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Structural basis and synergism of ATP and Na⁺ activation in bacterial K⁺ uptake system KtrAB

Nature Communications 2024年第1期15卷 3850页

作者： Chiang, Wesley Tien Chang, Yao-Kai Hui, Wei-Han Chang, Shu-Wei Liao, Chen-Yi Chang, Yi-Chuan Chen, Chun-Jung Wang, Wei-Chen Lai, Chien-Chen Wang, Chun-Hsiung Luo, Siou-Ying Huang, Ya-Ping Chou, Shan-Ho Horng, Tzyy-Leng Hou, Ming-Hon Muench, Stephen P. Chen, Ren-Shiang Tsai, Ming-Daw Hu, Nien-Jen Graduate Institute of Biochemistry National Chung Hsing University Taichung 402202 Taiwan Institute of Biological Chemistry Academia Sinica Taipei 115201 Taiwan Department of Civil Engineering National Taiwan University Taipei 106319 Taiwan Department of Biomedical Engineering National Taiwan University Taipei 10663 Taiwan Life Science Group Scientific Research Division National Synchrotron Radiation Research Center Hsinchu 30092 Taiwan Institute of Molecular Biology National Chung Hsing University Taichung 402202 Taiwan Graduate Institute of Chinese Medical Science China Medical University Taichung 406040 Taiwan Department of Applied Mathematics Feng Chia University Taichung 407102 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung 402202 Taiwan School of Biomedical Sciences Faculty of Biological Sciences and the Astbury Centre for Structural Molecular Biology University of Leeds Leeds LS2 9JT United Kingdom Department of Life Science Tunghai University Taichung 407224 Taiwan Institute of Biochemical Sciences National Taiwan University Taipei 106319 Taiwan Ph.D Program in Translational Medicine National Chung Hsing University Taichung 402202 Taiwan

The K+ uptake system KtrAB is essential for bacterial survival in low K+ environments. The activity of KtrAB is regulated by nucleotides and Na+. Previous studies proposed a putative gating mechanism of KtrB regulated...

The K⁺ uptake system KtrAB is essential for bacterial survival in low K⁺ environments. The activity of KtrAB is regulated by nucleotides and Na⁺. Previous studies proposed a putative gating mechanism of KtrB regulated by KtrA upon binding to ATP or ADP. However, how Na⁺ activates KtrAB and the Na⁺ binding site remain unknown. Here we present the cryo-EM structures of ATP- and ADP-bound KtrAB from Bacillus subtilis (BsKtrAB) both solved at 2.8 Å. A cryo-EM density at the intra-dimer interface of ATP-KtrA was identified as Na⁺, as supported by X-ray crystallography and ICP-MS. Thermostability assays and functional studies demonstrated that Na⁺ binding stabilizes the ATP-bound BsKtrAB complex and enhances its K⁺ flux activity. Comparing ATP- and ADP-BsKtrAB structures suggests that BsKtrB Arg417 and Phe91 serve as a channel gate. The synergism of ATP and Na⁺ in activating BsKtrAB is likely applicable to Na⁺-activated K⁺ channels in central nervous system. © The Author(s) 2024.

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Mahakala: a Python-based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Space-times

arXiv

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arXiv 2023年

作者： Sharma, Aniket Medeiros, Lia Chan, Chi-Kwan Halevi, Goni Mullen, Patrick D. Stone, James M. Wong, George N. Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar PO Manauli Punjab Mohali140306 India School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States CCS-2 Los Alamos National Laboratory Los AlamosNM United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States

We introduce Mahakala, a Python-based, modular, radiative ray-tracing code for curved space-times. We employ Google’s JAX framework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr metrics. JAX also enables Mahakala to run in parallel on both CPUs and GPUs and achieve speeds comparable to C-based codes. Mahakala natively uses the Cartesian Kerr-Schild coordinate system, which avoids numerical issues caused by the "pole" of spherical coordinates. We demonstrate Mahakala’s capabilities by simulating the 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature of Mahakala allows us to easily quantify the relative contribution of different regions of the flow to image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole. We also quantify the relative contribution of the disk, forward jet, and counter jet to 1.3 mm images. Copyright © 2023, The Authors. All rights reserved.

关键词： Python

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The development and implementation of an innovative inquiry-based learning to build students’ higher order thinking skills in teaching of inorganic reactions

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AIP Conference Proceedings 2022年第1期2659卷

作者： Erna Helena Tampubolon Manihar Situmorang Ida Duma Riris 1Department of Chemistry Education Graduate Study Program (Program Pascasarjana) Universitas Negeri Medan Jl. Willem Iskandar Psr V Medan North Sumatra INDONESIA 20221 2Department of Physics Education Faculty of Teacher Training and Education Universitas HKBP Nommensen Jl. Sutomo No. 4A North Sumatra INDONESIA 3Department of Chemistry Faculty of Mathematics and Natural Science Universitas Negeri Medan Jl. Willem Iskandar Psr V Medan North Sumatra INDONESIA 20221

Higher order thinking skills are life skills that scientists need to have in solving problems related to scientific work. This study aims to develop an inquiry-based active learning model to build higher order thinking skills as well as to improve learning outcomes in teaching General Chemistry. This research was conducted at the Faculty of Teacher Training and Education, Universitas HKBP Nommensen, involving 50 undergraduate students who are studying General Chemistry. The research stages are carried out by providing inquiry-based innovative learning resources for Inorganic Chemical Reactions, followed by standardization and validation of teaching materials, and implementation of innovative learning resources for active learning.. The assessment of the inquiry task resulted in students’ activities is categorized good (M=64.11±5.48), Inquiry based learning has been able to build students’ higher-order thinking skills in good category (M=74.53±14.67). Students master the concept of chemical reactions well, which is indicated by the high average score on learning outcomes in the experimental group (M=75.16±15.48) is higher than in control group (M=60.16±15.07). Both groups are significantly different (ttest 3,082 >ttabel 1,386). The inquiry-based active learning model is relevant to be used in teaching General Chemistry during abnormal times of the Covid-19 pandemic and even better on normal times.

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Physicochemical quality of “Jamu” syrup with different ratios of amomum compactum and Syzygium aromaticum L.

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AIP Conference Proceedings 2023年第1期2634卷

作者： Mazarina Devi Budi Wibowotomo Muntholib Muntholib Desiana Merawati Ahmad Sulaeman Mansoor Abdul Hamid 1Department of Industrial Technology Faculty of Engineering Universitas Negeri Malang Jl. Semarang 5 Malang 65145 Indonesia 2Department of Chemistry Education Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang 5 Malang 65145 Indonesia 3Department of Sport Science Faculty of Sport Science Universitas Negeri Malang Jl. Semarang 5 Malang 65145 Indonesia 4Department of Community Nutrition Faculty of Human Ecology Institut Pertanian Bogor Bogor Indonesia 5Food Technology and Bioprocessing Program Universiti Malaysia Sabah Kinabalu Malaysia

Spices are indigenous plants flourishing well in Indonesia contain lots of bioactive compounds. It is potential to be developed as functional food products. The aims of this study are to examine the physicochemical properties of spice syrups composed of different ratios of cardamom and clove. This study employed an experimental design by identifying the effects of different ratios of cardamom and clove in spice syrup colored by teleng flowers (Clitoria ternatea). The collected data were then analyzed using the Independent Sample T-Test. Results showed that syrup with a higher ratio of cloves had a higher a- value (redness). Meanwhile, the L and b+ values were not different. The higher ratio of cloves used was also affected in accordance with chemical analysis results of beta-carotene, gallic acid, kaempferol, and quercetin that showed a higher value of syrup parameter. It can be summarized that using the ratio of cardamom and cloves has a significant effect on the physicochemical properties of spice syrup.

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Reservoir characterization using stochastic seismic inversion in “K” gas field, Bonaparte Basin

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AIP Conference Proceedings 2021年第1期2320卷

作者： Tri Wicaksono Abdul Haris Agus Riyanto Pradityo Riyadi 1Reservoir Geophysics Graduate Program Physics Department FMIPA Universitas Indonesia Depok Indonesia 2Department of Mathematics FMIPA Universitas Indonesia Depok Indonesia 3Pusat Penelitian dan Pengembangan Teknologi Minyak dan Gas Bumi LEMIGAS Jakarta 12230 Indonesia

The deterministic seismic inversion method has been successfully used in various projects in exploration and development. This method enables the interpreter to get a better understanding of subsurface by omitting the wavelet and tuning effects; therefore, quantitative reservoir properties can be generated. However, this method has significant limitations by generating average impedances of each layer, and the range of values is smaller than the impedance from the wells; therefore, the inversion will not produce results that are not within the calibration range. Stochastic seismic inversion is done by conditioning well data and reproducing spatially varying statistics using a variogram, which could overcome the deterministic limitation. This method generates multiple realizations of high-frequency elastic properties that are consistent with both seismic amplitude and well data. These two methods are applied in “K” gas field, which located in the offshore Bonaparte Basin, Eastern Indonesia. Geologically, the “K” field is located within a relatively undeformed Australian continental margin that extends into Indonesian waters. This field contains a significant gas column, reservoir within shallow marine, highly mature, quartzose sandstone of the Middle Jurassic Plover Formation. Potential targets in the area may be large folds, horst blocks, and tilted fault blocks trap in the Palaeozoic section. The application of stochastic seismic inversion showed significant benefits compared to deterministic, especially in “K” gas field, where the reservoirs are stacked sandstone with intra-formational shale. Some of the reservoir and all the intraformational shales are below seismic resolution. Stochastic seismic inversion is able to distinguish those features, and in addition, the inverted volumes with multiple realizations with ranking criteria for P10, P50, and P90 of impedance, and vp/vs could be utilized to reduce the risk associated with exploration plan and field developme

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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A novel synthesis of 1,1’-(2,4,6-trihydroxy-1,3-phenylene)bis(ethan-1-one) (DAPG) using CuSO4.5H2O as a green catalyst

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AIP Conference Proceedings 2020年第1期2237卷

作者： Carissa Hertiningtyas Triana Kusumaningsih Maulidan Firdaus 1Master of Chemistry Program Graduate School Sebelas Maret University Indonesia 2Department of Chemistry Faculty of Mathematics and Natural Sciences Sebelas Maret University Indonesia

1,1’-(2,4,6-trihydroxy-1,3-phenylene) bis (ethan-1-one) (DAPG) has been successfully synthesized through Friedel-Craft acylation reaction between phloroglucinol and acetic anhydride in ethyl acetate at room temperature with CuSO4.5H2O catalyst. The CuSO4.5H2O was an environmentally-friendly heterogeneous catalyst. These reactions were carried out in various variations weight of the catalyst, i.e. 20, 25, 30, 35, 40, and 45 mmol. This reaction could be completed in 8-23 hours and produced DAPG compound in good to excellent yields. This product was fully characterized by FT-IR spectroscopy, 1H-NMR, and 13C-NMR.

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Changes in electroencephalography complexity of acute Ischemic stroke: A fuzzy approximate entropy analysis

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AIP Conference Proceedings 2020年第1期2314卷

作者： Osmalina Nur Rahma Alfian Pramudita Putra Angga Bagus Prasetyo Riries Rulaningtyas Sastra Kusuma Wijaya 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Airlangga University Indonesia 2Department of Physics Faculty of Mathematics & Natural Science University of Indonesia Indonesia

Stroke is one of the leading causes of death in the world. The previous study had shown that EEG (Electroencephalogram) was a promising tool to detect stroke since it is non-invasive and cheap. So, the EEG analysis method needs to be more explored to give an accurate prognosis assessment. Previously, complexity analysis, such as Fuzzy Approximate Entropy (fApEn), had been successfully differentiated between control and patients with Alzheimer's Disease. Thus, this study used fApEn to analyse the changes of electroencephalography complexity in Acute Ischemic Stroke (AIS) during hyperventilation and photic stimulation, which are a general protocol to investigate the abnormality in EEG. Photic stimulation conducted using white light that flashed with frequency 5Hz, 10Hz, and 15Hz consecutively for three minutes and then continued by hyperventilation for three minutes, approximately. This study compared the fApEn value from healthy subjects with AIS patients. The result showed that patient with AIS tended to conduct the lower and higher fApEn value compared to the healthy subject. The lower value of fApEn occurred because of the presence of the slow-waves on the EEG, while the higher fApEn indicated the epileptiform activity during the recording. The increment of the slow-waves and the presence of epileptiform activity could be considered as the characteristic of stroke even though its appearance is uncertain. However, complexity analysis of EEG could be useful in differentiating the healthy subject and patient with stroke or classifying the severity level of the stroke to improve stroke management.

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Z-score standard growth chart design of toddler weight using least square spline semiparametric regression

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AIP Conference Proceedings 2021年第1期2329卷

作者： Nur Chamidah Budi Lestari Anies Y. Wulandari Lailatul Muniroh 1Department of Mathematics Faculty of Science and Technology Universitas Airlangga Indonesia 5Research Group of Statistical Modeling in Life Science Fac. Sci. and Tech. Universitas Airlangga Indonesia 2Department of Mathematics Fac. Math. and Natural Sci. Universitas Jember Indonesia 3Statistics Study Program Faculty of Science and Technology Universitas Airlangga Indonesia 4Department of Health Nutrition Faculty of Public Health Universitas Airlangga Indonesia

Underweight is a nutritional status of toddlers characterized by a lack of weight based on anthropometric index weight-for-age. In Indonesia, the anthropometric index is recorded on a KMS (Card Towards Health) which refers to WHO-2005 Standard Growth Chart (WHO-2005 SGC). The toddler samples used by WHO-2005 SGC, i.e., from Ghana, Oman, Norway, the USA, Brazil, and India, are physically different from the Indonesian toddlers. This research aims to design Z-Score Standard Growth Chart (Z-Score SGC) of weight-for-age based on sample toddlers from East Java by using least square spline estimator in semiparametric regression model. The obtained Z-Score SGC would be used to assess the nutritional status of toddlers in East Java. Results showed that the highest average weight gain was achieved at interval of age 0 ≤ t < 6 for about 0.7045 kg, and growth of toddler boys was higher than girls with a difference of 0.4 kg. Also, Z-Score SGC gave a lower reference than WHO-2005 SGC such that percentage of malnutrition and less-nutrition status categories provided by Z-Score SGC was also lower than those by WHO-2005 SGC.

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