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PurposeWidely used conventional 2D T2 * approaches that are based on breath-held, electrocardiogram (ECG)-gated, multi-gradient-echo sequences are prone to motion artifacts in the presence of incomplete breath holding or arrhythmias, which is common in cardiac patients. To address these limitations, a 3D, non-ECG-gated, free-breathing T2 * technique that enables rapid whole-heart coverage was developed and validated.MethodsA continuous random Gaussian 3D k-space sampling was implemented using a low-rank tensor framework for motion-resolved 3D T2 * imaging. This approach was tested in healthy human volunteers and in swine before and after intravenous administration of ferumoxytol.ResultsSpatial-resolution matched T2 * images were acquired with 2-3-fold reduction in scan time using the proposed T2 * mapping approach relative to conventional T2 * mapping. Compared with the conventional approach, T2 * images acquired with the proposed method demonstrated reduced off-resonance and flow artifacts, leading to higher image quality and lower coefficient of variation in T2 *-weighted images of the myocardium of swine and humans. Mean myocardial T2 * values determined using the proposed and conventional approaches were highly correlated and showed minimal bias.ConclusionThe proposed non-ECG-gated, free-breathing, 3D T2 * imaging approach can be performed within 5 min or less. It can overcome critical image artifacts from undesirable cardiac and respiratory motion and bulk off-resonance shifts at the heart-lung interface. The proposed approach is expected to facilitate faster and improved cardiac T2 * mapping in those with limited breath-holding capacity or arrhythmias.

Introduction: Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method: Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results: The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84–0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion: Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.

Abstract This paper utilizes the theory of quantum diffusion to analyze the electron probability and spreading width of a wavepacket on each layer in a two-dimensional (2D) coupled system with edge disorder, aiming to clarify the effects of edge disorder on the stability of the electron periodic oscillations in 2D coupled systems. Using coupled 2D square lattices with edge disorder as an example, we show that, the electron probability and wavepacket spreading width exhibit periodic oscillations and damped oscillations, respectively, before and after the wavepacket reaches the boundary. Furthermore, these electron oscillations exhibit strong resistance against disorder perturbation with a longer decay time in the regime of large disorder, due to the combined influences of ordered and disordered site energies in the central and edge regions. Finally, we numerically verified the universality of the results through bilayer graphene, demonstrating that this anomalous quantum oscillatory behavior is independent of lattice geometry. Our findings are helpful in designing relevant quantum devices and understanding the influence of edge disorder on the stability of electron periodic oscillations in 2D coupled systems.

Abstract The K+ uptake system KtrAB is essential for bacterial survival in low K+ environments. The activity of KtrAB is regulated by nucleotides and Na+. Previous studies proposed a putative gating mechanism of KtrB regulated by KtrA upon binding to ATP or ADP. However, how Na+ activates KtrAB and the Na+ binding site remain unknown. Here we present the cryo-EM structures of ATP- and ADP-bound KtrAB from Bacillus subtilis (BsKtrAB) both solved at 2.8 Å. A cryo-EM density at the intra-dimer interface of ATP-KtrA was identified as Na+, as supported by X-ray crystallography and ICP-MS. Thermostability assays and functional studies demonstrated that Na+ binding stabilizes the ATP-bound BsKtrAB complex and enhances its K+ flux activity. Comparing ATP- and ADP-BsKtrAB structures suggests that BsKtrB Arg417 and Phe91 serve as a channel gate. The synergism of ATP and Na+ in activating BsKtrAB is likely applicable to Na+-activated K+ channels in central nervous system.

BackgroundMobile health technologies have been found to improve the self-management of chronic diseases. However, there is limited research regarding their feasibility in supporting recovery from substance use disorders (SUDs) in China. ObjectiveThe objective of this study was to examine the feasibility of a mobile phone-based ecological momentary assessment (EMA) app by testing the concordance of drug use assessed by the EMA, urine testing, and a life experience timeline (LET) assessment. MethodsA total of 75 participants dependent on heroin or amphetamine-type stimulant (ATS) in Shanghai were recruited to participate in a 4-week pilot study. Of the participants, 50 (67% [50/75]) were randomly assigned to the experimental group and 25 (33% [25/75]) were assigned to the control group. The experimental group used mobile health (mHealth) based EMA technology to assess their daily drug use in natural environments and received 2 short health messages each day, whereas the control group only received 2 short health messages each day from the app. Urine tests and LET assessments were conducted each week and a post-intervention survey was administered to both groups. The correlations among the EMA, the LET assessment, and the urine test were investigated. ResultsThe mean age of the participants was 41.6 (SD 8.0) years, and 71% (53/75) were male. During the 4 weeks of observation, 690 daily EMA survey data were recorded, with a response rate of 49.29% (690/1400). With respect to drug use, the percent of agreement between the EMA and the LET was 66.7%, 79.2%, 72.4%, and 85.8%, respectively, for each of the 4 weeks, whereas the percent of agreement between the EMA and the urine test was 51.2%, 65.1%, 61.9%, and 71.5%, respectively. The post-intervention survey indicated that 46% (32/70) of the participants preferred face-to-face interviews rather than the mHealth app. ConclusionsThis study demonstrated poor agreement between the EMA data and the LET and found that the acceptance of mHealth among individuals with SUDs in China was not positive. Hence, greater efforts are needed to improve the feasibility of mHealth in China.

Three species of the genus Sciota Hulst, 1888 are recognized from Northeast China: Sciota cynicella (Christoph, 1881), Sciota fumella (Eversmann, 1844), and Sciota adelphella (Fischer von R$\ddot{o} $ 수식 이미지slerstamm, 1836), of which Sciota cynicella (Christoph, 1881) is reported for the first time from China. This species can be distinguished from congeners by the gray color of basal area, the straight antemedial line and the distinct postmedial line on the forewing; by the stout aedeagus in male genitalia. In this study, a key to Northeastern Chinese species of genus Sciota is presented, and the illustrations of adults and genitalia are also provided.

[Objective] Industrial CO2 pipeline transportation typically operates in a supercritical/dense-phase high-pressure mode. In the event of a pipeline leakage, highly concentrated CO2 will be generated in the vicinity of the leakage port, presenting a significant risk to nearby persons and other creatures. [Methods] Currently, there is a research gap in quantitatively analyzing and evaluating the range of influence and safety distance related to CO2 pipeline leakage. To investigate the leakage and diffusion behavior of supercritical/dense-phase CO2 pipelines and determine safety distances for various CO2 exposure levels, a self-designed, large-scale CO2 pipeline relief and diffusion experimental facility was used to conduct CO2 pipeline relief experiments under varying pressure and temperature conditions, with the aim of examining the change pattern of CO2 concentration in the diffusion zone after relief and the CO2 influence range under typical volume fractions. [Results] Under the experimental pressures and temperatures, the volume fraction of CO2 near the relief outlet was predominantly influenced by jet action. Across all experimental conditions, the volume fraction of CO2 reached up to 70% at a distance of 5 m, while remaining below 8% at 10 m. The relief duration of CO2, with a volume fraction exceeding 4% at a distance of 5 m from the relief outlet,was calculated to be up to 125 s. Moreover, higher operating pressures in the pipe resulted in prolonged relief duration. The volume fraction of CO2 at a distant location from the relief outlet was influenced by turbulent diffusion and ambient wind conditions. For CO2 with a volume fraction of 4%, the maximum diffusion distance was 32 m at pressures ranging from 7.5 MPa to 10.5 MPa and 20 m at operating temperatures ranging from 20 ℃ to 40 ℃. Following the CO2 pipeline relief, the temperature in the diffusion zone decreased, and the temperature drop in the diffusion zone increased with higher initial pressure or lower initial temperature in the pipe. [Conclusion] The diffusion distance of CO2is influenced by both the operating pressure and the initial temperature in the pipe. Higher operating pressure or lower initial temperature will result in a greater diffusion distance. Additionally, the diffusion distance can also be influenced by ambient wind conditions. After the relief of an industrial CO2 pipeline, the hazardous area must be clearly demarcated, taking into account the operating status of the pipeline as well as a comprehensive evaluation of ambient wind speed and direction.

