A Resonance Model for Spontaneous Cortical Activity

How human brain function emerges from structure has intrigued researchers for decades and numerous models have been put forward, yet none of them yields a close structure-function relation. Here we present a resonance model based on neuronal spike timing dependent plasticity (STDP) principle to describe the spontaneous cortical activity by incorporating the dynamic interactions between neuronal populations into a wave equation, which is able to accurately predict the resting brain functional connectivity (FC), including the resting-state networks. Besides, the proposed model provides strong theoretical and experimental evidences that the spontaneous dynamic coupling between brain regions fluctuates with a low frequency. Crucially, it is able to account for how the negative functional correlations emerge during resonance. We test the model with a large cohort of subjects (1038) from the Human Connectome Project (HCP) S1200 release in both time and frequency domain, which exhibits superior performance to existing eigen-decomposition models

Dimethyl Sulfoxide Damages Mitochondrial Integrity and Membrane Potential in Cultured Astrocytes

Dimethyl sulfoxide (DMSO) is a polar organic solvent that is used to dissolve neuroprotective or neurotoxic agents in neuroscience research. However, DMSO itself also has pharmacological and pathological effects on the nervous system. Astrocytes play a central role in maintaining brain homeostasis, but the effect and mechanism of DMSO on astrocytes has not been studied. The present study showed that exposure of astrocyte cultures to 1% DMSO for 24 h did not significantly affect cell survival, but decreased cell viability and glial glutamate transporter expression, and caused mitochondrial swelling, membrane potential impairment and reactive oxygen species production, and subsequent cytochrome c release and caspase-3 activation. DMSO at concentrations of 5% significantly inhibited cell variability and promoted apoptosis of astrocytes, accompanied with more severe mitochondrial damage. These results suggest that mitochondrial impairment is a primary event in DMSO-induced astrocyte toxicity. The potential cytotoxic effects on astrocytes need to be carefully considered during investigating neuroprotective or neurotoxic effects of hydrophobic agents dissolved by DMSO

Study on different potato continuous cropping ways on rhizosphere soil nutrients and enzyme activities

To address the problem of food security, China produced potatoes as a staple food in 2015.However, there are increasing problems with continuous cropping production methods, potato continuous cropping has been inevitable.So it is necessary to research under the different potato continuous cropping ways, potato rhizosphere soil nutrients and enzyme activities which can direct potato fertilizer and ease potato continuous cropping obstacle. A two-growing season investigation was carried out during the spring and autumn of 2014 and 2015 to determine the different ways of potato continuous cropping on the overall growth of potatoes, soil nutrients, and enzyme activities. During continuous cropping nitrogen (N) content of rhizosphere soil was reduced; available potassium (Kav) was significantly reduced(p≤5%), especially in spring and autumn continuous cropping; and total phosphorus (Ptot) was reduced during the growth stage. However, the total potassium (Ktot), available phosphorus(Pav), and organic carbon (Ctot) increased before they decreased. For rhizosphere soil enzyme activities, urease initially increased and then decreased, and was lower in continuous cropping than multiple continuous cropping; in spring of 2015, invertase was the highest with continuous cropping. Catalase and polyphenol oxidase decreased initially before increasing. Continuous cropping in spring and autumn consumed more nutrients, especially potassium (K) than in spring. Therefore, potatoes planted in both spring and autumn enhanced the problems of continuous cropping. However, multiple continuous cropping that eased rhizosphere soil nutrient absorption and effectively improves soil nutrients and enzyme activities could provide an effective method for managing the negative impacts associated with continuous cropping

Transcriptional Regulation of PP2A-Aα Is Mediated by Multiple Factors Including AP-2α, CREB, ETS-1, and SP-1

Protein phosphatases-2A (PP-2A) is a major serine/threonine phosphatase and accounts for more than 50% serine/threonine phosphatase activity in eukaryotes. The holoenzyme of PP-2A consists of the scaffold A subunit, the catalytic C subunit and the regulatory B subunit. The scaffold subunits, PP2A-Aα/β, provide a platform for both C and B subunits to bind, thus playing a crucial role in providing specific PP-2A activity. Mutation of the two genes encoding PP2A-Aα/β leads to carcinogenesis and likely other human diseases. Regulation of these genes by various factors, both extracellular and intracellular, remains largely unknown. In the present study, we have conducted functional dissection of the promoter of the mouse PP2A-Aα gene. Our results demonstrate that the proximal promoter of the mouse PP2A-Aα gene contains numerous cis-elements for the binding of CREB, ETS-1, AP-2α, SP-1 besides the putative TFIIB binding site (BRE) and the downstream promoter element (DPE). Gel mobility shifting assays revealed that CREB, ETS-1, AP-2α, and SP-1 all bind to PP2A-Aα gene promoter. In vitro mutagenesis and reporter gene activity assays reveal that while SP-1 displays negative regulation, CREB, ETS-1 and AP-2Aα all positively regulate the promoter of the PP2A-Aα gene. ChIP assays further confirm that all the above transcription factors participate the regulation of PP2A-Aα gene promoter. Together, our results reveal that multiple transcription factors regulate the PP2A-Aα gene

NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals because it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy-six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submit- Avenue, Silver Spring, Maryland 20993; 22Glycoscience Research Laboratory, Genos, Borongajska cesta 83h, 10 000 Zagreb, Croatia; 23Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovacˇ ic´ a 1, 10 000 Zagreb, Croatia; 24Department of Chemistry, Georgia State University, 100 Piedmont Avenue, Atlanta, Georgia 30303; 25glyXera GmbH, Brenneckestrasse 20 * ZENIT / 39120 Magdeburg, Germany; 26Health Products and Foods Branch, Health Canada, AL 2201E, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9 Canada; 27Graduate School of Advanced Sciences of Matter, Hiroshima University, 1-3-1 Kagamiyama Higashi-Hiroshima 739–8530 Japan; 28ImmunoGen, 830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College, ul. Michalowskiego 12, 31–126 Krakow, Poland; 30Department of Pathology, Johns Hopkins University, 400 N. Broadway Street Baltimore, Maryland 21287; 31Mass Spec Core Facility, KBI Biopharma, 1101 Hamlin Road Durham, North Carolina 27704; 32Division of Mass Spectrometry, Korea Basic Science Institute, 162 YeonGuDanji-Ro, Ochang-eup, Cheongwon-gu, Cheongju Chungbuk, 363–883 Korea (South); 33Advanced Therapy Products Research Division, Korea National Institute of Food and Drug Safety, 187 Osongsaengmyeong 2-ro Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, 363–700, Korea (South); 34Center for Proteomics and Metabolomics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; 35Ludger Limited, Culham Science Centre, Abingdon, Oxfordshire, OX14 3EB, United Kingdom; 36Biomolecular Discovery and Design Research Centre and ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), Macquarie University, North Ryde, Australia; 37Proteomics, Central European Institute for Technology, Masaryk University, Kamenice 5, A26, 625 00 BRNO, Czech Republic; 38Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; 39Department of Biomolecular Sciences, Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany; 40AstraZeneca, Granta Park, Cambridgeshire, CB21 6GH United Kingdom; 41Merck, 2015 Galloping Hill Rd, Kenilworth, New Jersey 07033; 42Analytical R&D, MilliporeSigma, 2909 Laclede Ave. St. Louis, Missouri 63103; 43MS Bioworks, LLC, 3950 Varsity Drive Ann Arbor, Michigan 48108; 44MSD, Molenstraat 110, 5342 CC Oss, The Netherlands; 45Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5–1 Higashiyama, Myodaiji, Okazaki 444–8787 Japan; 46Graduate School of Pharmaceutical Sciences, Nagoya City University, 3–1 Tanabe-dori, Mizuhoku, Nagoya 467–8603 Japan; 47Medical & Biological Laboratories Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464–0858 Japan; 48National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Potters Bar, Hertfordshire EN6 3QG United Kingdom; 49Division of Biological Chemistry & Biologicals, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158–8501 Japan; 50New England Biolabs, Inc., 240 County Road, Ipswich, Massachusetts 01938; 51New York University, 100 Washington Square East New York City, New York 10003; 52Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom; 53GlycoScience Group, The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland; 54Department of Chemistry, North Carolina State University, 2620 Yarborough Drive Raleigh, North Carolina 27695; 55Pantheon, 201 College Road East Princeton, New Jersey 08540; 56Pfizer Inc., 1 Burtt Road Andover, Massachusetts 01810; 57Proteodynamics, ZI La Varenne 20–22 rue Henri et Gilberte Goudier 63200 RIOM, France; 58ProZyme, Inc., 3832 Bay Center Place Hayward, California 94545; 59Koichi Tanaka Mass Spectrometry Research Laboratory, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho Nakagyo-ku, Kyoto, 604 8511 Japan; 60Children’s GMP LLC, St. Jude Children’s Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1–5 Muromati 1-Chome, Nishiku, Kobe, 651–2241 Japan; 62Synthon Biopharmaceuticals, Microweg 22 P.O. Box 7071, 6503 GN Nijmegen, The Netherlands; 63Takeda Pharmaceuticals International Co., 40 Landsdowne Street Cambridge, Massachusetts 02139; 64Department of Chemistry and Biochemistry, Texas Tech University, 2500 Broadway, Lubbock, Texas 79409; 65Thermo Fisher Scientific, 1214 Oakmead Parkway Sunnyvale, California 94085; 66United States Pharmacopeia India Pvt. Ltd. IKP Knowledge Park, Genome Valley, Shamirpet, Turkapally Village, Medchal District, Hyderabad 500 101 Telangana, India; 67Alberta Glycomics Centre, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 68Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 69Department of Chemistry, University of California, One Shields Ave, Davis, California 95616; 70Horva´ th Csaba Memorial Laboratory for Bioseparation Sciences, Research Center for Molecular Medicine, Doctoral School of Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Egyetem ter 1, Hungary; 71Translational Glycomics Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprem, Egyetem ut 10, Hungary; 72Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; 73Proteomics Core Facility, University of Gothenburg, Medicinaregatan 1G SE 41390 Gothenburg, Sweden; 74Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Institute of Biomedicine, Sahlgrenska Academy, Medicinaregatan 9A, Box 440, 405 30, Gothenburg, Sweden; 75Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy at the University of Gothenburg, Bruna Straket 16, 41345 Gothenburg, Sweden; 76Department of Chemistry, University of Hamburg, Martin Luther King Pl. 6 20146 Hamburg, Germany; 77Department of Chemistry, University of Manitoba, 144 Dysart Road, Winnipeg, Manitoba, Canada R3T 2N2; 78Laboratory of Mass Spectrometry of Interactions and Systems, University of Strasbourg, UMR Unistra-CNRS 7140, France; 79Natural and Medical Sciences Institute, University of Tu¨ bingen, Markwiesenstrae 55, 72770 Reutlingen, Germany; 80Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; 81Division of Bioanalytical Chemistry, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; 82Department of Chemistry, Waters Corporation, 34 Maple Street Milford, Massachusetts 01757; 83Zoetis, 333 Portage St. Kalamazoo, Michigan 49007 Author’s Choice—Final version open access under the terms of the Creative Commons CC-BY license. Received July 24, 2019, and in revised form, August 26, 2019 Published, MCP Papers in Press, October 7, 2019, DOI 10.1074/mcp.RA119.001677 ER: NISTmAb Glycosylation Interlaboratory Study 12 Molecular & Cellular Proteomics 19.1 Downloaded from https://www.mcponline.org by guest on January 20, 2020 ted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide communityderived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods. Molecular & Cellular Proteomics 19: 11–30, 2020. DOI: 10.1074/mcp.RA119.001677.L

