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Learning classifiers with imbalanced data can be strongly biased toward the majority class. To address this issue, several methods have been proposed using generative adversarial networks (GANs). Existing GAN-based methods, however, do not effectively utilize the relationship between a classifier and a generator. This article proposes a novel three-player structure consisting of a discriminator, a generator, and a classifier, along with decision boundary regularization. Our method is distinctive in which the generator is trained in cooperation with the classifier to provide minority samples that gradually expand the minority decision region, improving performance for imbalanced data classification. The proposed method outperforms the existing methods on real data sets as well as synthetic imbalanced data sets.

Due to the increased morbidity and mortality by fungal infections and the emergence of severe antifungal resistance, there is an urgent need for new antifungal agents. Here, we screened for antifungal activity in our in-house library through the minimum inhibitory concentration test and derived two hit compounds with moderate antifungal activities. The hit compounds' antifungal activities and drug-like properties were optimized by substituting various aryl ring, alkyl chain, and methyl groups. Among the optimized compounds, 22h was the most promising candidate with good drug-like properties and exhibited potent fast-acting fungicidal antifungal effects against various fungal pathogens and synergistic antifungal activities with some known antifungal drugs. Additionally, 22h was further confirmed to disturb fungal cell wall integrity by activating multiple cell wall integrity pathways. Furthermore, 22h exerted significant antifungal efficacy in both the subcutaneous infection mouse model and ex vivo human nail infection model.

Monoamine oxidase-B (MAO-B) is a well-established therapeutic target for Parkinson’s disease (PD); however, previous clinical studies on currently available irreversible MAO-B inhibitors have yielded disappointing neuroprotective effects. Here, we tested the therapeutic potential of KDS2010, a recently synthesized potent, selective, and reversible MAO-B inhibitor in multiple animal models of PD. We designed and synthesized a series of α-aminoamide derivatives and found that derivative KDS2010 exhibited the highest potency, specificity, reversibility, and bioavailability (> 100%). In addition, KDS2010 demonstrated significant neuroprotective and anti-neuroinflammatory efficacy against nigrostriatal pathway destruction in the mouse MPTP model of parkinsonism. Treatment with KDS2010 also alleviated parkinsonian motor dysfunction in 6-hydroxydopamine-induced and A53T mutant α-synuclein overexpression rat models of PD. Moreover, KDS2010 showed virtually no toxicity or side effects in non-human primates. KDS2010 could be a next-generation therapeutic candidate for PD. Supplementary Information The online version contains supplementary material available at 10.1007/s13311-021-01097-4.

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role., Comment: Published in IEEE Access 2021 (https://ieeexplore.ieee.org/document/9529198)

Sphingosine-1-phosphate-1 (S1P

Microscopic visualization of DNA molecules is a simple, intuitive, and powerful method. Nonetheless, DNA-molecule quantification methods that employ microscopic visualization have not been reported so far. In this study, a new quantitative approach is presented that enables the counting of individual DNA molecules that have been rendered visible by fluorescence microscopy. Toward this, a microfluidic device was employed that directed DNA molecules into microchannels and deposited the molecules onto a positively charged surface. This microfluidic device had a vertically tapered channel inlet structure that prevented the accumulation of excess DNA molecules in the channel inlet while creating a tapering flow, thereby ensuring the even distribution of the DNA molecules in the microchannels. The channel heights and the density of positive charges on the surface were optimized for analysis. The linearity of this method with respect to the determination of the concentration of DNA in solutions was subsequently determined. The limit of detection was 0.48 fg/μL, which corresponds to 64 molecules of 7.25 kbp DNA in 1 μL of sample. This quantitative approach was finally used to count two types of plasmids co-transformed in an E. coli cell; a measurement that is typically considered challenging with gel electrophoresis.

