[Characterization of brain deactivations elicited by transient painful and tactile stimuli using functional MRI].

The aim of the present study was to explore the specific pattern of brain deactivation elicited by painful stimuli, in contrast with that elicited by tactile stimuli. Functional magnetic resonance imaging (fMRI) data were collected from 62 healthy subjects under painful and tactile stimuli with varying intensities. The brain deactivations under different conditions were identified using the general linear model. Two-way analysis of variance (ANOVA) was performed to test whether there was a significant interaction between perceived stimulus intensity (factor 1: high intensity, low intensity) and stimulus modality (factor 2: pain, touch) on the brain deactivations. The results showed that there were significant interactions between stimulus intensity and stimulus modality on the deactivations of left medial superior frontal gyrus, left middle occipital gyrus, left superior frontal gyrus and right middle occipital gyrus (P < 0.05, Cluster-level FWE). The deactivations induced by painful stimuli with low perceived intensity (ß = -3.38 ± 0.52) were significantly stronger than those induced by painful stimuli with high perceived intensity (ß = -1.22 ± 0.54) (P < 0.001), whereas the differences between the deactivations induced by tactile stimuli with different perceived intensities were not statistically significant. In addition, there were no significant differences between the deactivations elicited by painful and tactile stimuli with the same stimulus intensities. These results suggest that there is a specific relationship between the deactivations induced by painful stimuli in multiple brain regions (such as the left medial superior frontal gyrus) and the stimulus intensity, providing evidence for a deeper understanding of the brain mechanisms underlying pain perception.

Pulvinar Response Profiles and Connectivity Patterns to Object Domains.

Distributed cortical regions show differential responses to visual objects belonging to different domains varying by animacy (e.g., animals vs tools), yet it remains unclear whether this is an organization principle also applying to the subcortical structures. Combining multiple fMRI activation experiments (two main experiments and six validation datasets; 12 females and 9 males in the main Experiment 1; 10 females and 10 males in the main Experiment 2), resting-state functional connectivity, and task-based dynamic causal modeling analysis in human subjects, we found that visual processing of images of animals and tools elicited different patterns of response in the pulvinar, with robust left lateralization for tools, and distinct, bilateral (with rightward tendency) clusters for animals. Such domain-preferring activity distribution in the pulvinar was associated with the magnitude with which the voxels were intrinsically connected with the corresponding domain-preferring regions in the cortex. The pulvinar-to-right-amygdala path showed a one-way shortcut supporting the perception of animals, and the modulation connection from pulvinar to parietal showed an advantage to the perception of tools. These results incorporate the subcortical regions into the object processing network and highlight that domain organization appears to be an overarching principle across various processing stages in the brain.SIGNIFICANCE STATEMENT Viewing objects belonging to different domains elicited different cortical regions, but whether the domain organization applied to the subcortical structures (e.g., pulvinar) was unknown. Multiple fMRI activation experiments revealed that object pictures belonging to different domains elicited differential patterns of response in the pulvinar, with robust left lateralization for tool pictures, and distinct, bilateral (with rightward tendency) clusters for animals. Combining the resting-state functional connectivity and dynamic causal modeling analysis on task-based fMRI data, we found domain-preferring activity distribution in the pulvinar aligned with that in cortical regions. These results highlight the need for coherent visual theories that explain the mechanisms underlying the domain organization across various processing stages.

Vacancy defect engineered BiVO4 with low-index surfaces for photocatalytic application: a first principles study.

BiVO4 has been widely investigated as a photocatalyst material for water splitting due to its outstanding photocatalytic properties. In order to further improve its photocatalytic efficiency, it is necessary to conduct an in-depth study of improvement strategies, such as defect engineering. By focusing on the (001) and (011) surfaces, we carried out a systematic theoretical research on pristine and defective systems, including Bi, V and O vacancies. Based on density functional theory (DFT), the electronic properties, band alignments and Gibbs free energy of pristine and defective BiVO4 have been analyzed. The electronic structures of the (001) and (011) surfaces show different band gaps, and O vacancies make the BiVO4 become an n-type semiconductor, while Bi and V vacancies tend to form a p-type semiconductor. Moreover, the band edge positions indicate that holes are indeed easily accumulated on the (011) surface while electrons tend to accumulate on (001). However, the (011) surface with Bi and V vacancies does not have enough oxidation potential to oxidize water. The reaction free energy shows that O and Bi vacancies could lower the overpotential to some extent.

Exploring neurometabolic alterations in bipolar disorder with suicidal ideation based on proton magnetic resonance spectroscopy and machine learning technology.

Bipolar disorder (BD) is associated with a high risk of suicide. We used proton magnetic resonance spectroscopy (1H-MRS) to detect biochemical metabolite ratios in the bilateral prefrontal white matter (PWM) and hippocampus in 32 BD patients with suicidal ideation (SI) and 18 BD patients without SI, identified potential brain biochemical differences and used abnormal metabolite ratios to predict the severity of suicide risk based on the support vector machine (SVM) algorithm. Furthermore, we analyzed the correlations between biochemical metabolites and clinical variables in BD patients with SI. There were three main findings: (1) the highest classification accuracy of 88% and an area under the curve of 0.9 were achieved in distinguishing BD patients with and without SI, with N-acetyl aspartate (NAA)/creatine (Cr), myo-inositol (mI)/Cr values in the bilateral PWM, NAA/Cr and choline (Cho)/Cr values in the left hippocampus, and Cho/Cr values in the right hippocampus being the features contributing the most; (2) the above seven features could be used to predict Self-rating Idea of Suicide Scale scores (r = 0.4261, p = 0.0302); and (3) the level of neuronal function in the left hippocampus may be related to the duration of illness, the level of membrane phospholipid catabolism in the left hippocampus may be related to the severity of depression, and the level of inositol metabolism in the left PWM may be related to the age of onset in BD patients with SI. Our results showed that the combination of multiple brain biochemical metabolites could better predict the risk and severity of suicide in patients with BD and that there was a significant correlation between biochemical metabolic values and clinical variables in BD patients with SI.

