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Discovering and understanding new materials with desired properties are at the heart of materials science research, and machine learning (ML) has recently offered special shortcuts to the ultimate goal. Thanks to the nourishment of computer hardware and computational chemistry, the development of calculated scientific data repositories could fuel the ML models to investigate the vast materials space. At this moment, understanding this revolutionary paradigm is urgent, and this Review aims to deliver comprehensive information about the collaboration of ML with materials science. This Review summarizes recent achievements in catalysts design, which can be benefitted from ML because of the complex nature of catalytic reactions and vast candidate materials space. ML models for catalyst design could be transferred to applications in other domains and vice versa. The basic concepts of ML algorithms and practical guides to materials scientists are also demonstrated. Moreover, challenges and strategies of applying ML are discussed, which should be addressed collaboratively between materials scientists and ML communities. Ultimate integrations of ML in materials science are expected to accelerate the design, discovery, optimization, and interpretation of materials in both industry and academia, and this Review hopes to be the informative base camp for that journey.

In this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. To this end, we present a new practical task where only a few utterances of each fictional character are available to generate responses mimicking them. Furthermore, we propose a new method named Pseudo Dialog Prompting (PDP) that generates responses by leveraging the power of large-scale language models with prompts containing the target character's utterances. To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character's utterances as dialog history. Since only utterances of the characters are available in the proposed task, PDP matches each utterance with an appropriate pseudo-context from a predefined set of context candidates using a retrieval model. Through human and automatic evaluation, we show that PDP generates responses that better reflect the style of fictional characters than baseline methods., NAACL2022 (Short)

Gut microbes are closely associated with disease onset and improvement. However, the effects of gut microbes on the occurrence, prevention, and treatment of benign prostatic hyperplasia (BPH) are still unclear. We investigated the alteration of gut microbiota with implications for the diagnosis, prevention, and treatment of BPH and identified correlations among various indicators, including hormone indicators, apoptosis markers in BPH, and finasteride treatment models. BPH induction altered the abundance of Lactobacillus, Flavonifractor, Acetatifactor, Oscillibacter, Pseudoflavonifractor, Intestinimonas, and Butyricimonas genera, which are related to BPH indicators. Among these, the altered abundance of Lactobacillus and Acetatifactor was associated with the promotion and inhibition of prostate apoptosis, respectively. Finasteride treatment altered the abundance of Barnesiella, Acetatifactor, Butyricimonas, Desulfovibrio, Anaerobacterium, and Robinsoniella genera, which are related to BPH indicators. Among these, altered abundances of Desulfovibrio and Acetatifactor were associated with the promotion and inhibition of prostate apoptosis, respectively. In addition, the abundances of Lactobacillus and Acetatifactor were normalized after finasteride treatment. In conclusion, the association between apoptosis and altered abundances of Lactobacillus and Acetatifactor, among other gut microbes, suggests their potential utility in the diagnosis, prevention, and treatment of BPH.

Neuromorphic computing using analog non-volatile memory (NVM) devices has been the subject of various studies due to its potential ability to achieve extremely low power consumption less than that of traditional von Neumann architecture. However, using NVM devices, such as phase change memory (PCM) and resistive-RAM devices, presents various challenges, such as limitations in the number of conductance steps and device variability resulting from process variation and electro/thermo-dynamics. Limitations in the number of conductance steps and device variability could reduce the accuracy of neural network training. It is necessary to quantitatively analyze the effect of the number of conductance steps and synaptic device variability on the accuracy of neural network training and assess requirements for NVM devices to make NVM-based neuromorphic computing successful. We conducted the analysis using simulations focusing on a spiking neural network (SNN) based restricted Boltzmann machine (RBM) with PCM devices using the PCM-refresh method. The results of our quantitative simulation, which used the MNIST dataset, showed that having more than 500 conductance steps achieves comparable performance to that when there are more than 1000 conductance steps. We also found that less than 10% conductance update variation in the synaptic devices is required to achieve the comparable accuracy with the no variation case. These results can provide guidelines for designing and optimizing a synaptic device for realizing NVM-based neuromorphic computing.

Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low‐impedance sensing are desired. However, it has been a struggle to find materials and fabrication methods that satisfy both of these properties simultaneously in a single substrate. Here, nanofiber channels with a self‐formed ion‐blocking layer are fabricated to create organic electrochemical transistors (OECTs) that can be tailored to achieve low‐power neuromorphic computing and fast‐response sensing by transferring different amounts of electrospun nanofibers to each device. With their nanofiber architecture, the OECTs exhibit a low switching energy of 113 fJ and operate within a wide bandwidth (cut‐off frequency of 13.5 kHz), opening a new paradigm for energy‐efficient neuromorphic computing/sensing platforms in a biological environment without the leakage of personal information., Concepts of organic neuromorphic computing/sensing platforms for local monitoring and processing of biological signals in real time are presented. With a nanofiber architecture as a channel of a transistor, low‐power neuromorphic devices and wide‐bandwidth sensors are fabricated together on a single substrate without changing the materials, structures, and fabrication processes, demonstrating the future applications for neuromorphic computing/sensing platforms.

Acacetin, an important ingredient of acacia honey and a component of several medicinal plants, exhibits therapeutic effects such as antioxidative, anticancer, anti-inflammatory, and anti-plasmodial activities. However, to date, studies reporting a systematic investigation of the in vivo fate of orally administered acacetin are limited. Moreover, the in vitro physicochemical and biopharmaceutical properties of acacetin in the gastrointestinal (GI) tract and their pharmacokinetic impacts remain unclear. Therefore, in this study, we aimed to systematically investigate the oral absorption and disposition of acacetin using relevant rat models. Acacetin exhibited poor solubility (&le, 119 ng/mL) and relatively low stability (27.5&ndash, 62.0% remaining after 24 h) in pH 7 phosphate buffer and simulated GI fluids. A major portion (97.1%) of the initially injected acacetin dose remained unabsorbed in the jejunal segments, and the oral bioavailability of acacetin was very low at 2.34%. The systemic metabolism of acacetin occurred ubiquitously in various tissues (particularly in the liver, where it occurred most extensively), resulting in very high total plasma clearance of 199 &plusmn, 36 mL/min/kg. Collectively, the poor oral bioavailability of acacetin could be attributed mainly to its poor solubility and low GI luminal stability.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

PF-543, the most potent sphingosine kinase (SK) inhibitor, does not demonstrate effective anticancer activity in some cancer cells, unlike other known SK1 inhibitors. PF-543 has a non-lipid structure with a unique toluene backbone, however, the importance of this structure remains unclear. Therefore, the purpose of this study was to investigate changes in SK inhibitory and anticancer activities and to explore the role of the tolyl group structure of PF-543 through various modifications. We transformed the methyl group of PF-543 into hydrogen, fluorine, and hydroxy. PF-543 derivatives in which the methyl group was substituted by hydrogen and fluorine (compound 5) demonstrated SK1 inhibitory and anticancer activities similar to PF-543. Moreover, we performed molecular modeling studies of PF-543 and compound 5. To assess the metabolic stability of PF-543 and compound 5, we determined their degree of degradation using the liver microsomes of four different animal species (human, dog, rat, and mouse). However, both PF-543 and compound 5 showed poor microsomal stability. Therefore, for the medical applications of PF-543, the structural modifications of its other parts may be necessary. Our results provide important information for the design of additional PF-543 analogs.

Background/Aim: Lung cancer shows the highest estimated deaths in both males and females in the Unites States. Importin 7 is overexpressed in lung adenocarcinoma tissues. In this study, we aimed to demonstrate the anticancer effect of importin 7 down-regulation, especially in lung cancer. Materials and Methods: Glycerol propoxylate triacrylate spermine (GPT-SPE) is a biocompatible carrier used for aerosol gene delivery. Repeated aerosol delivery of GPT-SPE/shImportin 7 complexes was performed to 10-week-old male K-rasLA1mice (a murine lung cancer model) twice a week for 4 weeks (8 times) in a nose-only exposure chamber. Results: Aerosol delivery of GPT-SPE/shImportin 7 inhibits lung cancer in K-rasLA1mice compared to control and scramble control groups. Moreover, importin 7-down-regulated stable cell-line demonstrates suppression of proliferation through Akt inhibition and apoptosis. Conclusion: Down-regulation of importin 7 significantly suppresses lung cancer in vitro and in vivo.