The sphingosine-1-phosphate-1 (S1P

Three-dimensional (3D) host architectures for Li metal storage have emerged as promising strategies for resolving the critical issues of Li metal anodes: severe volume changes and dendritic growth of Li during charge–discharge processes. However, the ionic resistance of electrolyte-filled pores causes the preferential deposition of Li on top of the 3D architecture (i.e., top plating), resulting in early cell failure. To realize Li metal anodes with high capacity and cyclability, herein, we report 3D-architectured electrodes with controlled interfacial activity based on metal–organic framework (MOF)-derived carbon hosts. Electrochemical simulations of Li plating processes in the 3D-architectured electrode suggest that the top plating can be effectively suppressed by increasing the interfacial activity across the thickness toward bottom of the electrode. MOF nanoparticles are pyrolyzed to form highly porous carbon hosts, and then, atomic Ag clusters are incorporated into the host by a galvanic displacement reaction to enhance the interfacial activity. A bi-layered carbon electrode with controlled interfacial activity is fabricated via sequential electrophoretic deposition of the host materials: i.e., as-pyrolyzed carbon on top and Ag-incorporated carbon on bottom. In comparison with the conventional electrode with uniform interfacial activity, the bi-layered electrode exhibits reduced overpotentials for Li plating and enhanced cyclability during galvanostatic Li plating–stripping. The combined computational and experimental studies further demonstrate that the bottom layer with atomic Ag clusters acts as a preferential site for Li nucleation even in the presence of kinetic limitations of ionic transport. As a result, Li deposits are confined in the bi-layered carbon electrode, thereby enhancing the reversibility of Li metal storage. This work provides an effective strategy to develop long-cycling, high-capacity Li metal anodes by controlling the interfacial activity of 3D carbon architectures.

All-solid-state batteries (ASSBs) offer a fundamental solution to mitigate the safety and reliability issues of conventional lithium-ion batteries utilizing flammable liquid electrolytes. However, interfacial resistances between the cathode materials and solid electrolytes, which originate from poor solid-solid contacts and detrimental interfacial reactions, limit the electrochemical performances of ASSBs. Recently, various approaches have been developed to realize high-capacity composite cathodes with improved cycling stability. For example, single-crystal LiNi0.8Co0.1Mn0.1O2 (NCM811) has been reported to mitigate the capacity fading resulting from cracking of commonly adopted multi-crystal NCM811. Also, halide-type solid electrolytes are reported to exhibit high Li-ion conductivity and electrochemical stability with NCM. In this work, we systematically study the interfacial and charge-transport impedances of NCM composite cathodes with halide solid electrolytes. The composite cathodes comprising single- or multi-crystal NCM811 and Li6PS5Cl or Li3InCl6 (and conductive agents) are fabricated to characterize their electrochemical impedance behaviors. In particular, we analyze the evolution of the interfacial and charge-transport impedances during charge–discharge cycling based on a multi-rail transmission line model combined with interfacial reactions. In addition, we investigate the chemical, electrochemical, and mechanical degradations of composite cathodes using various analytical tools to elucidate performance-determining factors of ASSBs.

Sleep staging is an important part of clinical neurology. However, it is still performed manually by technical experts and is labor-intensive and time-consuming. To overcome these obstacles in the manual sleep staging process, a large number of machine learning-based classifiers with hand-engineered features have been proposed. Additionally, combinations of a deep neural network (DNN) have been recently highlighted as the state-of-the-art classifiers in view of their effectiveness for automatic sleep staging. In spite of the existence of a large number of these types of classifiers, to-this-date, no prior DNN-based approach has attempted sleep-stage classification using pediatric electroencephalographic (EEG) signals. In this paper, we propose a novel end-to-end classifier based on a multi-domain hybrid neural network (HNN-multi) approach consisting of a convolutional neural network and bidirectional long short-term memory for automatic sleep staging with pediatric scalp EEG recordings. To find effective temporal, spatial, and domain-specific conditions, we investigated noticeable changes in the classification performance corresponding to: 1) the length of input signals; 2) the number of channels; and 3) the types of input signals in the time and frequency domains. Our HNN-based classifier yielded the best performance metrics using 30-s time series in combination with an instantaneous frequency using a 19-channel, three-stage classification, with overall accuracy, F1 score, and Cohen's Kappa, equal to 92.21%, 0.90, and 0.88, respectively. We suggest that an effective combination of temporal and spatial time-domain clues with time-varying frequency domain information plays a pivotal role in pediatric, automatic sleep staging. Sufficiently reasonable performance of our HNN-based approach coping with the highly complicated pediatric EEG signatures hopefully sheds light on the clinical feasibility of the DNN-based automatic sleep staging for pediatric neurology.

Instantaneous drowsiness ( i.e., lapse or micro-sleep) during various activities such as driving or construction causes enormous socioeconomic losses. Thus, a virtuous cycle system that monitors a subject’s drowsiness can improve work efficiency and safety. We propose a novel framework to detect instantaneous drowsiness with only a two-second length of electroencephalography (EEG). To achieve reliable performance, we use multitaper power spectral density for feature extraction along with extreme gradient boosting as a machine learning classifier. In addition, we introduce a novel phenotype labeling of instantaneous drowsiness by combining both task dependent and independent measures of alertness (psychomotor vigilance task and electrooculography technique, respectively). The results show that our techniques outperform others used in previous studies. We also identified which spectral components ( $\theta $ , $\alpha $ , and $\gamma $ ) and channels (Fp1, Fp2, T3, T4, O1, O2, and electrocardiogram) play important roles in our drowsiness detection framework. To verify the applicability for a mobile environment, we implemented our framework on a wireless EEG, as well as on a wired EEG. We hereby present our successful results.

Cisplatin (CP) is an anti-cancer drug used for treatment of solid tumors, but the major adverse effect is drug-induced nephrotoxicity. The current study aimed to determine biomarkers that might predict nephrotoxicity induced by CP using serum or urinary proton nuclear magnetic resonance (1H NMR) spectral data in male Sprague-Dawley (S-D) rats. CP (0, 0.5 or 5 mg/kg) was intraperitoneally (i.p.) administered for single dose. Animals were sacrificed 2 days (D2) or 8 days (D8) after administration of CP in order to perform analysis of serum biochemistries and histopathologic examination. Urine samples were collected every 24 hr from pre-treatment to sacrifice. Serum and urinary 1H NMR spectral data revealed apparent differential clustering between control and CP-treated groups as evidenced by principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) in global and targeted profiling. The concentrations of endogenous serum metabolites, alanine, betaine, glucose, glutamine, lactate, and leucine were significantly increased on D2. Urinary concentrations of alanine, glucose, glycine, guanidinoacetate, acetate, and lactate were significantly elevated on D2 or D8, whereas concentrations of urinary metabolites, citrate and hippurate were significantly decreased on D2 or D8. The correlation of serum and urinary 1H NMR OPLS-DA with serum biochemistry and renal histopathologic changes suggests that 1H NMR urinalysis may be used to reliably predict or screen CP-induced nephrotoxicity. Data suggest that these altered endogenous metabolites might serve as specific biomarkers for CP-induced nephrotoxicity.

We previously developed a novel series of vinyl sulfones as nuclear factor erythroid 2-related factor 2 (Nrf2) activators with therapeutic potential for Parkinson’s disease (PD). However, the previously developed lead compound (1) exhibited undesirable druglike properties. Here, we optimized vinyl sulfones by introducing nitrogen heterocycles to improve druglike properties. Among the synthesized compounds, 17e was the most promising drug candidate with good druglike properties. Compound 17e showed superior effects on Nrf2 activation in cell-based assays compared to compound 1 (17e: half-maximal effective concentration (EC50) = 346 nM; 1: EC50 = 530 nM). Compound 17e was further confirmed to induce expression of Nrf2-dependent antioxidant enzymes at both mRNA and protein levels. In a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced mouse model of PD, 17e significantly attenuated loss of tyrosine hydroxylase-immunopositive dopaminergic neurons, suppressed microglial activation, and alleviated PD-associa...

In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. However, most studies apply a peculiar evaluation protocol called point adjustment (PA) before scoring. In this paper, we theoretically and experimentally reveal that the PA protocol has a great possibility of overestimating the detection performance; that is, even a random anomaly score can easily turn into a state-of-the-art TAD method. Therefore, the comparison of TAD methods after applying the PA protocol can lead to misguided rankings. Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden. Based on our findings, we propose a new baseline and an evaluation protocol. We expect that our study will help a rigorous evaluation of TAD and lead to further improvement in future researches., Comment: 11 pages, 8 figures

