43 research outputs found
On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization
In this paper, we propose a general procedure for establishing the geometric
landscape connections of a Riemannian optimization problem under the embedded
and quotient geometries. By applying the general procedure to the fixed-rank
positive semidefinite (PSD) and general matrix optimization, we establish an
exact Riemannian gradient connection under two geometries at every point on the
manifold and sandwich inequalities between the spectra of Riemannian Hessians
at Riemannian first-order stationary points (FOSPs). These results immediately
imply an equivalence on the sets of Riemannian FOSPs, Riemannian second-order
stationary points (SOSPs), and strict saddles of fixed-rank matrix optimization
under the embedded and the quotient geometries. To the best of our knowledge,
this is the first geometric landscape connection between the embedded and the
quotient geometries for fixed-rank matrix optimization and it provides a
concrete example of how these two geometries are connected in Riemannian
optimization. In addition, the effects of the Riemannian metric and quotient
structure on the landscape connection are discussed. We also observe an
algorithmic connection between two geometries with some specific Riemannian
metrics in fixed-rank matrix optimization: there is an equivalence between
gradient flows under two geometries with shared spectra of Riemannian Hessians.
A number of novel ideas and technical ingredients including a unified treatment
for different Riemannian metrics, novel metrics for the Stiefel manifold, and
new horizontal space representations under quotient geometries are developed to
obtain our results. The results in this paper deepen our understanding of
geometric and algorithmic connections of Riemannian optimization under
different Riemannian geometries and provide a few new theoretical insights to
unanswered questions in the literature
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence
In this paper, we propose a new {\it \underline{R}ecursive} {\it
\underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it
\underline{R}ank} constrained least squares {\it \underline{O}ptimization}
(RISRO). As its name suggests, the algorithm is based on a new sketching
framework, recursive importance sketching. Several existing algorithms in the
literature can be reinterpreted under the new sketching framework and RISRO
offers clear advantages over them. RISRO is easy to implement and
computationally efficient, where the core procedure in each iteration is only
solving a dimension reduced least squares problem. Different from numerous
existing algorithms with locally geometric convergence rate, we establish the
local quadratic-linear and quadratic rate of convergence for RISRO under some
mild conditions. In addition, we discover a deep connection of RISRO to
Riemannian manifold optimization on fixed rank matrices. The effectiveness of
RISRO is demonstrated in two applications in machine learning and statistics:
low-rank matrix trace regression and phase retrieval. Simulation studies
demonstrate the superior numerical performance of RISRO
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
Mask Propagation for Efficient Video Semantic Segmentation
Video Semantic Segmentation (VSS) involves assigning a semantic label to each
pixel in a video sequence. Prior work in this field has demonstrated promising
results by extending image semantic segmentation models to exploit temporal
relationships across video frames; however, these approaches often incur
significant computational costs. In this paper, we propose an efficient mask
propagation framework for VSS, called MPVSS. Our approach first employs a
strong query-based image segmentor on sparse key frames to generate accurate
binary masks and class predictions. We then design a flow estimation module
utilizing the learned queries to generate a set of segment-aware flow maps,
each associated with a mask prediction from the key frame. Finally, the
mask-flow pairs are warped to serve as the mask predictions for the non-key
frames. By reusing predictions from key frames, we circumvent the need to
process a large volume of video frames individually with resource-intensive
segmentors, alleviating temporal redundancy and significantly reducing
computational costs. Extensive experiments on VSPW and Cityscapes demonstrate
that our mask propagation framework achieves SOTA accuracy and efficiency
trade-offs. For instance, our best model with Swin-L backbone outperforms the
SOTA MRCFA using MiT-B5 by 4.0% mIoU, requiring only 26% FLOPs on the VSPW
dataset. Moreover, our framework reduces up to 4x FLOPs compared to the
per-frame Mask2Former baseline with only up to 2% mIoU degradation on the
Cityscapes validation set. Code is available at
https://github.com/ziplab/MPVSS.Comment: NeurIPS 202
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. KovacË icÂŽ 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 739â8530 Japan; 28ImmunoGen,
830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College,
ul. Michalowskiego 12, 31â126 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, 363â883 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, 363â700, 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, 5â1 Higashiyama, Myodaiji, Okazaki 444â8787 Japan; 46Graduate School of
Pharmaceutical Sciences, Nagoya City University, 3â1 Tanabe-dori, Mizuhoku, Nagoya 467â8603 Japan; 47Medical & Biological Laboratories
Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464â0858 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 158â8501 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 20â22 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; 60Childrenâs GMP LLC, St. Jude Childrenâs
Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1â5 Muromati 1-Chome, Nishiku,
Kobe, 651â2241 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; 70HorvaÂŽ 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 Tuš 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
Authorâs ChoiceâFinal 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: 11â30, 2020. DOI: 10.1074/mcp.RA119.001677.L
Numerical Investigation of Multistage Fractured Horizontal Wells considering Multiphase Flow and Geomechanical Effects
Hydraulic fracturing is a key technology in unconventional reservoir production, yet many simulators only consider the single-phase flow of shale gas, ignoring the two-phase flow process caused by the retained fracturing fluid in the early stage of production. In this study, a three-dimensional fluidâgasâsolid coupling reservoir model is proposed, and the governing equations which involve the early injection water phenomenon and stress-sensitive characteristics of shale gas reservoirs are established. The finite elementâfinite difference method was used for discretisation of stress and strain equations and the equations of flow balances. Further, a sensitivity analysis was conducted to analyse fracture deformation changes in the production. Fracture characteristics under different rock mechanics coefficients were simulated, and the influence of rock mechanics parameters on productivity was further characterised. The stimulated reservoir volume zone permeability could determine the retrofitting effect, the permeability increased from 0.02 to 0.1âmD, and cumulative gas production increased from 18.08 to 26.42 million m3, thus showing an increase of 8.34 million m3, or 46%. The effect of Youngâs modulus on the yield was smaller than Poissonâs ratio and the width and length of the fractures. Production was most sensitive to the length of the fractures. The length of the fracture increased from 200 to 400âm, and the cumulative gas production increased from 26.44 to 38.34 million m3, showing an increase of 11.9 million m3, or 45%. This study deepens the understanding of the production process of shale gas reservoirs and has significance for the fluidâgasâsolid coupling of shale gas reservoirs
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The Social Evaluation of the Interval River ofShanghai Houtan Wetland Park
Carbapenem-Resistant Klebsiella pneumoniae Infections among ICU Admission Patients in Central China: Prevalence and Prediction Model
Objective. To investigate the prevalence of infections due to carbapenem-resistant Klebsiella pneumoniae (CRKP) among ICU admission patients in central China and develop a reliable prediction model. Methods. Five hundred and seven consecutive ICU admission patients with Klebsiella pneumoniae (KP) infection were enrolled in this retrospective multicenter case-control study from January 2014 to June 2018. The prevalence and antimicrobial susceptibility pattern were analyzed. Multivariate analysis was performed by logistic regression modeling to determine the risk factors. A prediction model was developed and verified using data from six hospitals in central China. Results. Of the total 507 isolates of KP, 244 (48.1%) strains were carbapenem resistant. The majority of these isolates were from sputum (30.9%) and blood (20.9%) samples. Tigecycline had good activity against CRKP (95.5%). The most common sequence type (ST) of CRKP was ST11 (84.4%), and 98.6% of them had the blaKPC-2 antimicrobial resistance gene. Thirteen variables were identified as independent risk factors for CRKP infection, including KP colonization or infection in the preceding year (OR=3.32, 95% CI 2.01-4.38), CD4/CD8 ratio <1 (OR=2.98, 95% CI 2.02-4.19), and parenteral nutrition â©Ÿ48 h (OR=1.88, 95% CI 1.22-3.04). The model developed to predict CRKP infection was effective, with an area under the receiver-operating characteristic curve of 0.854 (95% CI 0.821-0.884, p<0.001). Conclusions. ST11 carrying the blaKPC-2 antimicrobial resistance gene was the most common type of CRKP among the ICU admission patients in central China. The model demonstrated excellent predictive performance and exhibited good discrimination
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The Social Evaluation of the Interval River ofShanghai Houtan Wetland Park
The complete mitochondrial genome of Hybos grossipes (Linnaeus, 1767) (Diptera: Empididae)
The genus Hybos Meigen, 1803 belongs to the subfamily Hybotinae of the family Empididae. Here we report a mitogenome of Hybos grossipes (Linnaeus, 1767) as the new representative of the subfamily Hybotinae. The complete mitogenome is 16,325âbp in total, consisting of 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes, and a control region. The nucleotide composition is biased toward A and T, accounting for 77.2% of the total. All PCGs start with ATN codons except COI, NAD1, and NAD5, and end with TAA or incomplete stop codon T. The phylogenetic result generated by IQ-Tree based on 13 PGCs showed that the subfamily Hybotinae is monophyletic, and the subfamily Hybotinae is a sister group of the subfamily Ocydromiinae