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ObjectivesTo investigate the genetic characteristics and transmission mechanism of the NDM-1-, IMP-4-, and SHV-12-producing multidrug-resistant (MDR) clinical isolate, Citrobacter freundii BC73.MethodsC. freundii BC73 was isolated from a urine specimen of a urological patient diagnosed with bladder cancer at a Chinese teaching hospital. Antimicrobial susceptibility testing was carried out using DL-120E susceptibility cards and DL-96A system. Whole genome sequencing (WGS) of the isolate was performed using the Illumina and Oxford Nanopore platforms to analyze the genetic context of drug resistance genes and plasmid characteristics. The phylogenetic tree was constructed and visualized by KSNP3.0 software and iTOL5.0 online database.ResultsC. freundii isolate BC73 co-carrying blaNDM-1, blaIMP-4 and blaSHV-12 were multidrug-resistant. blaNDM-1 and blaIMP-4 were located on a novel IncFIB-like plasmid, pCFBC1, and an IncN-IncU hybrid plasmid, pCFBC2, respectively. The transferability of blaNDM-1 and blaIMP-4 from C. freundii BC73 to E. coli J53 was successfully demonstrated. The genetic context of the blaNDM-1 and blaIMP-4 genes were ISCR27-groEL-∆groES-cutA-dsbD-trpF-bleMBL-blaNDM-1-∆ISAba125-IS3000 and intI1-blaIMP-4-Kl.pn.13-mobC-IS6100, respectively. Additionally, two extensive transposition units (MGE1 in pCFBC1, MGE2 in pCFBC2) were identified and numerous antimicrobial resistance genes were discovered on it.ConclusionTo our knowledge, our study represents the first characterization of a ST22 C. freundii isolate co-harboring blaNDM-1, blaIMP-4, and blaSHV-12, obtained from a urine sample. The dissemination of this MDR isolate should be of close concern in future clinical surveillance.

The environment pollution and petroleum problems, which are increasingly becoming serious, have caused the vehicle industry to transition into a low-carbon and energy-saving industry. During processes, plug-in fuel-cell electric vehicles (PFCEVs) play an important role due to their advantages of rapid fueling, high energy density and efficiency, low operating temperature, and zero onboard emissions. PFCEVs use high-capacity rechargeable batteries to avoid working in low-efficiency areas. However, a robust energy management strategy that can achieve reliable energy distribution by regulating the output power of the fuel cell and battery within the hybrid powertrain merits further investigation. Considering the close relationship between the driving cycle, state of charge (SOC), equivalent factor, and hydrogen consumption, a trip distance adaptive equivalent consumption minimum strategy integrating driving cycle prediction is proposed. A backpropagation-based neural network is used to predict short-term vehicle velocity and analyze future changes in vehicle demand power. Planning a path to the destination with the help of the global positioning system, the intelligent transportation system can also obtain traffic flow information for the entire trip. The equivalent factor is dynamically corrected in real time using the driving distance and SOC to realize the adaptability of the energy management strategy. Finally, the velocity prediction sequence is combined with the objective function. The sequential quadratic programming algorithm is used to optimize the equivalent hydrogen consumption of the objective function and to obtain the distributed power of the fuel cell and battery. The vehicle simulation model is built and compared with a traditional energy management strategy based on MATLAB/Simulink software. The simulation results show that the driving cycle prediction algorithm based on the backpropagation-based neural network predicts future short-term conditions better, with a 12.5% higher accuracy than the Markov method. The proposed energy management strategy allows the fuel cell to operate in high-efficiency areas. The hydrogen consumption is 55.6% less than that of the CD/CS strategy under the UDDS cycle. The hardware in the loop experiment verifies a hydrogen consumption that is 26.8% less than that of the CD/CS strategy under the EUDC cycle. The numerical validation results demonstrate the superior performance of the proposed strategy in terms of hydrogen consumption over the CD/CS strategy. The effectiveness of the proposed strategy is validated by hardware during the loop experiment.

The open nature of the wireless channel makes the drone communication vulnerable to adverse spoofing attacks, and the radio frequency fingerprint (RFF) identification is promising in effectively safeguarding the access security for drones. Since drones are constantly flying in the three dimensional aerial space, the unique RFF identification problem emerges in drone communication that the effective extraction and identification of RFF suffer from the time-varying channel effects and unavoidable jitterings due to the constant flight. To tackle this issue, we propose augmenting the training RFF dataset by regenerating the drone channel characteristics and compensate the fractional frequency offset. The proposed method estimates the Rician K value of the channel and curve-fits the statistical distribution, the Rician channels are regenerated using the sinusoidal superposition method. Then, a probabilistic switching channel is also set up to introduce the Rayleigh channel effects into the training dataset. The proposed method effectively addresses the unilateral channel effects in the training dataset and achieves the balanced channel effect distribution. Consequently, the pre-trained model can extract channel-robust RFF features in drone air-ground channels. In addition, by compensating the fractional frequency offset, the proposed method removes the unstable frequency components and retains the stable integer frequency offset. Then, the stable frequency offset features that are robust to environmental changes can be extracted. The proposed method achieves an average classification accuracy of 97% under spatial and temporal varying conditions.

Abstract Background Obesity has been confirmed to be associated with infertility. However, the association between metabolically healthy obesity (MHO), a subset of obesity with no metabolic abnormalities, and female infertility has not yet been investigated. This study aimed to examine the association between MHO and the risk of female infertility among United States. Methods This study utilized a cross-sectional design and included 3542 women aged 20–45 years who were selected from the National Health and Nutrition Examination Survey (NHANES) 2013–2020 database. The association between MHO and the risk of infertility was evaluated using risk factor–adjusted logistic regression models. Results Higher BMI and WC were associated with increased infertility risk after adjusting for potential confounding factors (OR (95% CI): 1.04(1.02, 1.06), P = 0.001; OR (95% CI): 1.02 (1.01, 1.03), P

Background: Altered epigenetic reprogramming and events contribute to breast cancer (Bca) progression and metastasis. How the epigenetic histone demethylases modulate breast cancer progression remains poorly defined. We aimed to elucidate the biological roles of KDM4A in driving Notch1 activation and Bca progression. Methods: The KDM4A expression in Bca specimens was analyzed using quantitative PCR and immunohistochemical assays. The biological roles of KDM4A were evaluated using wound-healing assays and an in vivo metastasis model. The Chromatin Immunoprecipitation (ChIP)-qPCR assay was used to determine the role of KDM4A in Notch1 regulation. Results: Here, we screened that targeting KDM4A could induce notable cell growth suppression. KDM4A is required for the growth and progression of Bca cells. High KDM4A enhances tumor migration abilities and in vivo lung metastasis. Bioinformatic analysis suggested that KDM4A was highly expressed in tumors and high KDM4A correlates with poor survival outcomes. KDM4A activates Notch1 expressions via directly binding to the promoters and demethylating H3K9me3 modifications. KDM4A inhibition reduces expressions of a list of Notch1 downstream targets, and ectopic expressions of ICN1 could restore the corresponding levels. KDM4A relies on Notch1 signaling to maintain cell growth, migration and self-renewal capacities. Lastly, we divided a panel of cell lines into KDM4Ahigh and KDM4Alow groups. Targeting Notch1 using specific LY3039478 could efficiently suppress cell growth and colony formation abilities of KDM4Ahigh Bca. Conclusion: Taken together, KDM4A could drive Bca progression via triggering the activation of Notch1 pathway by decreasing H3K9me3 levels, highlighting a promising therapeutic target for Bca.

In the era of the 6G cellular networks,the base station (BS) should be able to provide seamless connectivity for a large number of Internet of things (IoT) devices distributed in various areas.However,constrained by their locations,some IoT devices may possess unreliable communication channels to the BS due to the obstacles between them.It is a big obstacle on the roadmap towards the massively connected IoT networks in the era of 6G .To tackle this challenge,the possibility was studied to employ the newly emerging technique of intelligent reflecting surface (IRS) for enhancing the strength of the IoT devices’ signals at the BS.Specifically,the transmit power minimization problem in an uplink IoT communication system was focused on,where the overhead for channel estimation was taken into account.On one hand,for the case where the BS was equipped with a small or moderate number of antennas,an iterative algorithm was proposed to jointly optimize the user power allocation,the IRS reflecting coefficients,and the BS beamforming vectors.On the other hand,for the case where the BS had a massive number of antennas,i.e.,massive multiple-input multiple-output (MIMO),a low-complexity design of the user power allocation,the IRS reflecting coefficients,and the BS beamforming vectors was provided in a closed-form.Numerical results validate the great potential of IRS for enhancing massive connectivity in the future 6G-assisted IoT networks.

Abstract Background Perkinsosis, a disease caused by the protist Perkinsus, is responsible for mass mortalities of many molluscan species worldwide. The rapid, early and accurate detection of Perkinsus infection is necessary to react to outbreaks, and manage disease transmission. Current methods for diagnosis of Perkinsus spp. are time-consuming or require professional equipment and experienced personnel, rendering them unsuitable for field application. Recombinase polymerase amplification (RPA) assay is a highly sensitive and selective isothermal amplification technique that operates at temperatures of 37–42 °C, requires minimal sample preparation, and is capable of amplifying as low as 1–10 target DNA copies in less than 20 minutes. Methods We report a novel RPA assay that amplifies the internal transcriber spacer (ITS) region of P. beihaiensis, which, followed by rapid detection of amplicons using a lateral flow (LF) strip, enables easy visualization of results by the naked eye. Results The LF-RPA assay successfully amplified P. beihaiensis DNA using a set of primers of 20–25 bp in length. After incubation at 37 °C for 25 min, results were read within 5 min by the naked eye on a lateral flow strip. Our LF-RPA assay was comparably sensitive to qPCR assay, and capable of detecting as few as 26 copies of P. beihaiensis DNA. Cross-amplification occurred with other two Perkinsus species, P. olseni and P. chesapeaki, but not with other potential pathogen taxa in culture environments. We compared the performance of LF-RPA, conventional PCR and qPCR assays on 60 oyster samples. While LF-RPA assay results were 86.2% as sensitive, 77.4% as specific, and generally in agreement with those of conventional PCR results, they were more (93.3%) sensitive, (86.7%) specific, and agreed better with qPCR assay results. Future research should focus on developing simple DNA extraction methods that do not require professional laboratories and complicated extraction procedures, to facilitate application of this LF-RPA assay in the field. Conclusions Our LF-RPA assay provides a rapid and efficient method for detecting species of Perkinsus. This novel assay has potential to be used in field applications.

Shiyiwei Shenqi Tablet (SSTs) has been widely used for treatment of different types of cancer including breast cancer. SST has drawn more and more interest due to the low rate of side effects. The aim of this study was to investigate the metabolites in serums of breast cancer patients who received base-line chemotherapy only or combination treatment with SST. An untargeted metabolomics method was developed to investigate the alteration of metabolism in patients’ serums using ultra-high-performance liquid chromatography/Q-exactive Orbitrap mass spectrometry. The patients were separated based on the metabolomics data, and further analyses showed that SST treatment can affect the metabolism of glucose, fatty acid, bile acid and amino acid. In particular, SST treatment significantly reduced some short peptides which are potential tumor neoantigens. This study may provide novel insights into the mechanism underlying interaction between SST and base-line chemotherapy in terms of affecting metabolic pathways and thereby changing metabolic products, which might shed new light for clinical medication.