Abstract Interferon (IFN) exerts its effects through interferon-stimulated genes (ISGs), but its efficacy is limited by interferon resistance, which can be caused by the ubiquitination of key proteins. UBE2O was initially identified as a promising therapeutic target based on data from the TCGA and iUUCD 2.0 databases. Through the inhibition of UBE2O, interferon α/β signaling and overall interferon signaling were activated. Integrating data from proteomic, mass spectrometry, and survival analyses led to the identification of IFIT3, a mediator of interferon signaling, as a ubiquitination substrate of UBE2O. The results of in vitro and in vivo experiments demonstrated that the knockdown of UBE2O can enhance the efficacy of interferon-α by upregulating IFIT3 expression. K236 was identified as a ubiquitination site in IFIT3, and the results of rescue experiments confirmed that the effect of UBE2O on interferon-α sensitivity is dependent on IFIT3 activity. ATO treatment inhibited UBE2O and increased IFIT3 expression, thereby increasing the effectiveness of interferon-α. In conclusion, these findings suggest that UBE2O worsens the therapeutic effect of interferon-α by targeting IFIT3 for ubiquitination and degradation.

In the field of machine learning, hyperparameters are one of the key factors that affect prediction performance. Previous studies have shown that optimizing hyperparameters can improve the performance of inner-version defect prediction and cross-project defect prediction, but the impact on the performance of cross-version defect prediction is unclear. This paper chooses five classical defect prediction models (decision tree, K-nearest neighbors, random forests, support vector machine, and multi-layer perceptron) and four common hyperparameter optimization algorithms (Bayesian optimization based on TPE, Bayesian optimization based on SMAC, random search, and simulated annealing). An empirical study is conducted on PROMISE dataset to explore the influence of optimizing hyperparameters on the performance of cross-version defect prediction. The results indicate that: firstly, there is an obvious improvement in the AUC of cross-version defect prediction after optimizing the decision tree, K-nearest neighbors and multi-layer perceptron models; secondly, the optimal models still maintain the same stability as the default hyperparametric models; thirdly, hyperparameter optimization takes 1 to 2 minutes for all models on average except the complicated multi-layer perceptron model and it is feasible to optimize the hyperparameter of model in cross-version defect prediction experiment. The above results indicate that the hyperparameter optimization of the model should be considered in the process of cross-version defect prediction to improve its performance.

PURPOSE: To develop a free-breathing, non-electrocardiogram technique for simultaneous myocardial T1 , T2 , T2 *, and fat-fraction (FF) mapping in a single scan. METHODS: The MR Multitasking framework is adapted to quantify T1 , T2 , T2 *, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high-dimensional image is modeled as a low-rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques. RESULTS: In static and motion phantoms, T1 /T2 /T2 */FF measurements showed substantial consistency (R > 0.98) and excellent agreement (intraclass correlation coefficient > 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T1 , T2 , T2 *, and FF measurements that agreed with those from references. CONCLUSIONS: The proposed free-breathing, non-electrocardiogram, motion-resolved Multitasking technique allows simultaneous quantification of myocardial T1 , T2 , T2 *, and FF in a single 2.5-min scan.

PurposeTo perform fast 3D steady-state CEST (ss-CEST) imaging using MR Multitasking.MethodsA continuous acquisition sequence with repetitive ss-CEST modules was developed. Each ss-CEST module contains a single-lobe Gaussian saturation pulse, followed by a spoiler gradient and eight FLASH readouts (one "training line" + seven "imaging lines"). Three-dimensional Cartesian encoding was used for k-space acquisition. Reconstructed CEST images were quantified with four-pool Lorentzian fitting.ResultsSteady-state CEST with whole-brain coverage was performed in 5.6 s per saturation frequency offset at the spatial resolution of 1.7 × 1.7 × 3.0 mm3 . The total scan time was 5.5 min for 55 different frequency offsets. Quantitative CEST maps from multipool fitting showed consistent image quality across the volume.ConclusionThree-dimensional ss-CEST with whole-brain coverage can be done at 3 T within 5.5 min using MR Multitasking.