Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit

High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex and discontinuous nature of this problem pose significant challenges in both statistics and computation. In this paper, we propose a tensor block model and the computationally efficient methods, \emph{high-order Lloyd algorithm} (HLloyd), and \emph{high-order spectral clustering} (HSC), for high-order clustering. The convergence guarantees and statistical optimality are established for the proposed procedure under a mild sub-Gaussian noise assumption. Under the Gaussian tensor block model, we completely characterize the statistical-computational trade-off for achieving high-order exact clustering based on three different signal-to-noise ratio regimes. The analysis relies on new techniques of high-order spectral perturbation analysis and a "singular-value-gap-free" error bound in tensor estimation, which are substantially different from the matrix spectral analyses in the literature. Finally, we show the merits of the proposed procedures via extensive experiments on both synthetic and real datasets.Comment: 65 page

A comparative study of the proventricular structure in twenty Chinese Tettigoniidae (Orthoptera) species

This study focuses on the proventriculus and the alimentary canal of twenty Tettigoniidae species among three subfamilies, Tettigoniinae, Phaneropterinae and Conocephalinae. Each part of the alimentary canal and the inner structure of proventriculus were examined under optic microscope and scanning electron microscopy. As a result, the length of each part of the alimentary canal and the inner structure of proventriculus were highly associated with feeding habits. Carnivorous species always had a short foregut and long cilia on the base of the sclerotized appendix in proventriculus, whereas herbivorous species always had a longer foregut and a highly sclerotized proventriculus. These results increase understanding of the alimentary canal in Tettigoniidae and will be useful in future studies of their feeding habits

Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features

Graph neural networks (GNNs) with missing node features have recently received increasing interest. Such missing node features seriously hurt the performance of the existing GNNs. Some recent methods have been proposed to reconstruct the missing node features by the information propagation among nodes with known and unknown attributes. Although these methods have achieved superior performance, how to exactly exploit the complex data correlations among nodes to reconstruct missing node features is still a great challenge. To solve the above problem, we propose a self-supervised guided hypergraph feature propagation (SGHFP). Specifically, the feature hypergraph is first generated according to the node features with missing information. And then, the reconstructed node features produced by the previous iteration are fed to a two-layer GNNs to construct a pseudo-label hypergraph. Before each iteration, the constructed feature hypergraph and pseudo-label hypergraph are fused effectively, which can better preserve the higher-order data correlations among nodes. After then, we apply the fused hypergraph to the feature propagation for reconstructing missing features. Finally, the reconstructed node features by multi-iteration optimization are applied to the downstream semi-supervised classification task. Extensive experiments demonstrate that the proposed SGHFP outperforms the existing semi-supervised classification with missing node feature methods.Comment: Accepted by 48th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

Spatio-Temporal Evolution of Sandy Land and its Impact on Soil Wind Erosion in the Kubuqi Desert in Recent 30 Years

Continuous remote-sensing monitoring of sand in desert areas and the exploration of the spatio–temporal evolution characteristics of soil–wind erosion has an important scientific value for desertification prevention and ecological restoration. In this study, the Kubuqi Desert was selected as the study area, and the Landsat series satellite remote sensing data, supplemented by satellite remote sensing data such as GE images, SPOT-5, ZY-3, GF-1/2/6, etc., integrated object-oriented, decision tree, and auxiliary human–computer interaction interpretation methods, developed the Kubuqi Desert area dataset from 1990 to 2020, and established a soil erosion intensity database of the past 30 years based on the soil–wind erosion correction equation. The results show that the application of the training samples obtained by a high-score collaborative ground sampling to land use/cover classification in desert areas can effectively improve the efficiency of remote-sensing mapping of sand changes and the accuracy of change information identification, and the overall accuracy of the classification results is 95%. In general, the sandy area of the Kubuqi Desert area has decreased year by year, during which the mobile sand in the hinterland of the desert has expanded in a scattered distribution. The overall soil–wind erosion intensity showed a downward trend, especially since 2000; the ecological improvement trend after the implementation of desertification control projects is obvious. Changes in the sand type contributed the most to the reduction of soil–wind erosion intensity (contribution 81.14%), ecological restoration played a key role in reducing the soil–wind erosion intensity (contribution 14.42%), and the increase of forest and grass vegetation covers and agricultural oases played a positive role in solidifying the soil- and wind-proof sand fixation. The pattern of sandy land changes in desert areas is closely related to the national ecological civilization construction policy and the impact of climate change

Molecular mechanism of activation of human musk receptors OR5AN1 and OR1A1 by (R)-muscone and diverse other musk-smelling compounds

