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Spectrum - DOAJ

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Last Updated: 09 September 2022

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Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture.

Recent olfactory results on odorants have been correlated with their corresponding molecular characteristics. We can represent predicted mass spectrum as those of an odor mixture by using mass spectrum in the predictive model, and the mixing ratio can be determined. We show that the mass spectrum of apple flavor with enhanced 'fruit' and'sweet' impressions can be obtained using 59 and 60 molecules respectively using our analysis software.

Source link: https://doi.org/10.1371/journal.pone.0273011


Molecular characterization of extended-spectrum ß-lactamase producers, carbapenemase producers, polymyxin-resistant, and fosfomycin-resistant Enterobacterales among pigs from Egypt

Objects: To perform the first prospective surveillance evaluating the presence of genes encoding colistin resistance, fosfomycin resistance, carbapenemase, or extended-spectrum -lactamases in Enterobacterial isolates isolated from pigs' gut flora. Methods: In a slaughterhouse in Cairo, Egypt, 81 rectal swabs were collected from pigs between February and April 2020. Samples were tested for different resistance mechanisms using SuperPolymyxin, ChromID ESBL, SuperFOS, and SuperCarba specific agar plates. blaNDM-5 and blaOXA-244 have also been identified by us. In E. coli isolates, fosA3, fosA4, and fosA6 were identified. Notably, co-occurrence of ESBL genes and mcr or fosA genes was observed together, notably. Among ESBL and FosA manufacturers, IncFIB-type was most prevalent. The mcr-1 gene was carried on a variety of plasmids. Conclusion: These findings have raised serious public health concerns because Egyptian pig meat could act as a reservoir for antimicrobial resistance genes, triggering worldwide dissemination.

Source link: https://doi.org/10.1016/j.jgar.2022.05.022


Gastrointestinal colonization of extended-spectrum beta-lactamase-producing bacteria among children below five years of age hospitalized with fever in Dar es Salaam, Tanzania

To date, the association between ESBL-PE faecal carriage and the risk of subsequent ESBL-PE infection has not well established, and studies on the transmission of such pathogens in children with invasive infections such as bloodstream infections are yet to be investigated internationally. Methods: This cross-sectional study was conducted in Dar es Salaam, Tanzania, between March 2017 and July 2018. ChromID ESBL's Screen For ESBL-PE using selective media, we used rectal swabs to screen for ESBL-PE. The overall prevalence of ESBL-PE carriage in children 4 to 6 months old was 56%, and it was highest among children 4 to 6 months old. Compared to those without BSI, children with BSI had a higher ESBL-PE carriage than those without BSI. We discovered the blaCTX-M gene in 97% of all phenotypically detected ESBL-PE, with blaCTX-M gene in 97% of those; among those, blaCTX-M-15 was the dominant phenotypically detected ESBL-PE; among those, blaCTX-M-15 was the most prominent. In Tanzania, we observe a high prevalence of ESBL-PE faecal carriage in children with BSI.

Source link: https://doi.org/10.1016/j.jgar.2022.05.023


Expansion of the clinical and neuroimaging spectrum associated with NDUFS8‐related disorder

Diabetes ketoacidosis among one of the patients was characterized as autou2010antibody positive diabetic ketoacidosis. Brain MRIs in two of the three patients revealed widespread cerebral and cerebellar white matter involvement, including corticospinal tracts, but not notably, there was little evidence of deep gray matter structures. Our study expands the neuroimaging phenotype of NDUFS8-related disorder to include progressive leukodystrophy with increasing brainstem and cerebellar involvement, with increased bone sparing of the basal ganglia. This paper presents a comprehensive review of case presentation and progressive neuroimaging results of three patients from two unrelated families with identical pathogenic NDUFS8 variants, which expands the disease spectrum of NDUFS8-u2010-associated neurological disease.

Source link: https://doi.org/10.1002/jmd2.12303


Traffic Learning: A Deep Learning Approach for Obtaining Accurate Statistical Information of the Channel Traffic in Spectrum Sharing Systems

The channel traffic log's historical records have increasingly been used to make informed decisions in spectrum sharing schemes in recent years. Hence, we investigate extensively the methods in the literature that improve the estimation of the channel traffic statistics under ISS, specifically the closed-form expression strategy and the algorithmic reconstruction approach. Traffic Learning as a Deep Learning solution for accurate estimation of channel traffic data under ISS is introduced. For the estimation of several quantitative measures, deep neural networks using Multilayer Perceptron models are used for this novel approach.

Source link: https://doi.org/10.1109/ACCESS.2021.3109861


Multi-Dimensional Small-Scale Cooperative Spectrum Sensing Approach for Cognitive Radio Receivers

Cognitive radio is one of the most important new technologies that have been introduced to address future wireless networks' high data traffic. The use of MAE provides several interpretations of the received PU signal, which is considered as the backbone of many signal processing tasks, such as DOA estimation of PU signals. The estimated time difference of arrival between the output correlation signals coming from fast convolution blocks is based on estimation. The received PU signal versions are also used as inputs for separate MF detectors to get various decisions using the polar variety at the CR receiver antennas. Additionally, the estimated DOA of the PU signal is used to produce a combination of the signal variations emanating from each antenna branch's signal channels, as well as a high signal to noise ratio.

Source link: https://doi.org/10.1109/ACCESS.2021.3082870


Deep Reinforcement Learning Based Dynamic Spectrum Competition in Green Cognitive Virtualized Networks

This paper explores the most competitive spectrum for a virtual network operator in cognitive cellular networks with energy-harvesting base stations. Multiple cognitive virtual network operators obtain spectrum services from a mobile network operator through spectrum sensing and leasing in order to offer data services to their subscribers. Spectrum acquired by sensing is usually cheaper than traditional spectrum lease via long-term contract, but is unreliable due to the licensed users' unpredictable activities. We then create a deep reinforcement learning algorithm that uses deep neural networks as function approximators, so that the CVNO can know the right decision policy by interacting with the environment. The experiment results show that the new device will significantly raise the CVNO’s long-term performance relative to other learning and non-learning techniques.

Source link: https://doi.org/10.1109/ACCESS.2021.3069969

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions