ≥ 18) was on average 73% and 85% correct, respectively. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models would be greatly improved by including a measure of distance between the camera and the target.« less

Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy.

PubMed

da Silva, Neirivaldo C; Pimentel, Maria Fernanda; Honorato, Ricardo S; Talhavini, Marcio; Maldaner, Adriano O; Honorato, Fernanda A

2015-08-01

The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

PubMed Central

Meszlényi, Regina J.; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network. PMID:29089883

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

PubMed

Zhang, Jianhua; Li, Sunan; Wang, Rubin

2017-01-01

In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

PubMed

Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Huiying; Hou, Zhangshuan; Huang, Maoyi

The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrologicalmore » parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified accordingly to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using a Principal component analyses (PCA) and expectation-maximization (EM) –based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each S-class with similar parameter sensitivity characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. Inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.« less

Rotationally invariant clustering of diffusion MRI data using spherical harmonics

NASA Astrophysics Data System (ADS)

Liptrot, Matthew; Lauze, François

2016-03-01

We present a simple approach to the voxelwise classification of brain tissue acquired with diffusion weighted MRI (DWI). The approach leverages the power of spherical harmonics to summarise the diffusion information, sampled at many points over a sphere, using only a handful of coefficients. We use simple features that are invariant to the rotation of the highly orientational diffusion data. This provides a way to directly classify voxels whose diffusion characteristics are similar yet whose primary diffusion orientations differ. Subsequent application of machine-learning to the spherical harmonic coefficients therefore may permit classification of DWI voxels according to their inferred underlying fibre properties, whilst ignoring the specifics of orientation. After smoothing apparent diffusion coefficients volumes, we apply a spherical harmonic transform, which models the multi-directional diffusion data as a collection of spherical basis functions. We use the derived coefficients as voxelwise feature vectors for classification. Using a simple Gaussian mixture model, we examined the classification performance for a range of sub-classes (3-20). The results were compared against existing alternatives for tissue classification e.g. fractional anisotropy (FA) or the standard model used by Camino.1 The approach was implemented on both two publicly-available datasets: an ex-vivo pig brain and in-vivo human brain from the Human Connectome Project (HCP). We have demonstrated how a robust classification of DWI data can be performed without the need for a model reconstruction step. This avoids the potential confounds and uncertainty that such models may impose, and has the benefit of being computable directly from the DWI volumes. As such, the method could prove useful in subsequent pre-processing stages, such as model fitting, where it could inform about individual voxel complexities and improve model parameter choice.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images

PubMed Central

Miri, Maliheh; Amini, Zahra; Rabbani, Hossein; Kafieh, Raheleh

2017-01-01

Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar–venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches. PMID:28553578

Typecasting catchments: Classification, directionality, and the pursuit of universality

NASA Astrophysics Data System (ADS)

Smith, Tyler; Marshall, Lucy; McGlynn, Brian

2018-02-01

Catchment classification poses a significant challenge to hydrology and hydrologic modeling, restricting widespread transfer of knowledge from well-studied sites. The identification of important physical, climatological, or hydrologic attributes (to varying degrees depending on application/data availability) has traditionally been the focus for catchment classification. Classification approaches are regularly assessed with regard to their ability to provide suitable hydrologic predictions - commonly by transferring fitted hydrologic parameters at a data-rich catchment to a data-poor catchment deemed similar by the classification. While such approaches to hydrology's grand challenges are intuitive, they often ignore the most uncertain aspect of the process - the model itself. We explore catchment classification and parameter transferability and the concept of universal donor/acceptor catchments. We identify the implications of the assumption that the transfer of parameters between "similar" catchments is reciprocal (i.e., non-directional). These concepts are considered through three case studies situated across multiple gradients that include model complexity, process description, and site characteristics. Case study results highlight that some catchments are more successfully used as donor catchments and others are better suited as acceptor catchments. These results were observed for both black-box and process consistent hydrologic models, as well as for differing levels of catchment similarity. Therefore, we suggest that similarity does not adequately satisfy the underlying assumptions being made in parameter regionalization approaches regardless of model appropriateness. Furthermore, we suggest that the directionality of parameter transfer is an important factor in determining the success of parameter regionalization approaches.

MODELING DYNAMIC VEGETATION RESPONSE TO RAPID CLIMATE CHANGE USING BIOCLIMATIC CLASSIFICATION

EPA Science Inventory

Modeling potential global redistribution of terrestrial vegetation frequently is based on bioclimatic classifications which relate static regional vegetation zones (biomes) to a set of static climate parameters. The equilibrium character of the relationships limits our confidence...

Physical Human Activity Recognition Using Wearable Sensors.

PubMed

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-12-11

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Physical Human Activity Recognition Using Wearable Sensors

PubMed Central

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-01-01

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject. PMID:26690450

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

Applicability of the Proposed Japanese Model for the Classification of Gastric Cancer Location: The "PROTRADIST" Retrospective Study.

PubMed

Marano, Luigi; Petrillo, Marianna; Pezzella, Modestino; Patriti, Alberto; Braccio, Bartolomeo; Esposito, Giuseppe; Grassia, Michele; Romano, Angela; Torelli, Francesco; De Luca, Raffaele; Fabozzi, Alessio; Falco, Giuseppe; Di Martino, Natale

2017-06-01

The extension of lymphadenectomy for surgical treatment of gastric cancer remains discordant among European and Japanese surgeons. Kinami et al. (Kinami S, Fujimura T, Ojima E, et al. PTD classification: proposal for a new classification of gastric cancer location based on physiological lymphatic flow. Int. J. Clin. Oncol. 2008;13:320-329) proposed a new experimental classification, the "Proximal zone, Transitional zone, Distal zone" (PTD) classification, based on the physiological lymphatic flow of gastric cancer site. The aim of the present retrospective study is to assess the applicability of PTD Japanese model in gastric cancer patients of our Western surgical department. Two groups of patients with histologically documented adenocarcinoma of the stomach were retrospectively obtained: In the first group were categorized 89 patients with T1a-T1b tumor invasion; and in the second group were 157 patients with T2-T3 category. The data collected were then categorized according to the PTD classification. In the T1a-T1b group there were no lymph node metastases within the r-GA or r-GEA compartments for tumors located in the P portion, and similarly there were no lymphatic metastases within the l-GEA or p-GA compartments for tumors located in the D portion. On the contrary, in the T2-T3 group the lymph node metastases presented a diffused spreading with no statistical significance between the two classification models. Our results show that the PTD classification based on physiological lymphatic flow of the gastric cancer site is a more physiological and clinical version than the Upper, Medium And Lower classification. It represents a valuable and applicable model of cancer location that could be a guide to a tailored surgical approach in Italian patients with neoplasm confined to submucosa. Nevertheless, in order to confirm our findings, larger and prospective studies are needed.

Topic Detection in Online Chat

DTIC Science & Technology

2009-09-01

CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18 . SECURITY CLASSIFICATION OF THIS PAGE Unclassified 19. SECURITY CLASSIFICATION...Documents and Author-Author Documents—Radial Kernel. .............. 66 Figure 18 . Classifiers Results: LDA Models Created by Textbook-Author...Trained on Two Classes............................................................................................... 72 Table 18 . Maximum

Effects of sample survey design on the accuracy of classification tree models in species distribution models

Treesearch

Thomas C. Edwards; D. Richard Cutler; Niklaus E. Zimmermann; Linda Geiser; Gretchen G. Moisen

2006-01-01

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by...

Using New Models to Analyze Complex Regularities of the World: Commentary on Musso et al. (2013)

ERIC Educational Resources Information Center

Nokelainen, Petri; Silander, Tomi

2014-01-01

This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is…

A Classification Model and an Open E-Learning System Based on Intuitionistic Fuzzy Sets for Instructional Design Concepts

ERIC Educational Resources Information Center

Güyer, Tolga; Aydogdu, Seyhmus

2016-01-01

This study suggests a classification model and an e-learning system based on this model for all instructional theories, approaches, models, strategies, methods, and technics being used in the process of instructional design that constitutes a direct or indirect resource for educational technology based on the theory of intuitionistic fuzzy sets…

Use of collateral information to improve LANDSAT classification accuracies

NASA Technical Reports Server (NTRS)

Strahler, A. H. (Principal Investigator)

1981-01-01

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Drug related webpages classification using images and text information based on multi-kernel learning

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Xiao, Liping; Zheng, Wenjuan

2015-12-01

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Online clustering algorithms for radar emitter classification.

PubMed

Liu, Jun; Lee, Jim P Y; Senior; Li, Lingjie; Luo, Zhi-Quan; Wong, K Max

2005-08-01

Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: One is model-based using the Minimum Description Length (MDL) criterion and the other is based on competitive learning. Computational complexity is analyzed for each algorithm and then compared. Simulation results show the superior performance of the model-based algorithm over competitive learning in terms of better classification accuracy, flexibility, and stability.

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

[Classification in medicine. An introductory reflection on its aim and object].

PubMed

Giere, W

2007-07-01

Human beings are born with the ability to recognize Gestalt and to classify. However, all classifications depend on their circumstances and intentions. There is no ultimate classification, and there is no one correct classification in medicine either. Examples for classifications of diagnoses, symptoms and procedures are discussed. The path to gaining knowledge and the basic difference between collecting data (patient file) and sorting data (register) will be illustrated using the BAIK information model. Additionally the model shows how the doctor can profit from the active electronic patient file which automatically offers him other relevant information for his current decision and saves time. "Without classification no new knowledge, no new knowledge through classification". This paradox will be solved eventually: a change of paradigms requires the overcoming of the currently valid classification system in medicine as well. Finally more precise recommendations will be given on how doctors can be freed from the burden of the need to classify and how the whole health system can gain much more valid data without limiting the doctors' freedom and creativity through co-ordinated use of IT, all while saving money at the same time.

A hybrid architecture for the implementation of the Athena neural net model

NASA Technical Reports Server (NTRS)

Koutsougeras, C.; Papachristou, C.

1989-01-01

The implementation of an earlier introduced neural net model for pattern classification is considered. Data flow principles are employed in the development of a machine that efficiently implements the model and can be useful for real time classification tasks. Further enhancement with optical computing structures is also considered.

Diagnostic Classification Models: Are They Necessary? Commentary on Rupp and Templin (2008)

ERIC Educational Resources Information Center

Gorin, Joanna S.

2009-01-01

In their paper "Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art," Andre Rupp and Jonathan Templin (2008) provide a comparative analysis of selected psychometric models useful for the analysis of multidimensional data for purposes of diagnostic score reporting. Recent assessment…

Refined docking as a valuable tool for lead optimization: application to histamine H3 receptor antagonists.

PubMed

Levoin, Nicolas; Calmels, Thierry; Poupardin-Olivier, Olivia; Labeeuw, Olivier; Danvy, Denis; Robert, Philippe; Berrebi-Bertrand, Isabelle; Ganellin, C Robin; Schunack, Walter; Stark, Holger; Capet, Marc

2008-10-01

Drug-discovery projects frequently employ structure-based information through protein modeling and ligand docking, and there is a plethora of reports relating successful use of them in virtual screening. Hit/lead optimization, which represents the next step and the longest for the medicinal chemist, is very rarely considered. This is not surprising because lead optimization is a much more complex task. Here, a homology model of the histamine H(3) receptor was built and tested for its ability to discriminate ligands above a defined threshold of affinity. In addition, drug safety is also evaluated during lead optimization, and "antitargets" are studied. So, we have used the same benchmarking procedure with the HERG channel and CYP2D6 enzyme, for which a minimal affinity is strongly desired. For targets and antitargets, we report here an accuracy as high as at least 70%, for ligands being classified above or below the chosen threshold. Such a good result is beyond what could have been predicted, especially, since our test conditions were particularly stringent. First, we measured the accuracy by means of AUC of ROC plots, i. e. considering both false positive and false negatives. Second, we used as datasets extensive chemical libraries (nearly a thousand ligands for H(3)). All molecules considered were true H(3) receptor ligands with moderate to high affinity (from microM to nM range). Third, the database is issued from concrete SAR (Bioprojet H(3) BF2.649 library) and is not simply constituted by few active ligands buried in a chemical catalogue.

Model Validation and Site Characterization for Early Deployment MHK Sites and Establishment of Wave Classification Scheme

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kilcher, Levi F

Model Validation and Site Characterization for Early Deployment Marine and Hydrokinetic Energy Sites and Establishment of Wave Classification Scheme presentation from from Water Power Technologies Office Peer Review, FY14-FY16.

Classification

NASA Astrophysics Data System (ADS)

Oza, Nikunj

2012-03-01

A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example would represent one sunspot’s classification (y_i) and the corresponding set of measurements (x_i). The output of a supervised learning algorithm is a model h that approximates the unknown mapping from the inputs to the outputs. In our example, h would map from the sunspot measurements to the type of sunspot. We may have a test set S—a set of examples not used in training that we use to test how well the model h predicts the outputs on new examples. Just as with the examples in T, the examples in S are assumed to be independent and identically distributed (i.i.d.) draws from the distribution D. We measure the error of h on the test set as the proportion of test cases that h misclassifies: 1/|S| Sigma(x,y union S)[I(h(x)!= y)] where I(v) is the indicator function—it returns 1 if v is true and 0 otherwise. In our sunspot classification example, we would identify additional examples of sunspots that were not used in generating the model, and use these to determine how accurate the model is—the fraction of the test samples that the model classifies correctly. An example of a classification model is the decision tree shown in Figure 23.1. We will discuss the decision tree learning algorithm in more detail later—for now, we assume that, given a training set with examples of sunspots, this decision tree is derived. This can be used to classify previously unseen examples of sunpots. For example, if a new sunspot’s inputs indicate that its "Group Length" is in the range 10-15, then the decision tree would classify the sunspot as being of type “E,” whereas if the "Group Length" is "NULL," the "Magnetic Type" is "bipolar," and the "Penumbra" is "rudimentary," then it would be classified as type "C." In this chapter, we will add to the above description of classification problems. We will discuss decision trees and several other classification models. In particular, we will discuss the learning algorithms that generate these classification models, how to use them to classify new examples, and the strengths and weaknesses of these models. We will end with pointers to further reading on classification methods applied to astronomy data.

A Review of Equation of State Models, Chemical Equilibrium Calculations and CERV Code Requirements for SHS Detonation Modelling

DTIC Science & Technology

2009-10-01

parameters for a large number of species. These authors provide many sample calculations with the JCZS database incorporated in CHEETAH 2.0, including...FORM (highest classification of Title, Abstract, Keywords) DOCUMENT CONTROL DATA (Security classification of title, body of abstract and...CLASSIFICATION OF FORM 13. ABSTRACT (a brief and factual summary of the document. It may also appear elsewhere in the body of the document itself

Formalization of the classification pattern: survey of classification modeling in information systems engineering.

PubMed

Partridge, Chris; de Cesare, Sergio; Mitchell, Andrew; Odell, James

2018-01-01

Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the "one and the many." Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor's work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus to the ISE literature. The literature survey follows the evolution of ISE's understanding of how to formalize the classification pattern. The various proposals are assessed using the classical example of classification; the Linnaean taxonomy formalized using powersets as a benchmark for formal expressiveness. The broad conclusion of the survey is that (1) the ISE community is currently in the early stages of the process of understanding how to formalize the classification pattern, particularly in the requirements for expressiveness exemplified by powersets, and (2) that there is an opportunity to intervene and speed up the process of adoption by clarifying this expressiveness. Given the central place that the classification pattern has in domain modeling, this intervention has the potential to lead to significant improvements.

Use of hydrologic landscape classification to diagnose streamflow predictability in Oregon

EPA Science Inventory

We implement a spatially lumped rainfall-runoff model to predict daily streamflow at 88 catchments within Oregon, USA and analyze its performance within the context of Oregon Hydrologic Landscapes (OHL) classification. OHL classification is used to characterize the physio-climat...

A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI)

PubMed Central

Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

2017-01-01

Introduction: Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. Aim: The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. Methods: first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. Results: There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. Conclusion: The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research. PMID:28883671

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Cell-based medicinal chemistry optimization of high-throughput screening (HTS) hits for orally active antimalarials. Part 1: challenges in potency and absorption, distribution, metabolism, excretion/pharmacokinetics (ADME/PK).

PubMed

Chatterjee, Arnab K

2013-10-24

Malaria represents a significant health issue, and novel and effective drugs are needed to address parasite resistance that has emerged to the current drug arsenal. Antimalarial drug discovery has historically benefited from a whole-cell (phenotypic) screening approach to identify lead molecules. This approach has been utilized by several groups to optimize weakly active antimalarial pharmacophores, such as the quinolone scaffold, to yield potent and highly efficacious compounds that are now poised to enter clinical trials. More recently, GNF/Novartis, GSK, and others have employed the same approach in high-throughput screening (HTS) of large compound libraries to find novel scaffolds that have also been optimized to clinical candidates by GNF/Novartis. This perspective outlines some of the inherent challenges in cell-based medicinal chemistry optimization, including optimization of oral exposure and hERG activity.

High throughput ion-channel pharmacology: planar-array-based voltage clamp.

PubMed

Kiss, Laszlo; Bennett, Paul B; Uebele, Victor N; Koblan, Kenneth S; Kane, Stefanie A; Neagle, Brad; Schroeder, Kirk

2003-02-01

Technological advances often drive major breakthroughs in biology. Examples include PCR, automated DNA sequencing, confocal/single photon microscopy, AFM, and voltage/patch-clamp methods. The patch-clamp method, first described nearly 30 years ago, was a major technical achievement that permitted voltage-clamp analysis (membrane potential control) of ion channels in most cells and revealed a role for channels in unimagined areas. Because of the high information content, voltage clamp is the best way to study ion-channel function; however, throughput is too low for drug screening. Here we describe a novel breakthrough planar-array-based HT patch-clamp technology developed by Essen Instruments capable of voltage-clamping thousands of cells per day. This technology provides greater than two orders of magnitude increase in throughput compared with the traditional voltage-clamp techniques. We have applied this method to study the hERG K(+) channel and to determine the pharmacological profile of QT prolonging drugs.

Fluorine and Fluorinated Motifs in the Design and Application of Bioisosteres for Drug Design.

PubMed

Meanwell, Nicholas A

2018-02-05

The electronic properties and relatively small size of fluorine endow it with considerable versatility as a bioisostere and it has found application as a substitute for lone pairs of electrons, the hydrogen atom, and the methyl group while also acting as a functional mimetic of the carbonyl, carbinol, and nitrile moieties. In this context, fluorine substitution can influence the potency, conformation, metabolism, membrane permeability, and P-gp recognition of a molecule and temper inhibition of the hERG channel by basic amines. However, as a consequence of the unique properties of fluorine, it features prominently in the design of higher order structural metaphors that are more esoteric in their conception and which reflect a more sophisticated molecular construction that broadens biological mimesis. In this Perspective, applications of fluorine in the construction of bioisosteric elements designed to enhance the in vitro and in vivo properties of a molecule are summarized.

Autonomous beating rate adaptation in human stem cell-derived cardiomyocytes

PubMed Central

Eng, George; Lee, Benjamin W.; Protas, Lev; Gagliardi, Mark; Brown, Kristy; Kass, Robert S.; Keller, Gordon; Robinson, Richard B.; Vunjak-Novakovic, Gordana

2016-01-01

The therapeutic success of human stem cell-derived cardiomyocytes critically depends on their ability to respond to and integrate with the surrounding electromechanical environment. Currently, the immaturity of human cardiomyocytes derived from stem cells limits their utility for regenerative medicine and biological research. We hypothesize that biomimetic electrical signals regulate the intrinsic beating properties of cardiomyocytes. Here we show that electrical conditioning of human stem cell-derived cardiomyocytes in three-dimensional culture promotes cardiomyocyte maturation, alters their automaticity and enhances connexin expression. Cardiomyocytes adapt their autonomous beating rate to the frequency at which they were stimulated, an effect mediated by the emergence of a rapidly depolarizing cell population, and the expression of hERG. This rate-adaptive behaviour is long lasting and transferable to the surrounding cardiomyocytes. Thus, electrical conditioning may be used to promote cardiomyocyte maturation and establish their automaticity, with implications for cell-based reduction of arrhythmia during heart regeneration. PMID:26785135

[Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy].

PubMed

Ma, Xiao; Yuan, Hong-fu; Song, Chun-feng; Hu, Ai-qin; Li, Xiao-yu; Zhao, Zhong; Li, Xiu-qin; Guo Zhen; Zhu, Zhi-qiang

2015-07-01

A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model, PLS-DA model, PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.

Universal etiology, multifactorial diseases and the constitutive model of disease classification.

PubMed

Fuller, Jonathan

2018-02-01

Infectious diseases are often said to have a universal etiology, while chronic and noncommunicable diseases are said to be multifactorial in their etiology. It has been argued that the universal etiology of an infectious disease results from its classification using a monocausal disease model. In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the Nineteenth Century germ theorists. Copyright © 2017 Elsevier Ltd. All rights reserved.

Statistical methods and neural network approaches for classification of data from multiple sources

NASA Technical Reports Server (NTRS)

Benediktsson, Jon Atli; Swain, Philip H.

1990-01-01

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Deconvolution single shot multibox detector for supermarket commodity detection and classification

NASA Astrophysics Data System (ADS)

Li, Dejian; Li, Jian; Nie, Binling; Sun, Shouqian

2017-07-01

This paper proposes an image detection model to detect and classify supermarkets shelves' commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PubMed

Jiao, Y; Chen, R; Ke, X; Cheng, L; Chu, K; Lu, Z; Herskovits, E H

2011-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

PubMed

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

A New Value Classification and Values to Be Acquired by Students Related to This Classification

ERIC Educational Resources Information Center

Acat, M. Bahaddin; Aslan, Mecit

2012-01-01

The aim of this study is to access a new value classification and analyse the views of teacher and parents related to this classification. The general survey model was employed in this study. The population of this study is composed of school teachers working in primary schools and parents of their students in Eskisehir. The present study adopted…

Mode of Action (MOA) Assignment Classifications for Ecotoxicology: An Evaluation of approaches

EPA Science Inventory

The mode of toxic action (MOA) is recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, MOA classification has never been standardized in ecotoxicology, and a comprehensive comparison of classific...

Land Covers Classification Based on Random Forest Method Using Features from Full-Waveform LIDAR Data

NASA Astrophysics Data System (ADS)

Ma, L.; Zhou, M.; Li, C.

2017-09-01

In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

USGS Publications Warehouse

Wright, C.; Gallant, Alisa L.