Si doping superlattice structure on 6H-SiC(0001)

Si-DSL structures multilayers are prepared on 6H-SiC(0001) successfully. The energy offsets of the n-Si/n-6H-SiC heterojunction in the conduction band and valance band are 0.21eV and 1.65eV, respectively. TEM characterizations of the p/n-Si DSL confirms the epitaxial growth of the Si films with [1-11] preferred orientation and the misfit dislocations with a Burgers vector of 1/3 at the p-Si/n-Si interface. J-V measurements indicate that the heterostructure has apparent rectifying behavior. Under visible illumination with light intensity of 0.6W/cm2, the heterostructure demonstrates significant photoelectric response, and the photocurrent density is 2.1mA/cm2. Non-UV operation of the SiC-based photoelectric device is realized

Research on the Effect of Planting Density on Rice Yield and Quality

Planting density is of great significance in adjusting the population structure of rice, increasing yield and reducing the cost. And suitable planting density can not only bring the yield potential of rice population into full play, obtain the maximum grain yield per unit area, but save labor, protect the environment and improve rice quality. This article summarizes the impact of planting density on rice growth, yield, the components as well as qualities at an attempt to provide theoretical guidance for high yield and quality cultivation of rice

The effect of controllable train-tail devices on the longitudinal impulse of the combined trains under initial braking

Abstract The 20,000-ton combined train running has greatly promoted China’s heavy-haul railway transportation capability. The application of controllable train-tail devices could improve the braking wave of the train and braking synchronism, and alleviate longitudinal impulse. However, the characteristics of the controllable train-tail device such as exhaust area, exhaust duration and exhaust action time are not uniform in practice, and their effects on the longitudinal impulse of the train are not apparent, which is worth studying. In this work, according to the formation of the Datong–Qinhuangdao Railway, the train air brake and longitudinal dynamics simulation system (TABLDSS) is applied to establish a 20,000-ton combined train model with the controllable train-tail device, and the braking characteristics and the longitudinal impulse of the train are calculated synchronously with changing the air exhaust time, exhaust area, and action lag time under initial braking. The results show that the maximum coupler force of the combined train will decrease with the extension of the continuous exhaust time, while the total exhaust time of the controllable train-tail device remains unchanged; the maximum coupler force of the combined train reduces by 32.5% with the exhaust area increasing from 70% to 140%; when the lag time between the controllable train-tail device and the master locomotive is more than 1.5 s, the maximum coupler force of the train increases along with the time difference enlargement

An improved YOLOv3-tiny method for fire detection in the construction industry

To prevent fire accidents on construction site and improve the accuracy of fire detection, an improved YOLOv3-tiny method (I-YOLOv3-tiny) is proposed in this paper. Although the YOLOv3-tiny has a fast detection speed and low equipment requirement, the accuracy is relatively low on fire detection. The improvement of the I-YOLOv3-tiny method is followed by three steps. Firstly, the feature extraction of fire images is enhanced by optimizing the network structure. Secondly, a multi-scale fusion is used to improve the detection effect of fire targets. Finally, the anchor boxes that are suitable for fire data sets are selected by k-means clustering. The results show that I-YOLOv3-tiny has an increased percentage of 4 on the mAP, the Recall rate has an increased percentage of 4, and AVG IOU has an increased percentage of 6. The proposed model meets the real-time performance of fire detection. This study is of theoretical and practical significance on fire safety management and accident prevention in the construction industry