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and ‘Memcomputing’. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix–vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices – including phase change memory, conductive-bridging RAM, filamentary and non-filamentary RRAM, and other NVMs – have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.

Exponential growth in data generation and large-scale data science has created an unprecedented need for inexpensive, low-power, low-latency, high-density information storage. This need has motivated significant research into multi-level memory devices that are capable of storing multiple bits of information per device. The memory state of these devices is intrinsically analog. Furthermore, much of the data they will store, along with the subsequent operations on the majority of this data, are all intrinsically analog-valued. Ironically though, in the current storage paradigm, both the devices and data are quantized for use with digital systems and digital error-correcting codes. Here, we recast the storage problem as a communication problem. This then allows us to use ideas from analog coding and show, using phase change memory as a prototypical multi-level storage technology, that analog-valued emerging memory devices can achieve higher capacities when paired with analog codes. Further, we show that storing analog signals directly through joint coding can achieve low distortion with reduced coding complexity. Specifically, by jointly optimizing for signal statistics, device statistics, and a distortion metric, we demonstrate that single-symbol analog codings can perform comparably to digital codings with asymptotically large code lengths. These results show that end-to-end analog memory systems have the potential to not only reach higher storage capacities than discrete systems but also to significantly lower coding complexity, leading to faster and more energy efficient data storage.

Lysyl-tRNA synthetase (KRS) can be released from cancer cells to cause inflammation, but the mechanism of KRS secretion is unknown. Kim et al. demonstrate that KRS is cleaved by caspase-8, which exposes a binding motif for syntenin and facilitates the secretion of KRS in exosomes., Aminoacyl-tRNA synthetases (ARSs), enzymes that normally control protein synthesis, can be secreted and have different activities in the extracellular space, but the mechanism of their secretion is not understood. This study describes the secretion route of the ARS lysyl-tRNA synthetase (KRS) and how this process is regulated by caspase activity, which has been implicated in the unconventional secretion of other proteins. We show that KRS is secreted from colorectal carcinoma cells within the lumen of exosomes that can trigger an inflammatory response. Caspase-8 cleaved the N-terminal of KRS, thus exposing a PDZ-binding motif located in the C terminus of KRS. Syntenin bound to the exposed PDZ-binding motif of KRS and facilitated the exosomic secretion of KRS dissociated from the multi-tRNA synthetase complex. KRS-containing exosomes released by cancer cells induced macrophage migration, and their secretion of TNF-α and cleaved KRS made a significant contribution to these activities, which suggests a novel mechanism by which caspase-8 may promote inflammation.

We study theoretically the influence of the surface plasmon excitation on the Goos-H\"{a}nchen lateral shift of a $p$-polarized Gaussian beam incident obliquely on a dielectric-metal bilayer in the Otto configuration. We find that the lateral shift depends sensitively on the thickness of the metal layer and the width of the incident beam, as well as on the incident angle. Near the incident angle at which surface plasmons are excited, the lateral shift changes from large negative values to large positive values as the thickness of the metal layer increases through a critical value. For wide incident beams, the maximal forward and backward lateral shifts can be as large as several hundred times of the wavelength. As the width of the incident Gaussian beam decreases, the magnitude of the lateral shift decreases rapidly, but the ratio of the width of the reflected beam to that of the incident beam, which measures the degree of the deformation of the reflected beam profile, increases. In all cases considered, we find that the reflected beam is split into two parts. We also find that the lateral shift of the transmitted beam is always positive and very weak., Comment: 16 pages, 7 figures

Summary The expeditious development of information technology has led to the rise of artificial intelligence (AI). However, conventional computing systems are prone to volatility, high power consumption, and even delay between the processor and memory, which is referred to as the von Neumann bottleneck, in implementing AI. To address these issues, memristor-based neuromorphic computing systems inspired by the human brain have been proposed. A memristor can store numerous values by changing its resistance and emulate artificial synapses in brain-inspired computing. Here, we introduce six types of memristors classified according to their operation mechanisms: ionic migration, phase change, spin, ferroelectricity, intercalation, and ionic gating. We review how memristor-based neuromorphic computing can learn, infer, and even create, using various artificial neural networks. Finally, the challenges and perspectives in the competing memristor technology for neuromorphic computing systems are discussed., Graphical abstract, Magnetism; Electromagnetics; Computing Methodology; Devices

Relatively large noise of phase change material is one of the obstacles for realization of multilevel cell (MLC) using phase change memory (PCM) technology. We experimentally verify that the noise in PCM can be lowered as much as $\sim 4$ times by a novel PCM cell which utilizes metal nitride liner to provide an alternative conductive path to the amorphous region with large noise. Program-and-verify (PNV) simulation based on noise measurement data confirms that the small noise of a PCM cell with metal nitride liner improves the MLC performance either by reducing number of PNV iterations or bit-error rate.

An empirical, versatile finite-element model is developed to predict void formation in as-deposited or melt-quenched amorphous Ge2Sb2Te5 during annealing. This model incorporates void formation with nucleation and growth of the crystals along with thermal models that capture laser heating of the nano-structures during device fabrication. Modeling of void formation during Joule heating or furnace annealing can be implemented in a similar way. The modeling results are compared to example experimental results obtained from pore-cell phase change memory structures.

Honokiol (2-(4-hydroxy-3-prop-2-enyl-phenyl)-4-prop-2-enyl-phenol) and magnolol (4-Allyl-2-(5-allyl-2-hydroxy-phenyl)phenol) are the major active polyphenol constituents of Magnolia officinalis (Magnoliaceae) bark, which has been widely used in traditional Chinese medicine (Houpu Tang) for the treatment of various diseases, including anxiety, stress, gastrointestinal disorders, infection, and asthma. The aim of this study was to investigate the direct effects of honokiol and magnolol on hepatic CYP1A and 2C-mediated metabolism in vitro using rat liver microsomes and in vivo using the Sprague-Dawley rat model. Honokiol and magnolol inhibited in vitro CYP1A activity (probe substrate: phenacetin) more potently than CYP2C activity (probe substrate: diclofenac): The mean IC50 values of honokiol for the metabolism of phenacetin and diclofenac were 8.59 &mu, M and 44.7 &mu, M, while those of magnolol were 19.0 &mu, M and 47.3 &mu, M, respectively. Notably, the systemic exposure (AUC and Cmax) of phenacetin, but not of diclofenac, was markedly enhanced by the concurrent administration of intravenous honokiol or magnolol. The differential effects of the two phytochemicals on phenacetin and diclofenac in vivo pharmacokinetics could at least be partly attributed to their lower IC50 values for the inhibition of phenacetin metabolism than for diclofenac metabolism. In addition, the systemic exposure, CL, and Vss of honokiol and magnolol tended to be similar between the rat groups receiving phenacetin and diclofenac. These findings improve our understanding of CYP-mediated drug interactions with M. officinalis and its active constituents.

We report the synthesis of quaternary tungsten bronze nanocrystals (QTBN) doped with sodium and cesium, Na x Cs y WO z , which were prepared via a simple thermal decomposition process involving the combination of ammonium metatungstates with oleylamine as both the surfactant and the solvent. The QTBN capped with oleylamine had an average diameter of about 30 nm and exhibited a shielding property of approximately 97% of near-infrared radiation across a wavelength range of 780–2100 nm, while transmitting 64% of visible light at 432 nm upon dispersion in a non-polar solvent of toluene. Our characterizations showed that both sodium and cesium ions could successfully be intercalated into the framework of the cubic pyrochlore structure of tungsten oxide at a relatively low reaction temperature and within a short time, generating the quaternary compound of tungsten bronze nanoparticles. As a result, our procedure conferred near infrared absorption properties upon the QTBN that are superior to those of ternary tungsten bronze nanoparticles in terms of absorption range and intensity, suggesting a significantly advanced solution process capable of producing useful near infrared absorbents.

Optical diffraction tomography (ODT) provides label-free three-dimensional (3D) refractive index (RI) measurement of biological samples. However, due to the nature of the RI values of biological specimens, ODT has limited access to molecular specific information. Here, we present an optical setup combining ODT with three-channel 3D fluorescence microscopy, to enhance the molecular specificity of the 3D RI measurement. The 3D RI distribution and 3D deconvoluted fluorescence images of HeLa cells and NIH-3T3 cells are measured, and the cross-correlative analysis between RI and fluorescence of live cells are presented.

We develop an invariant imbedding method for the computation of the electromagnetic scattering cross-section from three-dimensional spherical bodies. We demonstrate that we can precisely calculate quantities of interest in the scattering and the absorption of an incident plane wave in various situations, such as propagation through arbitrarily inhomogeneous media and multilayered structures. Examples include scattering by a Luneburg lens and by a spherical cavity enclosed in a spherical photonic crystal structure, the layers of which satisfy the Bragg conditions for the incident wavelengths. In the latter case, light confinement in a very small core with a radius of 1.55 µm with high quality factors at a level of 1.7 × 106 could be achieved.

Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. Emerging memory technologies such as nanoscale two-terminal resistive switching memory devices offer a compact, scalable and low power alternative that permits on-chip co-located processing and memory in fine-grain distributed parallel architecture. Here we report first use of resistive switching memory devices for implementing and training a Restricted Boltzmann Machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive switching phase change memory (PCM) elements trained with a bio-inspired variant of the Contrastive Divergence (CD) algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a two-fold to ten-fold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared to untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the resistive switching PCM devices, a factor of ~150 times lower than conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations as well as number of gradual levels in the PCM analog memory devices., Accepted for publication in IEEE Transactions on Electron Devices. This version is the submitted version

Polyurethane foams were synthesized by substituting a portion of petroleum base polyether pol-yol with castor oil-derived polyol(COP). Contact angle tester and surface tensionmeter were used to examine the compatibility of petroleum base polyether polyol and COP. To investigate the optimum content of COP and surfactant, the content of COP has been changed from 0 wt% to 80 wt%. From the results of polyurethane foams synthesized by surfactant L-580K, DC-5950 and BF-2470, the best mechanical properties was observed when the content of COP was 30wt% and surfactant BF-2470.Key words : polyurethane foam, castor oil polyol, property, compatibility 1) I. 서 론 1894년 독일의 Wurtz와 Hoffman이 최초로 하이드록실기(hydroxyl group) 화합물과 이소시아네이트(isocyanate)와의 반응을 발표하면서 세상에 알려진 폴리우레탄은 1937년 Otto Bayer에 의하여 상업적인 용도로 개발되었다. 오늘날 폴리우레탄은 조선(ship-building), 건축, 자동차, 신발에 이르기까지 많은 산업 † 교신저자:ksb@kgu.ac.kr에서 중요한 역할을 한다. 경질 폴리우레탄 폼은 가정용 냉장고에서 LNG선박 및 저장탱크용 단열재에 이르기까지 다양하게 이용되고 있는 효과적인 단열재이다. 편안하고 튼튼한 매트리스와 자동차, 가정용 의자는 연질 폴리우레탄 폼으로부터 제조 된다. 또한 신발 밑창, 스포츠 장비, 자동차 범퍼들은 폴리우레탄 엘라스토머의 형태로 만들어진다[1]. 폴리우레탄은 하이드록실기를 가지는 폴리올 (po-lyol)과 폴리이소시아네이트(polyisocyanate)와의 발열 반응으로 만들어 지게 되는데 현재 이 두 물질들은 KIGAS Vol. 16, No. 5, pp 66~75, 2012 http://dx.doi.org/10.7842/kigas.2012.16.5.66(Journal of the Korean Institute of Gas)