10 research outputs found
CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation
We study the task of weakly-supervised point cloud semantic segmentation with
sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce
the expensive cost of dense annotations. Unfortunately, with extremely sparse
annotated points, it is very difficult to extract both contextual and object
information for scene understanding such as semantic segmentation. Motivated by
masked modeling (e.g., MAE) in image and video representation learning, we seek
to endow the power of masked modeling to learn contextual information from
sparsely-annotated points. However, directly applying MAE to 3D point clouds
with sparse annotations may fail to work. First, it is nontrivial to
effectively mask out the informative visual context from 3D point clouds.
Second, how to fully exploit the sparse annotations for context modeling
remains an open question. In this paper, we propose a simple yet effective
Contextual Point Cloud Modeling (CPCM) method that consists of two parts: a
region-wise masking (RegionMask) strategy and a contextual masked training
(CMT) method. Specifically, RegionMask masks the point cloud continuously in
geometric space to construct a meaningful masked prediction task for subsequent
context learning. CMT disentangles the learning of supervised segmentation and
unsupervised masked context prediction for effectively learning the very
limited labeled points and mass unlabeled points, respectively. Extensive
experiments on the widely-tested ScanNet V2 and S3DIS benchmarks demonstrate
the superiority of CPCM over the state-of-the-art.Comment: Accepted by ICCV 202
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Discovery of a Novel Hypervirulent Acinetobacter baumannii Strain in a Case of Community-Acquired Pneumonia.
PurposeAcinetobacter baumannii is associated with both hospital-acquired infections and community-acquired pneumonia (CAP). Here, we describe a novel strain of A. baumannii in a case of CAP in a previously healthy rural villager from Central Eastern China.Materials and methodsA. baumannii isolated from the patient (LS01) was compared to well-characterized pathogenic strain (AB5075), nosocomial circulating strain in China (ZJ06), and wild-type strain (ATCC17978). Growth rate studies were conducted under different environmental stressors, and virulence studies were performed using Galleria mellonella larvae. Whole genome sequencing (WGS) was performed using MinIon and MiSeq. Center for Genomic Epidemiology, CLCbio, Geneious, and Virulence Factors of Pathogenic Bacteria database were used for genomic analysis.ResultsLS01 grew significantly faster at 37°C and 42°C and in the presence of zinc compared to other strains. LS01 was more virulent in G. mellonella, killing all larvae within 8 h. Although WGS revealed 44 virulence genes, these genes were also present in the other strains. While two chromosomally encoded β-lactamases were identified, there were no plasmids identified and LS01 was pan-susceptible to all antibiotics tested. Phylogenetic analysis revealed that the closest related strains were only 72.552% identical, supporting a novel strain.ConclusionLS01 is a novel strain of hypervirulent yet pan-drug susceptible A. baumannii isolated from a patient with no prior hospitalizations, sick contacts, or any of the typical risk factors. This raises concerns for an emerging pathogen, and more epidemiological studies should be conducted to assess the prevalence of this A. baumannii strain
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Retrospective Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Symptomatic Patients Prior to Widespread Diagnostic Testing in Southern California.
BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused one of the worst pandemics in recent history. Few reports have revealed that SARS-CoV-2 was spreading in the United States as early as the end of January. In this study, we aimed to determine if SARS-CoV-2 had been circulating in the Los Angeles (LA) area at a time when access to diagnostic testing for coronavirus disease 2019 (COVID-19) was severely limited.MethodsWe used a pooling strategy to look for SARS-CoV-2 in remnant respiratory samples submitted for regular respiratory pathogen testing from symptomatic patients from November 2019 to early March 2020. We then performed sequencing on the positive samples.ResultsWe detected SARS-CoV-2 in 7 specimens from 6 patients, dating back to mid-January. The earliest positive patient, with a sample collected on January 13, 2020 had no relevant travel history but did have a sibling with similar symptoms. Sequencing of these SARS-CoV-2 genomes revealed that the virus was introduced into the LA area from both domestic and international sources as early as January.ConclusionsWe present strong evidence of community spread of SARS-CoV-2 in the LA area well before widespread diagnostic testing was being performed in early 2020. These genomic data demonstrate that SARS-CoV-2 was being introduced into Los Angeles County from both international and domestic sources in January 2020