We acknowledge support from NSF (CHE-1265679) and NIH (5R01DC014423 subaward) (EB), NIH (5R01 DC014423) (HM), the European Reasearch Council (ERC) and the Engineering Science Research Council (EPSRC) (DO'H), FAPESP and CNPq (RAC), the Chinese Scholarship Council (CSC) for studentship support (MY), National Science Foundation (31070972) (HZ), Science and Technology Commission of Shanghai Municipality (16ZR1418300) (HZ), the Shanghai Eastern Scholar Program (J50201) (HZ). VSB thanks NIH grant 1R01GM106121-01A1 and computational time from NERSC.Understanding olfaction at the molecular level is challenging due to the lack of crystallographic models of odorant receptors (ORs). To better understand the molecular mechanism of OR activation, we focused on chiral (R)-muscone and other musk smelling odorants due to their great importance and widespread use in perfumery and traditional medicine, as well as environmental concerns associated with bioaccumulation of musks with estrogenic/antiestrogenic properties.  We experimentally and computationally examined the activation of human receptors OR5AN1 and OR1A1, recently identified as specifically responding to musk compounds.  OR5AN1 responds at nanomolar concentrations to musk ketone and robustly to macrocyclic sulfoxides and fluorine-substituted macrocyclic ketones; OR1A1 responds only to nitromusks. Structural models of OR5AN1 and OR1A1 based on quantum mechanics/molecular mechanics (QM/MM) hybrid methods were validated through direct comparisons with activation profiles from site-directed mutagenesis experiments and analysis of binding energies for 35 musk-related odorants.  The experimentally found chiral selectivity of OR5AN1 to (R)- over (S)-muscone was also computationally confirmed for muscone and fluorinated (R)-muscone analogs.  Structural models show that OR5AN1, highly responsive to nitromusks over macrocyclic musks, stabilizes odorants by hydrogen bonding to Tyr260 of transmembrane a-helix 6 and hydrophobic interactions with surrounding aromatic residues Phe105, Phe194 and, Phe207.  The binding of OR1A1 to nitromusks is stabilized by hydrogen bonding to Tyr258 along with hydrophobic interactions with surrounding aromatic residues Tyr251 and Phe206.  Hydrophobic/nonpolar and hydrogen bonding interactions contribute, respectively, 77% and 13% to the odorant binding affinities, as shown by an atom-based quantitative structure-activity relationship model.PostprintPeer reviewe

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

Suitability Evaluation of Urban Construction Land Based on an Approach of Vertical-Horizontal Processes

Suitability evaluation of urban construction land is critical for both urban master planning and the proper utilization of land resources. Using the Beihu New District of Jining City, China, as a case study, this paper introduces a novel research approach for comprehensive suitability evaluation based on vertical-horizontal processes. First, by considering both the land development potential and ecological constraint resistance, the potential-resistance (PR) model was developed and used to analyze the suitability for urban construction of vertical processes. Then, given the results of the vertical suitability analysis, the current urban built-up areas were selected as the sources of urban expansion, and the minimum cumulative resistance (MCR) model was applied to evaluate the suitability for urban development in terms of horizontal processes. The study area was regionalized into four categories—priority, suitable, restricted, and prohibited areas—which were defined based on the development threshold. The results showed that restricted and prohibited areas for urban construction occupied most of the study area. Totally, 648.51 km2 was categorized as restricted or prohibited, accounting for 12.89% and 54.75% of the total area, respectively. Priority and suitable areas for urban construction covered a total area of 310.37 km2, accounting for 16.55% and 15.81% of the total area, respectively. These areas were mainly distributed around urban centers and urban built-up areas. These findings reflect the substantial potential for future urban development and construction in the study area. The newly developed principles and methods of suitability evaluation for urban construction land presented in this paper provide more appropriate scales and spatial location for urban development and an ecological baseline for future urban growth

Effect of Land Use/Cover Change on Soil Wind Erosion in the Yellow River Basin since the 1990s

“Ecological conservation and high-quality development of the Yellow River Basin” is one of the fundamental national strategies related to national food security and ecological security in China. Evaluating the impact of land use/cover change (LUCC) on soil erosion is valuable to improving regional ecological environments and sustainable development. This study focused on the Yellow River Basin and used remote sensing data, the soil wind erosion modulus (SWEM) calculated with the revised wind erosion equation (RWEQ), to analyze the impact of regional scale LUCC from 1990 to 2018 on soil wind erosion. The main conclusions are as follows: (1) The total area of cultivated land, grass land, and unused land decreased, with a total reduction of 11,038.86 km²; total areas of forest land and built-up areas increased, increased by 2746.61 and 8356.77 km2, respectively, with differences within the region in these LUCC trends at different periods. From 1990 to 2000, the area of cultivated land increased by 1958.36 km2 and built-up land area increased by 1331.90 km2. The areas of forestland, grass land, water area, and unused land decreased. From 2000 to 2010, the area of cultivated land and grass land decreased by 4553.77 and 2351.39 km², respectively, whereas the areas of forestland and built-up land significantly increased. From 2010 to 2018, the area of cultivated land and grass land continued to decrease, and the area of built-up land continued to increase. (2) Since the 1990s, the SWEM has generally declined (Slope1990–2018 = −0.38 t/(ha·a)). Total amount of wind erosion in 2018 decreased by more than 50% compared with the amount in 1990. During this period, the intensity of wind erosion first increased and then decreased. In terms of the SWEM, 90.63% of the study area showed a decrease. (3) From 1990 to 2018, LUCC reduced the total amount of soil wind erosion by 15.57 million tons. From 1990 to 2000, the conversion of grass land/forest land to cultivated land and the expansion of desert resulted in a significant increase in soil wind erosion. From 2000 to 2018, the amount of soil wind erosion decreased at a rate of about 1.22 million tons/year, and the total amount of soil wind erosion decreased by 17.8921 million tons. During this period, the contribution rate of ecological programs (e.g., conversion of cultivated land to forest land and grass land, ecological engineering construction projects, etc.) to reduction of regional soil wind erosion was 59.13%, indicating that ecological programs have a positive role in reducing soil wind erosion intensity. The sustainable development of the ecological environment of the Yellow River Basin should be continued through strengthening ecological restoration and protection, to further consolidate gains made in this fragile ecosystem. This study provides scientific and technological support and relevant policy recommendations for the sustainable development of the Yellow River ecosystem under global change