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Abstract Investigating proton transport at the interface in an excited state facilitates the mechanistic investigation and utilization of nanomaterials. However, there is a lack of suitable tools for in-situ and interfacial analysis. Here we addresses this gap by in-situ observing the proton transport of graphene quantum dots (GQDs) in an excited state through reduction of magnetic resonance relaxation time. Experimental results, utilizing 0.1 mT ultra-low-field nuclear magnetic resonance relaxometry compatible with a light source, reveal the light-induced proton dissociation and acidity of GQDs’ microenvironment in the excited state (Hammett acidity function: –13.40). Theoretical calculations demonstrate significant acidity enhancement in –OH functionalized GQDs with light induction ( $${{\mathrm{p}}}{K}_{{\text{a}}}^{*}$$ p K a * = –4.62, stronger than that of H2SO4). Simulations highlight the contributions of edge and phenolic –OH groups to proton dissociation. The light-induced superacidic microenvironment of GQDs benefits functionalization and improves the catalytic performances of GQDs. Importantly, this work advances the understanding of interfacial properties of light-induced sp 2–sp 3 carbon nanostructure and provides a valuable tool for exploring catalyst interfaces in photocatalysis.

Summary: Background: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. Methods: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists’ detection performance. Findings: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p 0.99), and it detected 2 cases that had been missed by radiologists. Interpretation: The developed DL model can accurately detect colorectal cancer and improve radiologists’ detection performance, showing its potential as an effective computer-aided detection tool. Funding: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

Abstract Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Starting from the information feedback of cybernetics theory, mathematical statistics methods have powerful application and interpretation functions in sports events to solve practical problems of football player evaluation.

Abstract Background Combining the multimodal imaging and synergistic treatment in one platform can enhance the therapeutic efficacy and diagnosis accuracy. Results In this contribution, innovative Mn-doped Prussian blue nanoparticles (MnPB NPs) were prepared via microemulsion method. MnPB NPs demonstrated excellent T1 and T2 weighted magnetic resonance imaging (MRI) enhancement in vitro and in vivo. The robust absorbance in the near infrared range of MnPB NPs provides high antitumor efficacy for photothermal therapy (PTT) and photoacoustics imaging property. Moreover, with the doping of Mn, MnPB NPs exhibited excellent Fenton reaction activity for chemodynamic therapy (CDT). The favorable trimodal imaging and Fenton reaction enhanced mild temperature photothermal therapy in vitro and in vivo were further confirmed that MnPB NPs have significant positive effectiveness for integration of diagnosis and treatment tumor. Conclusions Overall, this Mn doped Prussian blue nanoplatform with multimodal imaging and chemodynamic/mild temperature photothermal co-therapy provides a reliable tool for tumor treatment. Graphical Abstract

To estimate the direction of arrival (DOA) of a linear frequency modulation (LFM) signal in a low signal-to-noise ratio (SNR) hydroacoustic environment by a small aperture array, a novel deconvolved beamforming method based on fractional Fourier domain delay-and-sum beamforming (FrFB) was proposed. Fractional Fourier transform (FrFT) was used to convert the received signal into the fractional Fourier domain, and delay-and-sum beamforming was subsequently performed. Noise resistance was acquired by focusing the energy of the LFM signal distributed in the time–frequency domain. Then, according to the convolution structure of the FrFB complex output, the influence of the fractional Fourier domain complex beam pattern was removed by deconvolution, and the target spatial distribution was restored. Therefore, an improved spatial resolution of DOA estimation was obtained without increasing the array aperture. The simulation and experimental results show that, with a small aperture array at low SNR, the proposed method possesses higher spatial resolution than FrFB and frequency-domain deconvolved conventional beamforming.

The exploitation and utilization of seabed sediments provide vital significance in many fields. Recently, the classification of seabed sediments using sub-bottom profiler(SBP) data has become a research focus. Specifically, SBP data can be applied not only for recognizing individual stratigraphic layers but also for classifying seabed sediments by inversion models. To improve the sorting effect of gravel and mud simultaneously, we propose a sediment classification method based on aback propagation neural network (BPNN) with the Biot-Stoll model and an attenuation-based model. In this method, two datasets of the mean grain size derived from these two models are trained through the BPNN classifier to classify seabed sediments. The proposed method is verified through SBP data and in-situ sampling data collected from the sea north of the Shandong Peninsula, China. The experimental results show that the overall accuracy of sediment classification is 89.4%, and the classification accuracy of gravel and mud reach 91.4% and 93.3%, respectively, confirming that gravel and mud can be more accurately distinguished based on the proposed method than the single Biot-Stoll model and the single attenuation-based model.

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide, and the WHO declared it a pandemic on March 11, 2020. Clinical characteristics and epidemiology features of patients infected with SARS-CoV-2 have been explored in the previous study. However, little is known about the combinative association of liver dysfunction and abnormal interleukins (ILs) in severe patients with COVID-19. This study was designed to estimate whether liver dysfunction and abnormal ILs could predict the severity of COVID-19. This study integrated liver function data and ILs data in patients with COVID-19 and found that liver injury and two ILs, interleukin-2 receptor (IL-2R) and interleukin-6 (IL-6), were closely related to the prognosis of patients with COVID-19. This study may give more exact information to clinicians about the prognosis of patients with COVID-19. In addition, this correlational study between liver disorder and ILs may provide a new vision to diagnosis and treatment in patients.

Low field (LF) nuclear magnetic resonance (NMR) shows potential advantages to study pure heteronuclear J-coupling and observe the fine structure of matter. Power-line harmonics interferences and fixed-frequency noise peaks might introduce discrete noise peaks into the LF-NMR spectrum in an open environment or in a conductively shielded room, which might disturb J-coupling spectra of matter recorded at LF. In this paper, we describe a multi-channel sensor configuration of superconducting quantum interference devices, and measure the multiple peaks of the 2,2,2-trifluoroethanol J-coupling spectrum. For the case of low signal to noise ratio (SNR) < 1, we suggest two noise suppression algorithms using discrete wavelet analysis (DWA), combined with either least squares method (LSM) or gradient descent (GD). The de-noising methods are based on spatial correlation of the interferences among the superconducting sensors, and are experimentally demonstrated. The DWA-LSM algorithm shows a significant effect in the noise reduction and recovers SNR > 1 for most of the signal peaks. The DWA-GD algorithm improves the SNR further, but takes more computational time. Depending on whether the accuracy or the speed of the de-noising process is more important in LF-NMR applications, the choice of algorithm should be made.

The flow inside the orifices in diesel injector largely determines the injection characteristics, which further affects the performance of the diesel engine. In this paper, numerical simulation is conducted to characterize the fuel flow with characteristics of two-phase cavitation, high speed, high pressure, high turbulence intensity, and high viscosity in two crucial orifice locations. The OZ orifice is selected as a benchmark to select the appropriate turbulence model and cavitation model. The simulation results from different turbulence and cavitation models are analyzed, and the combination of the realizable k–ε turbulence model and the Zwart–Gerber–Belamri cavitation model proves to be superior. Then, based on the validated models, the compressibility and non-isothermal characteristics of the fuel and two geometric parameters of the nozzles in an injector are emphatically discussed to depict the cavitation flow. The results suggest that the compressibility and non-isothermal characteristics of fuel affect the mass flow rate, fuel flow velocity, and cavitation extent within the orifices with a maximum deviation of the mass flow rate of 7.1%. However, their influence on the orifice flow coefficient is relatively minor with a maximum deviation of 0.8%. The mass flow rate and flow coefficient are both positively correlated with the rounded edge radius and taper coefficient. A greater rounded edge radius and taper coefficient can lead to a better fuel flow capacity in the nozzle orifice. [ABSTRACT FROM AUTHOR]