Background and PurposeMonoamine oxidase-B (MAO-B) is a long-standing therapeutic target for Parkinson’s disease (PD), however, previous clinical studies demonstrated discouraging effects of currently available irreversible MAO-B inhibitors. Since KDS2010, a novel, potent, selective, and reversible MAO-B inhibitor, has been developed, here we tested its therapeutic potential in animal models of PD.Experimental ApproachWe designed and synthesized α-aminoamide derivatives and compared the specificity to MAO-B and reversibility of each compound with KDS2010. To investigate the in vivo therapeutic effect, we used MPTP mouse model with two different regimes of 3-day administration (pre-treatment or post-treatment) and 30-day administration. We assessed the therapeutic potential using behavioral and immunohistochemical analyses. Additionally, the functional recovery by KDS2010 was tested in 6-hydroxydopamine-induced and A53T-alpha-synuclein overexpression models. Lastly, to validate the potential as a clinical drug candidate, we investigated the pharmacokinetics and toxicity of KDS2010 in non-human primates.Key ResultsKDS2010 showed the highest potency, specificity, and reversibility among the α-aminoamide derivatives, with high bioavailability (>100%) and BBB permeability. KDS2010 also showed significant neuroprotective and anti-neuroinflammatory effects in the nigrostriatal pathway, leading to an alleviation of MPTP-induced parkinsonism in all administration regimes. In particular, the therapeutic effect of KDS2010 was superior to selegiline, an irreversible MAO-B inhibitor. KDS2010 also showed a potent therapeutic effect in 6-hydroxydopamine and A53T models. Moreover, KDS2010 showed virtually no toxicity or side-effect in non-human primates.Conclusion and ImplicationsKDS2010 shows excellent therapeutic potential and safety in various PD animal models. KDS2010, therefore, could be a next-generation therapeutic candidate for PD.Representative SchematicWhat is already knownKDS2010 is a recently developed potent, selective, and reversible MAO-B inhibitor.MAO-B is critical for PD pathology through astrocytic GABA and H2O2 synthesis.What this study addsKDS2010 treatment dramatically recovers from PD-related pathology and motor deficit after pre- and post-treatment regimes in several animal models of PD.KDS2010 exhibits low toxicity and excellent pharmacokinetic profile in non-human primates.What is the clinical significance?KDS2010 is a safe and promising therapeutic candidate for Parkinson’s disease.Reversible MAO-B inhibitors could be more effective for treatment of Parkinson’s disease, overcoming the short-lived actions of irreversible MAO-B inhibitors.

To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input. Since existing methods replace each token with a predefined value (i.e., zero), the resulting sentence lies out of the training data distribution, yielding misleading interpretations. In this study, we raise the out-of-distribution problem induced by the existing interpretation methods and present a remedy; we propose to marginalize each token out. We interpret various NLP models trained for sentiment analysis and natural language inference using the proposed method., Comment: 10 pages, 5 figures, to be published in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)

Pt is used in many catalytic applications from three-way catalysts to fuel cells. Particle size and morphology are key parameters that influence the electrocatalytic activity. Flame synthesis is an efficient and continuous manufacturing route capable of producing Pt nanoparticles with desirable catalytic properties. In order to understand the effect of the forced oxidant, solvent composition, and quench rate on nanoparticle formation, a systematic study of process parameters using Pt was undertaken. Reactive spray deposition technology was used as the synthesis platform. In order to obtain a tight control of the Pt size, it was found that the fuel must contain a sufficient enthalpy of combustion and appropriate propane content. The fuel must be mixed adequately to form a turbulent diffusion flame and must create conditions where unwanted Pt coarsening cannot occur. From this study, the conditions that favor this morphology require an O2 flow rate setting of 6.89 L/min or higher, a propane content of 20 wt%, and no quenching. Electrochemical data show decreasing electrochemical surface area (75–36 m2/gPt), increasing mass activity (120–610 mA/mgPt), and increasing specific activity (150–1350 $$ \upmu {\text{A}}/ {\text{cm}}^{2} {_{\text{Pt}}} $$ ) with increasing oxidant flow.

Identification of suspected automobile-related lubricants could provide valuable information in forensic cases. We examined that automobile lubricants might exhibit the chemometric characteristics to their individual usages. To compare the degree of clustering in the plots, we co-plotted general industrial oils that were highly dissimilar with automobile lubricants in additive compositions. 1 H-NMR spectroscopy was used with multivariate statistics as a tool for grouping, clustering, and identification of automobile lubricants in laboratory conditions. We analyzed automobile lubricants including automobile engine oils, automobile transmission oils, automobile gear oils, and motorcycle oils. In contrast to the general industrial oils, automobile lubricants showed relatively high tendencies of clustering to their usages. Our pilot study demonstrated that the comparison of known and questioned samples to their usages might be possible in forensic fields.

Mesoporous carbon nanoparticles (MCNs) have been applied in a variety of drug/gene carriers. In addition to their potential benefits, many studies of their potential toxicity have been reported, showing the limitations of metabolic contextualization. In this study, we conducted 1H-nuclear magnetic resonance (NMR) profiling combined with statistical methods such as orthogonal partial least squares discriminant analysis and Pearson correlation analysis to assess metabolic alterations in the whole body of zebrafish (Danio rerio) in the presence of various concentrations of MCNs. The MCN exposure influenced numerous metabolites in energy metabolism (e.g., metabolites involved in glycolysis and tricarboxylic acid cycle) and disturbed the balance of neurotransmitters and osmoregulators. Our findings demonstrate the potential applicability of using a metabolomics approach to determine underlying metabolic disturbances caused by MCNs.

Proton nuclear magnetic resonance (1H-NMR) spectroscopy was used to study the response of zebrafish (Danio rerio) to increasing concentrations of bisphenol A (4,4′-(propane-2,2-diyl)diphenol, BPA). Orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to detect aberrant metabolomic profiles after 72 h of BPA exposure at all levels tested (0.01, 0.1, and 1.0 mg/L). The OPLS-DA score plots showed that BPA exposure caused significant alterations in the metabolome. The metabolomic changes in response to BPA exposure generally exhibited nonlinear patterns, with the exception of reduced levels of several metabolites, including glutamine, inosine, lactate, and succinate. As the level of BPA exposure increased, individual metabolite patterns indicated that the zebrafish metabolome was subjected to severe oxidative stress. Interestingly, ATP levels increased significantly at all levels of BPA exposure. In the present study, we demonstrated the applicability of 1H-NMR-based metabolomi...

Network slicing is envisioned as one of the core enablers for 5G. It empowers service providers to multiplex their physical infrastructure over multiple logical networks. Slice orchestration performs lifecycle management and virtual resource management of a network slice. Slice capacity planning, one of the key responsibilities of slice orchestration faces two major challenges: (i) minimum slice capacity estimation for a given slice configuration, and (ii) lack of simple network slicing supported open source solutions due to which the research and development of slice resource orchestration activities are hit adversely. The feature-rich existing solutions have significantly high learning curve. This paper addresses these two issues. We present a probabilistic model for slice capacity planning and propose a simple orchestration-centric discrete event simulator to develop and evaluate minimum slice capacity requirement. The results taken on our custom simulator show around 96% of resource utilization, and have less than 5% relative error when compared with the theoretical model.

The Kelch-like ECH-associated protein 1 (Keap1)-Nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway plays a pivotal role in the cellular defense system against oxidative stress by inducing antioxidant and anti-inflammatory effects. We previously developed Nrf2 activators that potentially protect the death of dopaminergic (DAergic) neuronal cells against oxidative stress in Parkinson’s disease (PD). In this study, we designed and synthesized a class of halogenated vinyl sulfones by inserting halogens and pyridine to maximize Nrf2 activation efficacy. Among the synthesized compounds, (E)-3-chloro-2-(2-((2-chlorophenyl)sulfonyl)vinyl)pyridine (9d) significantly exhibited potent Nrf2 activating efficacy (9d: EC50 = 26 nM) at least 10-fold compared with the previous developed compounds (1 and 2). Furthermore, treating with 9d remarkably increased Nrf2 nuclear translocation and Nrf2 protein levels in microglial BV-2 cells. 9d was shown to induce the expression of antioxidant response genes HO-1, GCLC, GCLM, and SOD-1 at both the mRNA and protein levels and suppress proinflammatory cytokines and enzymes. Also, 9d remarkably protected DAergic neurons and restored the PD-associated motor dysfunction in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse model.

A 1H-nuclear magnetic resonance (NMR) spectroscopy with multivariate analysis was applied to detect the toxicity of antiacetylcholinesterase insecticides, methomyl (methyl (1E)-N-(methylcarbamoyloxy)ethanimidothioate) and methidathion (3-(dimethoxyphosphinothioyl sulfanylmethyl)-5-methoxy-1,3,4-thiadiazol-2-one), using zebrafish (Danio rerio) and Chinese bleak (Aphyocypris chinensis). Generally, methomyl and methidathion have been believed not to highly accumulate in fish tissues. However, these pesticides showed their toxicity by altering patterns of whole-body metabolites in neurotransmitter balance, energy metabolism, oxidative stress, and muscle maintenance in low concentrations. We used Pearson correlation analysis to contextualize the metabolic markers in pesticide treated groups. We observed that the positive correlations of choline with acetate and betaine in untreated control were shifted to null correlations showing acetylcholinesterase specific toxicity. This research demonstrated the a...

Aging and age-related diseases in humans are mainly caused by oxidative stress through over-production of reactive oxygen species (ROS). In our previous study, multi-micronutrient supplement containing antioxidants and phytochemicals (MP) resulted in higher antioxidative activity by increasing plasma folate with resistance to DNA damage and LDL oxidation to subjects. In this study, plasma samples were analysed for metabolomics study using 1 H NMR. The metabolic profiles before MP supplementation and after 8-week MP supplementation were clearly separated based on multivariant analyses of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). Twenty-seven metabolites from variable importance plots were screened before MP supplementation and after 8-week MP supplementation. The 27 metabolites had significantly increased levels of betaine but significantly decreased levels of choline, serine, and threonine after MP supplementation. These results suggested that MP supplementation could activate folate cycle to increase resistance to DNA damages.

Heavy metals are common marine and soil pollutants that are mainly the result of industrial activity, and are a threat to the environment and human health. In this study, 1H nuclear magnetic resonance (NMR)-based metabolomics was applied to adult Danio rerio to monitor the metabolic change as a response to ZnCl2 and CdCl2 exposure at different concentrations for 72 h. NMR spectroscopy was used to identify and quantify the metabolites extracted from D. rerio. The metabolite profiles of the control and heavy metal exposed group were classified by partial least squares – discriminant analysis (PLS-DA) analysis, and potential contaminant-specific biomarkers were suggested. For the ZnCl2-exposed zebrafish, the levels of ATP, aspartate and NAD+ were increased, whereas the levels of formate, inosine, hypoxanthine and succinate decreased. In addition, the CdCl2-exposed zebrafish showed an increase in the levels of ATP and formate and a decrease in the levels of glutamate, inosine and glutathione. Overall,...

To evaluate whether metabolic profiles obtained using high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) and total choline-containing compound (tCho) on in vivo MRS could predict indolent tumors based on highly favorable prognostic factors. We analyzed 50 frozen tissue samples from 50 patients (mean 46.4 years, range 29–72 years) with breast cancer using HR MAS MRS. In vivo single-voxel MRS analyses were also performed on these patients preoperatively. We defined estrogen receptor (ER)-positive tumors with a low histological grade, low Ki-67-positivity (

The discovery of new biomarkers for early detection of drug-induced acute kidney injury (AKI) is clinically important. In this study, sensitive metabolomic biomarkers identified in the urine of rats were used to detect cisplatin-induced AKI. Cisplatin (10 mg kg(-1), i.p.) was administered to Sprague-Dawley rats, which were subsequently euthanized after 1, 3 or 5 days. In cisplatin-treated rats, mild histopathological alterations were noted at day 1, and these changes were severe at days 3 and 5. Blood urea nitrogen (BUN) and serum creatinine (SCr) levels were significantly increased at days 3 and 5. The levels of new urinary protein-based biomarkers, including kidney injury molecule-1 (KIM-1), glutathione S-transferase-α (GST-α), tissue inhibitor of metalloproteinase-1 (TIMP-1), vascular endothelial growth factor (VEGF), calbindin, clusterin, neutrophil, neutrophil gelatinase-associated lipocalin (NGAL), and osteopontin, were significantly elevated at days 3 and 5. Among urinary metabolites, trigonelline and 3-indoxylsulfate (3-IS) levels were significantly decreased in urine collected from cisplatin-treated rats prior to histological kidney damage. However, carbon tetrachloride (CCl4), a hepatotoxicant, did not affect these urinary biomarkers. Trigonelline is closely associated with GSH depletion and results in insufficient antioxidant capacity against cisplatin-induced AKI. The predominant cisplatin-induced AKI marker appeared to be reduced in urinary 3-IS levels. Because 3-IS is predominantly excreted via active secretion in proximal tubules, a decrease is indicative of tubular damage. Further, urinary excretion of 3-IS levels was markedly reduced in patients with AKI compared to normal subjects. The area under the curve receiver operating characteristics (AUC-ROC) for 3-IS was higher than for SCr, BUN, lactate dehydrogenase (LDH), total protein, and glucose. Therefore, low urinary or high serum 3-IS levels may be more useful for early detection of AKI than conventional biomarkers.

