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BACKGROUND: Metal implants may affect the image quality, iodine concentration (IC), and CT Hounsfield unit (HU) quantification accuracy. PURPOSE: To investigate the quantitative accuracy of IC and HU from dual-layer spectral detector (DLCT) in the presence of metal artifacts. MATERIAL AND METHODS: An experimental cylindrical phantom containing eight iodine inserts and two metal inserts was designed. The phantom underwent scanning at three radiation dose levels and two tube voltage settings. A set of conventional images (CIs), virtual monoenergetic images (VMIs), and iodine concentration maps (ICMs) were generated and measured for all the eight iodine inserts. Quantitative indicators of mean absolute percentage error (MAPE), artifact index (AI), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and standard deviation (SD) on CIs and VMIs were calculated for IC and HU. Subjective score evaluation was also conducted. RESULTS: The MAPEiodine values of all regions of interest across different scanning configurations were all <5%. Almost all APEiodine values were <5%, indicating that metal artifacts had little impact on IC measurements. When the tube voltage was fixed, the SD value of attenuation decreased with the increase of the tube current; this is also true when the tube current was fixed. The middle energy reconstructions seemed to give a good balance between reducing artifacts and improving contrast. CONCLUSION: VMIs from DLCT can reduce metal artifacts, the accuracy of IC quantification is not sensitive to imaging parameters. In summary, metal implants exhibit minimal impact on image quality and IC quantification accuracy in reconstructed images from DLCT.
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Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay.
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Ribosomal DNA (rDNA) is important in the nucleolus and nuclear organization of human cells. Defective rDNA repeat maintenance has been reported to be closely associated with neurological disorders, such as Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, depression, suicide, etc. However, there has not been a comprehensive review on the role of rDNA in these disorders. In this review, we have summarized the role of rDNA in major neurological disorders to sort out the correlation between rDNA and neurological diseases and provided insights for therapy with rDNA as a target.
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Accurate visualization of tumor microenvironment is of great significance for personalized medicine. Here, we develop a near-infrared (NIR) fluorescence/photoacoustic (FL/PA) dual-mode molecular probe (denoted as NIR-CE) for distinguishing tumors based on carboxylesterase (CE) level by an analyte-induced molecular transformation (AIMT) strategy. The recognition moiety for CE activity is the acetyl unit of NIR-CE, generating the pre-product, NIR-CE-OH, which undergoes spontaneous hydrogen atom exchange between the nitrogen atoms in the indole group and the phenol hydroxyl group, eventually transforming into NIR-CE-H. In cellular experiments and in vivo blind studies, the human hepatoma cells and tumors with high level of CE were successfully distinguished by both NIR FL and PA imaging. Our findings provide a new molecular imaging strategy for personalized treatment guidance.
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Carboxilesterasa , Medicina de Precisión , Humanos , Carboxilesterasa/metabolismo , Sondas Moleculares/química , Colorantes Fluorescentes/química , Imagen Óptica , AnimalesRESUMEN
Oxytocin (OXT), a neuropeptide consisting of only nine amino acids, is synthesized in the paraventricular and supraoptic nuclei of the hypothalamus. Although OXT is best known for its role in lactation and parturition, recent research has shown that it also has a significant impact on social behaviors in mammals. However, a comprehensive review of this topic is still lacking. In this paper, we systematically reviewed the effects of OXT on social behavior in mammals. These effects of OXT from the perspective of five key behavioral dimensions were summarized: parental behavior, anxiety, aggression, attachment, and empathy. To date, researchers have agreed that OXT plays a positive regulatory role in a wide range of social behaviors, but there have been controversially reported results. In this review, we have provided a detailed panorama of the role of OXT in social behavior and, for the first time, delved into the underlying regulatory mechanisms, which may help better understand the multifaceted role of OXT. Levels of OXT in previous human studies were also summarized to provide insights for diagnosis of mental disorders.
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Oxitocina , Conducta Social , Oxitocina/metabolismo , Oxitocina/fisiología , Animales , Humanos , Neuropéptidos/metabolismo , Mamíferos/metabolismo , Ansiedad/metabolismo , Ansiedad/psicología , Agresión/fisiología , Empatía/fisiología , Femenino , Conducta Materna/fisiologíaRESUMEN
Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epilepsy in hospital settings. However, clinical diagnosis of patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) is challenging due to the presence of similar abnormal discharges in EEG displays compared to other types of epilepsy (non-SeLECTS) patients. To assist the diagnostic process of epilepsy, a comprehensive classification study utilizing machine learning or deep learning techniques is proposed. In this study, clinical EEG was collected from 33 patients diagnosed with either SeLECTS or non-SeLECTS, aged between 3 and 11 years. In the realm of classical machine learning, sharp wave features (including upslope, downslope, and width at half maximum) were extracted from the EEG data. These features were then combined with the random forest (RF) and extreme random forest (ERF) classifiers to differentiate between SeLECTS and non-SeLECTS. Additionally, deep learning was employed by directly inputting the EEG data into a deep residual network (ResNet) for classification. The classification results were evaluated based on accuracy, F1-score, area under the curve (AUC), and area under the precision-recall curve (AUPRC). Following a 10-fold cross-validation, the ERF classifier achieved an accuracy of 73.15 % when utilizing sharp wave feature extraction for classification. The F1-score obtained was 0.72, while the AUC and AUPRC values were 0.75 and 0.63, respectively. On the other hand, the ResNet model achieved a classification accuracy of 90.49 %, with an F1-score of 0.90. The AUC and AUPRC values for ResNet were found to be 0.96 and 0.92, respectively. These results highlighted the significant potential of deep learning methods in SeLECTS classification research, owing to their high accuracy. Moreover, feature extraction-based methods demonstrated good reliability and could assist in identifying relevant biological features of SeLECTS within EEG data.
