Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images

Rodriguez-Vazquez, Javier; Fernandez-Cortizas, Miguel; Perez-Saura, David; Molina, Martín; Campoy, Pascual

doi:10.20944/preprints202302.0070.v1

Cited by 2 publications

(1 citation statement)

References 48 publications

(55 reference statements)

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“…2 Subsequently, other carbapenemase genes were discovered, including bla NDM , bla OXA-48 , bla VIM , and bla IMP. [3][4][5][6] Among them, KPC-2, one of class A carbapenemase, is relatively common in China. With the widespread popularity of carbapenem and wide distribution of carbapenemase genes, carbapenemresistant K. pneumoniae (CRKP) isolation rates are gradually increasing globally.…”

Section: Introductionmentioning

confidence: 99%

Epidemiological and Molecular Characteristics of blaNDM-1 and blaKPC-2 Co-Occurrence Carbapenem-Resistant Klebsiella pneumoniae

Fang¹,

Liu²,

Yang³

et al. 2023

IDR

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Carbapenem-resistant Klebsiella pneumoniae (CRKP) has emerged and spread worldwide. It can usually cause a serious threat complicating treatment options in clinical settings. However, treatment options are limited. The present study investigates the prevalence and genetic characteristics of bla NDM-1 and bla KPC-2 co-harboring clinical isolates of Klebsiella pneumoniae. Methods: In this study, Multiplex polymerase chain reaction (PCR) was performed to detect the carbapenem-resistant genes, and the broth microdilution method was used to determine the minimum inhibitory concentrations (MICs) of antibacterial drugs. The transferability of carbapenem-resistant phenotypes was examined using filter mating assays. Overall, we used Illumina sequencing to evaluate the epidemiological and molecular characteristics of bla NDM-1 and bla KPC-2 (genes encoding carbapenemase) co-occurrence in CRKP strains. Results: All strains exhibited resistance to carbapenems and other antibiotics. However, they were still susceptible to polymyxin E. Among them, 18 isolates were positive for bla KPC-2 , bla NDM-1 , and multiple virulence determinants, such as genes encoding the virulence factor aerobactin, yersiniabactin, and the regulator of the mucoid phenotype (rmpA and rmpA2). Whole genome sequencing revealed that the 18 CRKP strains belonged to ST11 and capsular serotype KL64, and could be grouped into two evolutionary branches. Furthermore, these strains displayed hypervirulence potential since all of them carried pLVPK-like plasmid. Conclusion: These findings suggested that ST11-KL64 CRKP strains are major threats in terms of nosocomial infections in this hospital. Hence, new strategies should be urgently developed to monitor, diagnose, and treat this high-risk CRKP clone.

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Section: Introductionmentioning

confidence: 99%

Epidemiological and Molecular Characteristics of blaNDM-1 and blaKPC-2 Co-Occurrence Carbapenem-Resistant Klebsiella pneumoniae

Fang¹,

Liu²,

Yang³

et al. 2023

IDR

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Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision

Hascoet,

Pellet,

Aires

et al. 2023

Remote Sensing

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Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a methodology for deriving monthly corrections to global E datasets at 0.25∘ resolution. A principled approach is proposed to firstly use indirect information from the other water components to correct E estimates at the catchment level, and secondly to extend this sparse catchment-level information to global pixel-level corrections using machine learning (ML). Several E satellite products are available, each with its own errors (both random and systematic). Four such global E datasets are used to validate the proposed approach and highlight its ability to extract seasonal and regional systematic biases. The resulting E corrections are shown to accurately generalize WC closure constraints to unseen catchments. With an average deviation of 14% from the original E datasets, the proposed method achieves up to 20% WC residual reduction on the most favorable dataset.

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Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images

Cited by 2 publications

References 48 publications

Epidemiological and Molecular Characteristics of blaNDM-1 and blaKPC-2 Co-Occurrence Carbapenem-Resistant Klebsiella pneumoniae

Epidemiological and Molecular Characteristics of blaNDM-1 and blaKPC-2 Co-Occurrence Carbapenem-Resistant Klebsiella pneumoniae

Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision

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