The socioeconomic losses caused by extreme daytime drowsiness are enormous in these days. Hence, building a virtuous cycle system is necessary to improve work efficiency and safety by monitoring instantaneous drowsiness that can be used in any environment. In this paper, we propose a novel framework to detect extreme drowsiness using a short time segment (~ 2 s) of EEG which well represents immediate activity changes depending on a person's arousal, drowsiness, and sleep state. To develop the framework, we use multitaper power spectral density (MPSD) for feature extraction along with extreme gradient boosting (XGBoost) as a machine learning classifier. In addition, we suggest a novel drowsiness labeling method by combining the advantages of the psychomotor vigilance task and the electrooculography technique. By experimental evaluation, we show that the adopted MPSD and XGB techniques outperform other techniques used in previous studies. Finally, we identify that spectral components (theta, alpha, and gamma) and channels (Fp1, Fp2, T3, T4, O1, and O2) play an important role in our drowsiness detection framework, which could be extended to mobile devices.

Recently, advances in technology have reduced the sizes, lowered prices, and increased commercial use of drones. In the market, drones have become an inexpensive and accessible way to accomplish many tasks such as filming, surveillance, agriculture, delivery, and broadcasting. Such applications typically require a video recording for their operations. However, due to the noise generated by propellers and rotors of a drone, the quality of sound in a video recorded by the drone is quite limited. In order to solve the noise problem of drones, this paper proposes a novel hybrid noise reduction technique combining Active Noise Control and Spectral subtraction methods. Experimental results show that the proposed technique produces a speech signal with up to 67.5% similarity to the original signal. It outperforms Active Noise Control and Spectral Subtraction with similarities of 53.1% and 39.6%, respectively.

The effects of polytetrafluoroethylene (PTFE) binder content in the catalyst layer of high temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) utilizing phosphoric acid doped Advent TPS® polymer electrolyte membranes (pyridine bearing aromatic polyethers, TPS) has been investigated in terms of both hydrogen/oxygen and hydrogen/air performance. The anode and cathode gas diffusion electrodes (GDE) were fabricated with different PTFE/carbon weight ratios by a flame based process known as the Reactive Spray Deposition Technology (RSDT) method in order to increase the active platinum (Pt) surface area, with a goal of decreasing overall Pt levels to a total loading of 0.1 mg cm−2. The electrodes, prepared with different amounts of PTFE binder, have been tested in a single cell, with a 25 cm2 geometric area, under an operating temperature range of 160–200 °C. Tests measuring the Pt nanoparticle dispersion on the carbon supports, the pore size distribution, and the electrochemical surface area of the catalyst layer were also performed. The best cell performance was achieved with PTFE/carbon weight ratio of 0.9 over the entire range of operating temperatures. This optimal PTFE binder content resulted in well-developed Pt dispersion on the carbon support and small, uniformly sized pores which develop ideal capillary forces for distributing the phosphoric acid electrolyte evenly throughout the catalyst layer. This led to a high number of triple phase boundaries and maximized Pt utilization.

Benzyloxyphenyl moiety is a common structure of highly potent, selective and reversible inhibitors of monoamine oxidase B (MAO-B), safinamide and sembragiline. We synthesized 4-(benzyloxy)phenyl and biphenyl-4-yl derivatives including halogen substituents on the terminal aryl unit. In addition, we modified the carbon linker between amine group and the biaryl linked unit. Among synthesized compounds, 12c exhibited the most potent and selective MAO-B inhibitory effect (hMAO-B IC50: 8.9 nM; >10,000-fold selectivity over MAO-A) as a competitive inhibitor. In addition, 12c showed greater MAO-B inhibitory activity and selectivity compared to well-known MAO-B inhibitors such as selegiline, safinamide and sembragiline. In the MPTP-induced mouse model of Parkinson’s disease (PD), 12c significantly protected the tyrosine hydroxylase (TH)-immunopositive DAergic neurons and attenuated the PD-associated behavioral deficits. This study suggests characteristic structures as a MAO-B inhibitor that may provide a good insight for the development of therapeutic agents for PD.

Estrogen receptor (ER)-positive breast cancers overall have a good prognosis, however, some patients suffer relapses and do not respond to endocrine therapy. The purpose of this study was to determine whether there are any correlations between high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) metabolic profiles of core needle biopsy (CNB) specimens and the molecular markers currently used in patients with ER-positive breast cancers. The metabolic profiling of CNB samples from 62 ER-positive cancers was performed by HR-MAS MRS. Metabolic profiles were compared according to human epidermal growth factor receptor 2 (HER2) and Ki-67 status, and luminal type, using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). In univariate analysis, the HER2-positive group was shown to have higher levels of glycine and glutamate, compared to the HER2-negative group (P