2007-01-01

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

PubMed

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Automated detection of radioisotopes from an aircraft platform by pattern recognition analysis of gamma-ray spectra.

PubMed

Dess, Brian W; Cardarelli, John; Thomas, Mark J; Stapleton, Jeff; Kroutil, Robert T; Miller, David; Curry, Timothy; Small, Gary W

2018-03-08

A generalized methodology was developed for automating the detection of radioisotopes from gamma-ray spectra collected from an aircraft platform using sodium-iodide detectors. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, multivariate classification models based on nonparametric linear discriminant analysis were developed for application to spectra that were preprocessed through a combination of altitude-based scaling and digital filtering. Training sets of spectra for use in building classification models were assembled from a combination of background spectra collected in the field and synthesized spectra obtained by superimposing laboratory-collected spectra of target radioisotopes onto field backgrounds. This approach eliminated the need for field experimentation with radioactive sources for use in building classification models. Through a bi-Gaussian modeling procedure, the discriminant scores that served as the outputs from the classification models were related to associated confidence levels. This provided an easily interpreted result regarding the presence or absence of the signature of a specific radioisotope in each collected spectrum. Through the use of this approach, classifiers were built for cesium-137 ( 137 Cs) and cobalt-60 ( 60 Co), two radioisotopes that are of interest in airborne radiological monitoring applications. The optimized classifiers were tested with field data collected from a set of six geographically diverse sites, three of which contained either 137 Cs, 60 Co, or both. When the optimized classification models were applied, the overall percentages of correct classifications for spectra collected at these sites were 99.9 and 97.9% for the 60 Co and 137 Cs classifiers, respectively. Copyright © 2018 Elsevier Ltd. All rights reserved.

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region

NASA Astrophysics Data System (ADS)

Pastor, M. A.; Casado, M. J.

2012-10-01

This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes.

Distance Metric between 3D Models and 2D Images for Recognition and Classification

DTIC Science & Technology

1992-07-01

and 2D Image__ for Recognition and Classification D TIC Ronen Basri and Daphna Weinshall ELECTE JAN2 91993’ Abstract C Similarity measurements...Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-19-J-4038. Ronen Basri is supported by the...Distance Metric Between 3D Models and 2D Images for N00014-85-K-0124 Recognition and Classification N00014-91-J-4038 6. AUTHOR(S) Ronen Basri and

A Robust Geometric Model for Argument Classification

NASA Astrophysics Data System (ADS)

Giannone, Cristina; Croce, Danilo; Basili, Roberto; de Cao, Diego

Argument classification is the task of assigning semantic roles to syntactic structures in natural language sentences. Supervised learning techniques for frame semantics have been recently shown to benefit from rich sets of syntactic features. However argument classification is also highly dependent on the semantics of the involved lexicals. Empirical studies have shown that domain dependence of lexical information causes large performance drops in outside domain tests. In this paper a distributional approach is proposed to improve the robustness of the learning model against out-of-domain lexical phenomena.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models tomore » curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.« less

Patterns of Use of an Agent-Based Model and a System Dynamics Model: The Application of Patterns of Use and the Impacts on Learning Outcomes

ERIC Educational Resources Information Center

Thompson, Kate; Reimann, Peter

2010-01-01

A classification system that was developed for the use of agent-based models was applied to strategies used by school-aged students to interrogate an agent-based model and a system dynamics model. These were compared, and relationships between learning outcomes and the strategies used were also analysed. It was found that the classification system…

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

PubMed

Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

2016-01-01

Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.

The classification of body dysmorphic disorder symptoms in male and female adolescents.

PubMed

Schneider, Sophie C; Baillie, Andrew J; Mond, Jonathan; Turner, Cynthia M; Hudson, Jennifer L

2018-01-01

Body dysmorphic disorder (BDD) was categorised in DSM-5 within the newly created 'obsessive-compulsive and related disorders' chapter, however this classification remains subject to debate. Confirmatory factor analysis was used to test competing models of the co-occurrence of symptoms of BDD, obsessive-compulsive disorder, unipolar depression, anxiety, and eating disorders in a community sample of adolescents, and to explore potential sex differences in these models. Self-report questionnaires assessing disorder symptoms were completed by 3149 Australian adolescents. The fit of correlated factor models was calculated separately in males and females, and measurement invariance testing compared parameters of the best-fitting model between males and females. All theoretical models of the classification of BDD had poor fit to the data. Good fit was found for a novel model where BDD symptoms formed a distinct latent factor, correlated with affective disorder and eating disorder latent factors. Metric non-invariance was found between males and females, and the majority of factor loadings differed between males and females. Correlations between some latent factors also differed by sex. Only cross-sectional data were collected, and the study did not assess a broad range of DSM-5 defined eating disorder symptoms or other disorders in the DSM-5 obsessive-compulsive and related disorders chapter. This study is the first to statistically evaluate competing models of BDD classification. The findings highlight the unique features of BDD and its associations with affective and eating disorders. Future studies examining the classification of BDD should consider developmental and sex differences in their models. Copyright © 2017. Published by Elsevier B.V.

TEMPORAL CORRELATION OF CLASSIFICATIONS IN REMOTE SENSING

EPA Science Inventory

A bivariate binary model is developed for estimating the change in land cover from satellite images obtained at two different times. The binary classifications of a pixel at the two times are modeled as potentially correlated random variables, conditional on the true states of th...

Various forms of indexing HDMR for modelling multivariate classification problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

Age group classification and gender detection based on forced expiratory spirometry.

PubMed

Cosgun, Sema; Ozbek, I Yucel

2015-08-01

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

USGS Publications Warehouse

Howard, Daniel M.; Wylie, Bruce K.; Tieszen, Larry L.

2012-01-01

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R 2 = 0.90).

Risk assessments using the Strain Index and the TLV for HAL, Part I: Task and multi-task job exposure classifications.

PubMed

Kapellusch, Jay M; Bao, Stephen S; Silverstein, Barbara A; Merryweather, Andrew S; Thiese, Mathew S; Hegmann, Kurt T; Garg, Arun

2017-12-01

The Strain Index (SI) and the American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Value for Hand Activity Level (TLV for HAL) use different constituent variables to quantify task physical exposures. Similarly, time-weighted-average (TWA), Peak, and Typical exposure techniques to quantify physical exposure from multi-task jobs make different assumptions about each task's contribution to the whole job exposure. Thus, task and job physical exposure classifications differ depending upon which model and technique are used for quantification. This study examines exposure classification agreement, disagreement, correlation, and magnitude of classification differences between these models and techniques. Data from 710 multi-task job workers performing 3,647 tasks were analyzed using the SI and TLV for HAL models, as well as with the TWA, Typical and Peak job exposure techniques. Physical exposures were classified as low, medium, and high using each model's recommended, or a priori limits. Exposure classification agreement and disagreement between models (SI, TLV for HAL) and between job exposure techniques (TWA, Typical, Peak) were described and analyzed. Regardless of technique, the SI classified more tasks as high exposure than the TLV for HAL, and the TLV for HAL classified more tasks as low exposure. The models agreed on 48.5% of task classifications (kappa = 0.28) with 15.5% of disagreement between low and high exposure categories. Between-technique (i.e., TWA, Typical, Peak) agreement ranged from 61-93% (kappa: 0.16-0.92) depending on whether the SI or TLV for HAL was used. There was disagreement between the SI and TLV for HAL and between the TWA, Typical and Peak techniques. Disagreement creates uncertainty for job design, job analysis, risk assessments, and developing interventions. Task exposure classifications from the SI and TLV for HAL might complement each other. However, TWA, Typical, and Peak job exposure techniques all have limitations. Part II of this article examines whether the observed differences between these models and techniques produce different exposure-response relationships for predicting prevalence of carpal tunnel syndrome.

Automatic classification of animal vocalizations

NASA Astrophysics Data System (ADS)

Clemins, Patrick J.

2005-11-01

Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysis techniques in recent years. Some common tasks in bioacoustics are repertoire determination, call detection, individual identification, stress detection, and behavior correlation. Each research study, however, uses a wide variety of different measured variables, called features, and classification systems to accomplish these tasks. The well-established field of human speech processing has developed a number of different techniques to perform many of the aforementioned bioacoustics tasks. Melfrequency cepstral coefficients (MFCCs) and perceptual linear prediction (PLP) coefficients are two popular feature sets. The hidden Markov model (HMM), a statistical model similar to a finite autonoma machine, is the most commonly used supervised classification model and is capable of modeling both temporal and spectral variations. This research designs a framework that applies models from human speech processing for bioacoustic analysis tasks. The development of the generalized perceptual linear prediction (gPLP) feature extraction model is one of the more important novel contributions of the framework. Perceptual information from the species under study can be incorporated into the gPLP feature extraction model to represent the vocalizations as the animals might perceive them. By including this perceptual information and modifying parameters of the HMM classification system, this framework can be applied to a wide range of species. The effectiveness of the framework is shown by analyzing African elephant and beluga whale vocalizations. The features extracted from the African elephant data are used as input to a supervised classification system and compared to results from traditional statistical tests. The gPLP features extracted from the beluga whale data are used in an unsupervised classification system and the results are compared to labels assigned by experts. The development of a framework from which to build animal vocalization classifiers will provide bioacoustics researchers with a consistent platform to analyze and classify vocalizations. A common framework will also allow studies to compare results across species and institutions. In addition, the use of automated classification techniques can speed analysis and uncover behavioral correlations not readily apparent using traditional techniques.