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Aprendizaje Profundo , Epilepsia , Humanos , Preescolar , Niño , Reproducibilidad de los Resultados , Epilepsia/diagnóstico , Electroencefalografía/métodos , Aprendizaje AutomáticoRESUMEN
A micro turbine engine's thrust relies on combustion chamber efficiency, closely tied to the design of its evaporation tube. This study thoroughly investigates evaporation and atomization processes within the tube, introducing a pioneering bionic-inspired structure. Integrating a honeycomb sheet into the traditional tube, both configurations undergo a comparative analysis. Results show a direct correlation between elevated air temperatures and reduced fuel droplet diameters, leading to increased fuel evaporation rates. The bionic tube, with a 1mm-thick honeycomb sheet, 0.6 mm aperture diameter, and 3 sheets, significantly improves fuel droplet atomization and evaporation compared to the conventional design. This research holds broader significance in understanding and enhancing micro turbine engine performance.
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Ageing and cell senescence of mesenchymal stem cells (MSCs) limited their immunomodulation properties and therapeutic application. We previously reported that nucleosome assembly protein 1-like 2 (Nap1l2) contributes to MSCs senescence and osteogenic differentiation. Here, we sought to evaluate whether Nap1l2 impairs the immunomodulatory properties of MSCs and find a way to rescue the deficient properties. We demonstrated that metformin could rescue the impaired migration properties and T cell regulation properties of OE-Nap1l2 BMSCs. Moreover, metformin could improve the impaired therapeutic efficacy of OE-Nap1l2 BMSCs in the treatment of colitis and experimental autoimmune encephalomyelitis in mice. Mechanistically, metformin was capable of upregulating the activation of AMPK, synthesis of l-arginine and expression of inducible nitric oxide synthase in OE-Nap1l2 BMSCs, leading to an increasing level of nitric oxide. This study indicated that Nap1l2 negatively regulated the immunomodulatory properties of BMSCs and that the impaired functions could be rescued by metformin pretreatment via metabolic reprogramming. This strategy might serve as a practical therapeutic option to rescue impaired MSCs functions for further application.
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Encefalomielitis Autoinmune Experimental , Inmunomodulación , Células Madre Mesenquimatosas , Metformina , Ratones Endogámicos C57BL , Células Madre Mesenquimatosas/metabolismo , Células Madre Mesenquimatosas/efectos de los fármacos , Células Madre Mesenquimatosas/citología , Animales , Metformina/farmacología , Ratones , Inmunomodulación/efectos de los fármacos , Encefalomielitis Autoinmune Experimental/tratamiento farmacológico , Encefalomielitis Autoinmune Experimental/metabolismo , Encefalomielitis Autoinmune Experimental/inmunología , Colitis/tratamiento farmacológico , Colitis/metabolismo , Óxido Nítrico Sintasa de Tipo II/metabolismo , Óxido Nítrico/metabolismo , Reprogramación Celular/efectos de los fármacos , Diferenciación Celular/efectos de los fármacos , Senescencia Celular/efectos de los fármacos , Células Cultivadas , Movimiento Celular/efectos de los fármacos , Células de la Médula Ósea/metabolismo , Células de la Médula Ósea/efectos de los fármacos , Células de la Médula Ósea/citología , Reprogramación MetabólicaRESUMEN
Background: Improvement on the updating equation of an algorithm is among the most improving techniques. Due to the lack of search ability, high computational complexity and poor operability of equilibrium optimizer (EO) in solving complex optimization problems, an improved EO is proposed in this article, namely the multi-strategy on updating synthetized EO (MS-EO). Method: Firstly, a simplified updating strategy is adopted in EO to improve operability and reduce computational complexity. Secondly, an information sharing strategy updates the concentrations in the early iterative stage using a dynamic tuning strategy in the simplified EO to form a simplified sharing EO (SS-EO) and enhance the exploration ability. Thirdly, a migration strategy and a golden section strategy are used for a golden particle updating to construct a Golden SS-EO (GS-EO) and improve the search ability. Finally, an elite learning strategy is implemented for the worst particle updating in the late stage to form MS-EO and strengthen the exploitation ability. The strategies are embedded into EO to balance between exploration and exploitation by giving full play to their respective advantages. Result and Finding: Experimental results on the complex functions from CEC2013 and CEC2017 test sets demonstrate that MS-EO outperforms EO and quite a few state-of-the-art algorithms in search ability, running speed and operability. The experimental results of feature selection on several datasets show that MS-EO also provides more advantages.
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As human-robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method's effectiveness through a series of experiments, evaluating the system's robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion.