Roles of direct and indirect photodegradation in the photochemical fates of three 3rd generation fluoroquinolones.

Fluoroquinolones (FQs) are widely prescribed antibiotics that are commonly detected in aquatic environments, but the persistence, fates, and ecotoxicities of new generation FQs have yet to be fully investigated. We investigated the direct and indirect (hydroxyl radical (·OH), singlet oxygen (O21), and excited stated of organic matter (3CDOM*)) photodegradation of three 3rd generation FQs, moxifloxacin (MOX), gatifloxacin (GAT), and sparfloxacin (SPAR). The photodegradation rates and photolytic quantum yields (ΦFQ) of the FQs depended on their dissociation species at different pH in a range of 1×10-4 to 1×10-3 M mol-photon-1. Unlike MOX and GAT whose zwitterions had the highest ΦFQ, the anionic form of SPAR had the highest ΦFQ. The k·OH,FQ values were in the order of: k·OH,SPAR > k·OH,GAT ≈ k·OH,MOX with the 1010M-1s-1 order of magnitude. The kO21,FQ values were in the order of: kO21,SPAR (∼108M-1s-1) > kO21,MOX (∼107M-1s-1) > >> kO21,GAT (insignificant). Higher kLC*3,FQ values were observed for MOX (109 to 1010M-1s-1) compared to GAT and SPAR (108 to 109M-1s-1). The zwitterions had the highest reactivities with ·OH and the lowest reactivities with O21 and 3CDOM*. Reactions with ·OH enhanced the formation of transformation products (TPs) from decarboxylation and sidechain oxidation pathways, whereas reactions with O21 and 3CDOM* enhanced the formation of TPs from sidechain oxidation pathways. Some of the TPs were predicted to exhibit aquatic ecotoxicity and environmental persistence. The half-lives of the FQs were estimated to be 0.42 to 0.67 h for MOX and SPAR, and 4.6 to 4.9 h for GAT. Their half-lives and main photochemical fates depended on the surface water pH and water column depth. These results highlight the key roles that photodegradation plays in removing new generation FQs from aquatic environments, though this might lead to the formation of TPs that are harmful to aquatic ecosystems.

RT-CBAM: Refined Transformer Combined with Convolutional Block Attention Module for Underwater Image Restoration.

Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global receptive field and adaptability. Although transformers excel in capturing global features, they lag behind CNNs in handling fine local features, especially when dealing with underwater images containing complex and delicate structures. In order to tackle this challenge, we propose a refined transformer model by improving the feature blocks (dilated transformer block) to more accurately compute attention weights, enhancing the capture of both local and global features. Subsequently, a self-supervised method (a local and global blind-patch network) is embedded in the bottleneck layer, which can aggregate local and global information to enhance detail recovery and improve texture restoration quality. Additionally, we introduce a multi-scale convolutional block attention module (MSCBAM) to connect encoder and decoder features; this module enhances the feature representation of color channels, aiding in the restoration of color information in images. We plan to deploy this deep learning model onto the sensors of underwater robots for real-world underwater image-processing and ocean exploration tasks. Our model is named the refined transformer combined with convolutional block attention module (RT-CBAM). This study compares two traditional methods and six deep learning methods, and our approach achieved the best results in terms of detail processing and color restoration.

Photoproduction of reactive intermediates from dissolved organic matter in coastal seawater around an urban metropolis in South China: Characterization and predictive modeling.

Chromophoric dissolved organic matter (CDOM) is an important photochemical precursor to reactive intermediates (RIs) (e.g., excited triplet states of chromophoric dissolved organic matter (3CDOM⁎), hydroxyl radicals (·OH), and singlet oxygen (1O2)) in aquatic systems to drive the photodegradation of contaminants. There have been limited studies on the photoproduction of RIs in coastal seawater CDOM in Asia, which impedes our ability to model the lifetimes and fates of contaminants in these coastal seawater systems. Hong Kong is an urban metropolis in South China, whose coastal seawater is susceptible to anthropogenic activities from the surrounding areas and the nearby Pearl River. We investigated the photoproduction of RIs in seawater around Hong Kong during the wet vs. dry season. Higher intensities of fluorescent components, dissolved organic carbon concentration ([DOC]), apparent quantum yields of RIs (ΦRIs), and steady-state concentrations of photogenerated RIs ([RIs]ss) were observed for samples collected in the areas closest to the Pearl River during the wet season. Lower humification degrees and ΦRIs but higher intensities of fluorescent components and [RIs]ss were generally observed for the wet season samples compared to the dry season samples. Statistical analysis revealed strong significant correlations (Spearman |r| > 0.6, p < 0.05) between ΦRIs and the absorbance properties (including the absorbance ratio E2:E3, spectral slope coefficients S350-400, and spectral slope ratio SR) of CDOM, and between [RIs]ss and the quantity-reflected properties (including the fluorescence intensity of humic-like components) of CDOM. Our modeling analyses combining orthogonal partial least squares and stepwise multiple linear regression showed excellent prediction strengths for [1O2]ss and [3CDOM⁎]ss (R2adj > 0.7) when [DOC] and the chemical and optical properties of CDOM were used as predictor variables. These modeling results demonstrate the feasibility of predicting the concentrations and quantum yields of RIs in seawater around Hong Kong, and potentially other coastal cities in South China, from easily measurable chemical and optical properties.

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer.

BACKGROUND: Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients. METHODS: We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated. RESULTS: The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

Bombyx mori Ecdysone Receptor B1 May Inhibit BmNPV Infection by Triggering Apoptosis.

Bombyx mori nucleopolyhedrovirus (BmNPV) is a serious threat to sericulture. Nevertheless, no effective control strategy is currently available. The innate immunity of silkworm is critical in the antiviral process. Exploring its molecular mechanism provides theoretical support for the prevention and treatment of BmNPV. Insect hormone receptors play an essential role in regulating host immunity. We found a correlation between Bombyx mori ecdysone receptor B1 (BmEcR-B1) and BmNPV infection, whereas the underlying mechanism remains unclear. In this study, the expression patterns and sequence characteristics of BmEcR-B1 and its isoform, BmEcR-A, were initially analyzed. BmEcR-B1 was found to be more critical than BmEcR-A in silkworm development and responses to BmNPV. Moreover, RNAi and an overexpression in BmN cells showed BmEcR-B1 had antiviral effects in the presence of 20-hydroxyecdysone (20E); Otherwise, it had no antiviral activity. Furthermore, BmEcR-B1 was required for 20E-induced apoptosis, which significantly suppressed virus infection. Finally, feeding 20E had no significant negative impacts on larval growth and the cocoon shell, suggesting the regulation of this pathway has practical value in controlling BmNPV in sericulture. The findings of this study provide important theoretical support for understanding the mechanism of the silkworm innate immune system in response to BmNPV infection.

TyG-BMI and hypertension in Normoglycemia subjects in Japan: A cross-sectional study.

BACKGROUND: Insulin Resistance (IR) are associated with Hypertension (HTN). Triglyceride glucose-body mass index (TyG-BMI) is a readily available and clinically significant indicator of IR. This study aimed to investigate whether TyG-BMI is independently associated with HTN. METHODS: A total of 15,464 patients with normal blood glucose from 2004 to 2016 participated in this study. Participants were divided into four groups using the quartile method: TyG-BMI below 153.1, between 153.1 and 174.2, between 174.2 and 199.3, and over 199.3. The covariates included age, sex, BMI, WC, HDL-C, TC, TG, HbA1c, FPG, ALT, AST, GGT, SBP, DBP, smoking status, alcohol consumption, and exercise habits. RESULTS: The average age was 43.7 ± 8.9 years, and 45.4% were men. The prevalence of HTN was 6.2% (964/15464) of the population. TyG-BMI remained significantly associated with HTN after multivariate adjustment for TyG-BMI as a continuous variable (adjusted OR = 2.87, 95% CI: 1.90-4.34). Each 10-unit rise in TyG-BMI (continuous variable) was linked to a 31% increase in the prevalence of HTN (adjusted OR = 1.31, 95% CI: 1.25-1.37). In the subgroup analysis stratified by age, sex, waist circumference, and smoking status, the association between TyG-BMI and HTN were stable. CONCLUSION: In this study, TyG-BMI was highly correlated with HTN, but more experiments and different populations are needed to verify this.

Application-Layer Time Synchronization and Data Alignment Method for Multichannel Biosignal Sensors Using BLE Protocol.

Wearable wireless biomedical sensors have emerged as a rapidly growing research field. For many biomedical signals, multiple sensors distributed about the body without local wired connections are required. However, designing multisite systems at low cost with low latency and high precision time synchronization of acquired data is an unsolved problem. Current solutions use custom wireless protocols or extra hardware for synchronization, forming custom systems with high power consumption that prohibit migration between commercial microcontrollers. We aimed to develop a better solution. We successfully developed a low-latency, Bluetooth low energy (BLE)-based data alignment method, implemented in the BLE application layer, making it transferable between manufacturer devices. The time synchronization method was tested on two commercial BLE platforms by inputting common sinusoidal input signals (over a range of frequencies) to evaluate time alignment performance between two independent peripheral nodes. Our best time synchronization and data alignment method achieved absolute time differences of 69 ± 71 µs for a Texas Instruments (TI) platform and 477 ± 490 µs for a Nordic platform. Their 95th percentile absolute errors were more comparable-under 1.8 ms for each. Our method is transferable between commercial microcontrollers and is sufficient for many biomedical applications.

Virtual regression-based myoelectric hand-wrist prosthesis control and electrode site selection using no force feedback.

Most transradial prosthesis users with conventional "Sequential" myoelectric control have two electrode sites which control one degree of freedom (DoF) at a time. Rapid EMG co-activation toggles control between DoFs (e.g., hand and wrist), providing limited function. We implemented a regression-based EMG control method which achieved simultaneous and proportional control of two DoFs in a virtual task. We automated electrode site selection using a short-duration (90 s) calibration period, without force feedback. Backward stepwise selection located the best electrodes for either six or 12 electrodes (selected from a pool of 16). We additionally studied two, 2-DoF controllers: "Intuitive" control (hand open-close and wrist pronation-supination controlled virtual target size and rotation, respectively) and "Mapping" control (wrist flexion-extension and ulnar-radial deviation controlled virtual target left-right and up-down movement, respectively). In practice, a Mapping controller would be mapped to control prosthesis hand open-close and wrist pronation-supination. Eleven able-bodied subjects and 4 limb-absent subjects completed virtual target matching tasks (fixed target moves to a new location after being "matched," and subject immediately pursues) and fixed (static) target tasks. For all subjects, both 2-DoF controllers with 6 optimally-sited electrodes had statistically better target matching performance than Sequential control in number of matches (average of 4-7 vs. 2 matches, p< 0.001) and throughput (average of 0.75-1.25 vs. 0.4 bits/s, p< 0.001), but not overshoot rate and path efficiency. There were no statistical differences between 6 and 12 optimally-sited electrodes for both 2-DoF controllers. These results support the feasibility of 2-DoF simultaneous, proportional myoelectric control.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol.

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 µs, while that of the LIDA algorithm was 189.9 ± 204.7 µs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

Force/moment tracking performance during constant-pose, force-varying, bilaterally symmetric, hand-wrist tasks.

Bilateral movement is widely used for calibration of myoelectric prosthesis controllers, and is also relevant as rehabilitation therapy for patients with motor impairment and for athletic training. Target tracking and/or force matching tasks can be used to elicit such bilateral movement. Limited descriptive accuracy data exist in able-bodied subjects for bilateral target tracking or dominant vs non-dominant dynamic force matching tasks requiring more than one degree of freedom (DoF). We examined dynamic trajectory (0.75 Hz band-limited, white, uniform random) constant-posture, hand open-close, wrist pronation-supination target tracking and matching tasks. Tasks were normalized to maximum voluntary contraction (MVC), spanning a ± 30% MVC force range, in four 1-DoF and 2-DoF tasks: (1, 2) unilateral dominant limb tracking with/without visual feedback, and (3, 4) bilateral dominant/non-dominant limb tracking with mirror visual feedback. In 12 able-bodied subjects, unilateral tracking error with visual feedback averaged 10-15 %MVC, but up to 30 %MVC without visual feedback. Bilateral matching error averaged âˆ¼10 %MVC and was affected little by visual feedback type, so long as feedback was provided. In 1-DoF bilateral tracking, the dominant side had statistically lower error than the non-dominant side. In 2-DoF bilateral tracking, the side providing mirror visual feedback exhibited lower error than the opposite side. In 2-DoF tasks (assumed to be more challenging than their constituent 1-DoF tracking tasks), hand grip force errors grew disproportionately larger than those of each wrist DoF. In unilateral 1-DoF tasks, both hand vs target and wrist vs target latency averaged 250-350 ms. In unilateral 2-DoF tasks, wrist vs target latency also averaged 250-350 ms, while hand vs target latency averaged > 500 ms. These results provide guidance on bilateral 2-DoF hand-wrist performance in target tracking, and dominant vs non-dominant force matching tasks.

[Inactivation of Cd and As by an Enterobacter Isolated from Cd and As Contaminated Farmland Soil].

A strain of Enterobacter was screened from cadmium and arsenic contaminated farmland soil and its passivation mechanism of cadmium and arsenic were explored through removing performance and characterization experiments. The results showed that the screened strain M5 was identified as Enterobacter sp. with a sulfate-reduction function, and its maximum resistance concentration was approximately 1 mmol·L-1 to cadmium and arsenic. In the simulation system, the maximum removal efficiencies of cadmium and arsenic were 94.13% and 27.26% by strain M5, respectively. The results of SEM-EDS and XRD confirmed that Cd and As were fixed to CdS and As2S3, and XPS results showed that carboxyl groups, hydroxyl groups, and amide groups on the surface of the bacteria were mainly involved in biological adsorption. These results can provide new ideas and a theoretical basis for microbial applications to soil remediations for heavy metal pollution.

Distance Transform Pooling Neural Network for LiDAR Depth Completion.

Recovering dense depth maps from sparse depth sensors, such as LiDAR, is a recently proposed task with many computer vision and robotics applications. Previous works have identified input sparsity as the key challenge of this task. To solve the sparsity challenge, we propose a recurrent distance transform pooling (DTP) module that aggregates multi-level nearby information prior to the backbone neural network. The intuition of this module is originated from the observation that most pixels within the receptive field of the network are zero. This indicates a deep and heavy network structure has to be used to enlarge the receptive field aiming at capturing enough useful information as most processed signals are uninformative zeros. Our recurrent DTP module can fill in empty pixels with the nearest value in a local patch and recurrently transform distance to reach farther nearest points. The output of the proposed DTP module is a collection of multi-level semi-dense depth maps from original sparse to almost full. Processing this collection of semi-dense depth maps alleviates the network from the input sparsity, which helps a lightweight simplified ResNet-18 with 1M parameters achieve state-of-the-art performance on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) depth completion benchmark with LiDAR only. Besides the sparsity, the input LiDAR map also contains some incorrect values due to the sensor error. Thus, we further enhance the DTP with an error correction (EC) module to avoid the spreading of the incorrect input values. At last, we discuss the benefit of only using LiDAR for nighttime driving and the potential extension of the proposed method for sensor fusion and the indoor scenario. The code has been released online at https://github.com/placeforyiming/DistanceTransform-DepthCompletion.

Revisiting 2D Convolutional Neural Networks for Graph-Based Applications.

Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation. However, current GCNs have limitations on implementation such as network architectures due to their irregular inputs. In contrast, convolutional neural networks (CNNs) are capable of extracting rich features from large-scale input data, but they do not support general graph inputs. To bridge the gap between GCNs and CNNs, in this paper we study the problem of how to effectively and efficiently map general graphs to 2D grids that CNNs can be directly applied to, while preserving graph topology as much as possible. We therefore propose two novel graph-to-grid mapping schemes, namely, graph-preserving grid layout (GPGL) and its extension Hierarchical GPGL (H-GPGL) for computational efficiency. We formulate the GPGL problem as integer programming and further propose an approximate yet efficient solver based on a penalized Kamada-Kawai method, a well-known optimization algorithm in 2D graph drawing. We propose a novel vertex separation penalty that encourages graph vertices to lay on the grid without any overlap. Along with this image representation, even extra 2D maxpooling layers contribute to the PointNet, a widely applied point-based neural network. We demonstrate the empirical success of GPGL on general graph classification with small graphs and H-GPGL on 3D point cloud segmentation with large graphs, based on 2D CNNs including VGG16, ResNet50 and multi-scale maxout (MSM) CNN.

LiDAR-OSM-Based Vehicle Localization in GPS-Denied Environments by Using Constrained Particle Filter.

Cross-modal vehicle localization is an important task for automated driving systems. This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. The OSM modality provides not only a platform to generate simulated point cloud images, but also geometrical constraints (e.g., roads) to improve the particle filter's final result. The proposed approach is deterministic without any learning component or need for labelled data. Evaluated by using the KITTI dataset, it achieves accurate vehicle pose tracking with a position error of less than 3 m when considering the mean error across all the sequences. This method shows state-of-the-art accuracy when compared with the existing methods based on OSM or satellite maps.

Alterations of arterial morphology in patients with aberrant subclavian artery and type B dissection and its association with dissection.

OBJECTIVE: The arterial morphology in patients with aberrant subclavian artery (ASA) and its association with type B aortic dissection are important for treatment and prevention. In the present study, we examined the arterial morphology of ASA patients with type B dissection and evaluated its association with type B dissection in vivo. METHODS: Patients with aortic dissection who had undergone computed tomography angiography were screened for the presence of ASA and type B dissection from January 2011 to May 2021. The angles of ascending aorta, aortic arch, and aortic deviation and the diameters of the ascending aorta, aortic arch, ASA ostium, and middle ASA segment were measured on the computed tomography angiography scans of the ASA patients with type B dissection (group 1; n = 16), clinically matched counterparts without type B dissection (group 2; n = 32), and patients with clinically matched type B dissection without ASA (group 3, n = 32). The correlation between ASA morphology and type B dissection was analyzed using variance analysis or the Wallis H test. RESULTS: Compared with group 2, group 1 had a sharper ascending aortic angle (131.5° ± 13.7° vs 148.1° ± 7.8°; P = .001), a larger aortic deviation angle in plane 2 (28.2° ± 6.0° vs 22.1° ±7.2°; P = .005) and plane 3 (26.4° ±7.3° vs 21.8° ± 6.3°; P = .028). Similarly, group 1 had a greater diameter in the ascending aorta and aortic arch and the ostium and middle of the ASA (38.3 ± 4.1 mm vs 33.6 ± 4.5 mm [P = .001]; 34.0 ± 9.3 mm vs 26.2 ± 2.9 mm [P = .004]; 20.3 ± 9.3 mm vs 14.0 ± 3.2 mm [P = .018]; 10.8 ± 2.3 mm vs 9.0 ± 1.5 mm [P = .002], respectively), without a significant difference in the aortic arch angle. Compared with group 3, group 1 had a sharper ascending aortic angle (131.5° ± 13.7° vs 142.5° ± 11.7°; P = .026) and smaller aortic deviation angle in plane 1 (21.7° ± 6.2° vs 28.9° ± 6.2°; P = .04) and plane 3 (26.4° ± 7.3° vs 21.8° ± 6.3°; P = .007), although with no significant differences in the aortic arch angle, aortic deviation angle in plane 2, and ascending aortic diameter. CONCLUSIONS: The diameters of the ostium and middle segment of the ASA and ascending aorta and the angles of the ascending aorta and aortic deviation are potential risk factors for type B dissection in patients with ASA, which could provide new insights into the mechanism of type B dissection in patients with ASA.

Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects.

Recent research has advanced two degree-of-freedom (DoF), simultaneous, independent and proportional control of hand-wrist prostheses using surface electromyogram signals from remnant muscles as the control input. We evaluated two such regression-based controllers, along with conventional, sequential two-site control with co-contraction mode switching (SeqCon), in box-block, refined-clothespin and door-knob tasks, on 10 able-bodied and 4 limb-absent subjects. Subjects operated a commercial hand and wrist using a socket bypass harness. One 2-DoF controller (DirCon) related the intuitive hand actions of open-close and pronation-supination to the associated prosthesis hand-wrist actions, respectively. The other (MapCon) mapped myoelectrically more distinct, but less intuitive, actions of wrist flexion-extension and ulnar-radial deviation. Each 2-DoF controller was calibrated from separate 90 s calibration contractions. SeqCon performed better statistically than MapCon in the predominantly 1-DoF box-block task (>20 blocks/minute vs. 8-18 blocks/minute, on average). In this task, SeqCon likely benefited from an ability to easily focus on 1-DoF and not inadvertently trigger co-contraction for mode switching. The remaining two tasks require 2-DoFs, and both 2-DoF controllers each performed better (factor of 2-4) than SeqCon. We also compared the use of 12 vs. 6 optimally-selected EMG electrodes as inputs, finding no statistical difference. Overall, we provide further evidence of the benefits of regression-based EMG prosthesis control of 2-DoFs in the hand-wrist.

Preserved microstructural integrity of the corticospinal tract in patients with glioma-induced motor epilepsy: a study using mean apparent propagator magnetic resonance imaging.

BACKGROUND: To compare the microstructural integrity of the corticospinal tract (CST) between glioma patients with motor epilepsy and without epilepsy using mean apparent propagator magnetic resonance imaging (MAP-MRI). METHODS: A total of 26 patients with glioma adjacent to the CST pathway (10 with motor epilepsy and 16 without epilepsy) and 13 matched healthy controls underwent brain structural and diffusion MRI. The morphological characteristics of the CST (tract volume, tract number, and average length) were extracted, and diffusion parameter values including mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probabilities (RTAP), return-to-plane probabilities (RTPP), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) along the CST were evaluated. The CST features were compared between healthy and affected sides and the relative CST features were compared across the three groups of participants. A receiver operating characteristic (ROC) curve was plotted to assess the performance of each relative CST characteristic for glioma-induced CST changes. RESULTS: For patients without epilepsy, the tract number, tract volume, FA, RD, MSD, QIV, and RTAP changed significantly on the affected CST side compared with those on the healthy CST side (P=0.002, 0.002, 0.030 0.017, 0.039, 0.044, and 0.002, respectively). In contrast, for patients with motor epilepsy, no significant difference was found between the affected and healthy side in almost all CST features except RTPP (P=0.028). Compared with patients with motor epilepsy, the relative tract number, tract volume, AD, and RTAP were significantly lower (P=0.027, 0.018, 0.040, and 0.027, respectively) in patients without epilepsy, and their areas under the curve (AUCs) were 0.763, 0.781, 0.744, and 0.763, respectively. No significant difference was found between patients with motor epilepsy and matched healthy controls. CONCLUSIONS: The MAP-MRI is a promising approach for evaluating CST changes. It provides additional information reflecting the microstructural complexity of the CST and demonstrates the preserved microstructural integrity of the CST in glioma patients with motor epilepsy.

Magnetic Resonance Imaging Radiomics-Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study.

BACKGROUND: Determining the absence or presence of peripancreatic lymph nodal metastasis (PLNM) is important to the pathologic staging, prognostication, and guidance of treatment in pancreatic ductal adenocarcinoma (PDAC) patients. Computed tomography and MRI had a poor sensitivity and diagnostic accuracy in the assessment of PLNM. PURPOSES: To develop and validate a 3 T MRI primary tumor radiomics-based nomogram from multicenter datasets for pretreatment prediction of the PLNM in PDAC patients. STUDY TYPE: Retrospective. SUBJECTS: A total of 251 patients (156 men and 95 women; mean age, 60.85 ± 8.23 years) with histologically confirmed pancreatic ductal adenocarcinoma from three hospitals. FIELD STRENGTH AND SEQUENCES: A 3.0 T and fat-suppressed T1-weighted imaging. ASSESSMENT: Quantitative imaging features were extracted from fat-suppressed T1-weighted (FS T1WI) images at the arterial phase. STATISTICAL TESTS: Normally distributed data were compared by using t-tests, while the Mann-Whitney U test was used to evaluate non-normally distributed data. The diagnostic performances of the preoperative and postoperative nomograms were assessed in the external validation cohort with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). AUCs were compared with the De Long test. A p value below 0.05 was considered to be statistically significant. RESULTS: The AUCs of magnetic resonance imaging (MRI) Rad-score were 0.868 (95% confidence level [CI]: 0.613-0.852) and 0.772 (95% CI: 0.659-0.879) in the training and internal validation cohort, respectively. The preoperative and postoperative nomograms could accurately predict PLNM in the training cohort (AUC = 0.909 and 0.851) and were validated in both the internal and external cohorts (AUC = 0.835 and 0.805, 0.808 and 0.733, respectively). DCA indicated that the two novel nomograms are of similar clinical usefulness. DATA CONCLUSION: Pre-/postoperative nomograms and the constructed radiomics signature from primary tumor based on FS T1WI of arterial phase could serve as a potential tool to predict PLNM in patients with PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

High-Performance-Integrated Stretchable Supercapacitors Based on a Polyurethane Organo/Hydrogel Electrolyte.

Stretchable supercapacitors (SSCs) are promising energy storage devices for emerging wearable electronics. However, the low-energy density and poor deformation performance are still a challenge. Herein, an amphiphilic polyurethane-based organo/hydrogel electrolyte (APUGE) with a H2O/AN-in-salt (H2O/AN-NaClO4) is prepared for the first time. The as-prepared APUGE shows a wide voltage window (∼2.3 V), good adhesion, and excellent resilience. In addition, the intrinsically stretchable electrodes are prepared by coating the activated carbon slurry onto the PU/carbon black/MWCNT conductive elastic substrate. Based on the strong interface adhesion of the PU matrix, the as-assembled SSC delivers high-energy density (5.65 mW h cm-3 when the power density is 0.0256 W cm-3) and excellent deformation stability with 94.5% capacitance retention after 500 stretching cycles at 100% strain. This fully integrated construction concept is expected to be extended to multisystem stretchable metal ion batteries, stretchable lithium-sulfur batteries, and other stretchable energy storage devices.

Ultra-high b value DWI in distinguishing fresh gray matter ischemic lesions from white matter ones: a comparative study with routine and high b value DWI.

BACKGROUND: Fresh ischemic lesions (FILs) can occur in both the brain's gray matter (GM) and white matter (WM), with each location signifying a different prognosis for patients. This study aims to investigate the application of ultra-high b value diffusion-weighted imaging (DWI) in distinguishing FILs in these two areas via a comparative study with routine and high b value DWI. METHODS: Multiple b value DWI (b=0, 500, 1,000, 2,000, 4,000, 6,000, 8,000, 10,000 s/mm2) was performed on 47 patients with suspected acute ischemic stroke (AIS). Apparent diffusion coefficient (ADC) maps, including ADC500, ADC1,000, ADC2,000, ADC4,000, ADC6,000, ADC8,000, and ADC10,000, were calculated, and the mean ADC value of the FILs in the GM and WM on each map was obtained by referring to the structural magnetic resonance imaging (MRI). ADC value differences of the FILs in the GM and WM were compared using Mann-Whitney U tests, and receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of each ADC value in distinguishing FILs in the two areas. RESULTS: In the enrolled 34 patients, 145 FILs were identified, of which 42 involved the GM, 87 the WM, and 16 both the GM and WM. A total of 161 regions were delineated, 58 in the GM and 103 in the WM. The values of FILs in the WM on ADC2,000, ADC4,000, ADC6,000, ADC8,000, and ADC10,000 maps were significantly lower than those in the GM (P=0.007, P<0.001, P<0.001, P<0.001 and P<0.001, respectively), while no significant differences were found on ADC500 and ADC1,000 maps (P=0.427 and P=0.225, respectively). ROC curves demonstrated that the area under the curve (AUC) paralleled the increasing b value, ascending from ADC500 to ADC10,000 (0.538, 0.558, 0.629, 0.766, 0.827, 0.859, 0.872, in that order). CONCLUSIONS: Ultra-high b value DWI is extremely sensitive to the slight diffusion difference between FILs in the GM and the WM. Its sensitivity parallels the increasing b value, indicating its clinical advantage in identifying the microstructure of FILs.