Research on potential user identification model for electric energy substitution

NASA Astrophysics Data System (ADS)

Xia, Huaijian; Chen, Meiling; Lin, Haiying; Yang, Shuo; Miao, Bo; Zhu, Xinzhi

2018-01-01

The implementation of energy substitution plays an important role in promoting the development of energy conservation and emission reduction in china. Energy service management platform of alternative energy users based on the data in the enterprise production value, product output, coal and other energy consumption as a potential evaluation index, using principal component analysis model to simplify the formation of characteristic index, comprehensive index contains the original variables, and using fuzzy clustering model for the same industry user’s flexible classification. The comprehensive index number and user clustering classification based on constructed particle optimization neural network classification model based on the user, user can replace electric potential prediction. The results of an example show that the model can effectively predict the potential of users’ energy potential.

The development of a classification system for maternity models of care.

PubMed

Donnolley, Natasha; Butler-Henderson, Kerryn; Chapman, Michael; Sullivan, Elizabeth

2016-08-01

A lack of standard terminology or means to identify and define models of maternity care in Australia has prevented accurate evaluations of outcomes for mothers and babies in different models of maternity care. As part of the Commonwealth-funded National Maternity Data Development Project, a classification system was developed utilising a data set specification that defines characteristics of models of maternity care. The Maternity Care Classification System or MaCCS was developed using a participatory action research design that built upon the published and grey literature. The study identified the characteristics that differentiate models of care and classifies models into eleven different Major Model Categories. The MaCCS will enable individual health services, local health districts (networks), jurisdictional and national health authorities to make better informed decisions for planning, policy development and delivery of maternity services in Australia. © The Author(s) 2016.

Deep learning for tumor classification in imaging mass spectrometry.

PubMed

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data: An Approach for Building Effective Clinical Decision-Making System.

PubMed

Jane, Nancy Yesudhas; Nehemiah, Khanna Harichandran; Arputharaj, Kannan

2016-01-01

Clinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging. This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification. In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier. For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset. The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

A subject-independent pattern-based Brain-Computer Interface

PubMed Central

Ray, Andreas M.; Sitaram, Ranganatha; Rana, Mohit; Pasqualotto, Emanuele; Buyukturkoglu, Korhan; Guan, Cuntai; Ang, Kai-Keng; Tejos, Cristián; Zamorano, Francisco; Aboitiz, Francisco; Birbaumer, Niels; Ruiz, Sergio

2015-01-01

While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to “match” their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders. PMID:26539089

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty.

PubMed

Mihaljević, Bojan; Bielza, Concha; Benavides-Piccione, Ruth; DeFelipe, Javier; Larrañaga, Pedro

2014-01-01

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Analysis of swallowing sounds using hidden Markov models.

PubMed

Aboofazeli, Mohammad; Moussavi, Zahra

2008-04-01

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

EPA Science Inventory

In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER bindi...

Application of Classification Models to Pharyngeal High-Resolution Manometry

ERIC Educational Resources Information Center

Mielens, Jason D.; Hoffman, Matthew R.; Ciucci, Michelle R.; McCulloch, Timothy M.; Jiang, Jack J.

2012-01-01

Purpose: The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method: Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were…

Rough set classification based on quantum logic

NASA Astrophysics Data System (ADS)

Hassan, Yasser F.

2017-11-01

By combining the advantages of quantum computing and soft computing, the paper shows that rough sets can be used with quantum logic for classification and recognition systems. We suggest the new definition of rough set theory as quantum logic theory. Rough approximations are essential elements in rough set theory, the quantum rough set model for set-valued data directly construct set approximation based on a kind of quantum similarity relation which is presented here. Theoretical analyses demonstrate that the new model for quantum rough sets has new type of decision rule with less redundancy which can be used to give accurate classification using principles of quantum superposition and non-linear quantum relations. To our knowledge, this is the first attempt aiming to define rough sets in representation of a quantum rather than logic or sets. The experiments on data-sets have demonstrated that the proposed model is more accuracy than the traditional rough sets in terms of finding optimal classifications.

Accelerometry-based classification of human activities using Markov modeling.

PubMed

Mannini, Andrea; Sabatini, Angelo Maria

2011-01-01

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.