Brain-activation-based individual identification reveals individually unique activation patterns elicited by pain and touch.

Pain is subjective and perceived differently in different people. However, individual differences in pain-elicited brain activations are largely overlooked and often discarded as noises. Here, we used a brain-activation-based individual identification procedure to investigate the uniqueness of the activation patterns within the whole brain or brain regions elicited by nociceptive (laser) and tactile (electrical) stimuli in each of 62 healthy participants. Specifically, brain activation patterns were used as "fingerprints" to identify each individual participant within and across sensory modalities, and individual identification accuracy was calculated to measure each individual's identifiability. We found that individual participants could be successfully identified using their brain activation patterns elicited by nociceptive stimuli, tactile stimuli, or even across modalities. However, different participants had different identifiability; importantly, the within-pain, but not within-touch or cross-modality, individual identifiability obtained from three brain regions (i.e., the left superior frontal gyrus, the middle temporal gyrus and the insular gyrus) were inversely correlated with the scores of Pain Vigilance and Awareness Questionnaire (i.e., how a person is alerted to pain) across participants. These results suggest that each individual has a unique pattern of brain responses to nociceptive stimuli which contains both modality-nonspecific and pain-specific information and may be associated with pain-related behaviors shaped by his/her own personal experiences and highlight the importance of a transition from group-level to individual-level characterization of brain activity in neuroimaging studies.

Functional MRI evidence for primary motor cortex plasticity contributes to the disease's severity and prognosis of cervical spondylotic myelopathy patients.

OBJECTIVE: To investigate the brain mechanism of non-correspondence between diseases severity and compression degree of the spinal cord in cervical spondylotic myelopathy (CSM) patients and to test the utility of brain imaging biomarkers for predicting prognosis of CSM. METHODS: We calculated voxel-wise zALFF from 54 CSM patients and 50 healthy controls using resting-state fMRI data. In analysis 1, we identified the brain regions exhibited significant differences of zALFF between CSM patients and healthy controls. In analyses 2 through 3, we investigated the zALFF differences between light-symptom CSM patients and severe-symptom CSM patients while carefully matching the degree of compression between these two groups. In analysis 4, we tested the utility of zALFF within the primary motor cortex (M1) for predicting the prognosis of CSM. RESULTS: We found that (1) compared with the healthy controls, CSM patients exhibited higher ALFF within left M1, bilateral superior frontal gyrus, and lower zALFF within right precuneus and calcarine, suggesting altered brain neural activity in CSM patients; (2) after matching the compression degree, the CSM patients with more severe clinical symptoms exhibited higher zALFF within M1, indicating cortical function contributes to disease's severity of CSM; (3) taking the M1 zALFF as features in the prognosis prediction model improves the prediction accuracy, indicating that the M1 zALFF provide additional value for predicting the prognosis of CSM patients following decompression surgery. CONCLUSION: The functional state of M1 contributes to the disease's severity of CSM and can provide complementary information for predicting the prognosis of CSM following decompression surgery. KEY POINTS: • Cervical spondylotic myelopathy (CSM) patients exhibited increased zALFF within the primary motor cortex (M1), bilateral superior frontal gyrus, and decreased zALFF within the right precuneus and calcarine. • After matching the compression degree, the CSM patients with more severe clinical symptoms exhibited higher zALFF within M1, indicating cortical function contributes to disease severity of CSM. • zALFF within M1 provided additional value for predicting the prognosis of CSM patients.

High Mobility Two-Dimensional Bismuth Oxyselenide Single Crystals with Large Grain Size Grown by Reverse-Flow Chemical Vapor Deposition.

2D semiconductors with atomically thin body thickness have attracted tremendous research interest for high-performance nanoelectronics and optoelectronics. Most of the 2D semiconductors grown by chemical vapor deposition (CVD) methods suffer from rather low carrier mobility, small single-crystal size, and instability under ambient conditions. Here, we develop an improved CVD method with controllable reverse-gas flow to realize the direct growth of quality Bi2O2Se 2D single crystals on a mica substrate. The applied reverse flow significantly suppresses the random nucleation and thus promotes the lateral size of 2D Bi2O2Se crystals up to ∼750 µm. The Bi2O2Se field-effect transistors display high-room-temperature electron mobility up to ∼1400 cm2·V-1·s-1 and a well-defined drain current saturation. The on/off ratio of the Bi2O2Se transistor is larger than 107, and the sub-threshold swing is about 90 mV·dec-1. The responsivity, response time, and detectivity of Bi2O2Se photodetectors approach up to 60 A·W-1, 5 ms, and 2.4 × 1010 Jones at room temperature, respectively. Our results demonstrate large-size and high-quality Bi2O2Se grown by reverse-flow CVD as a high-performance channel material for next-generation transistors and photodetectors.

Porous Cobalt Sulfide Selenium Nanorods for Electrochemical Hydrogen Evolution.

A key process in electrochemical energy technology is hydrogen evolution reaction (HER). However, its electrochemical properties mainly depend on the catalytic activity of the material itself. Therefore, it is important to find efficient electrocatalysts to realize clean hydrogen production. As a typical kind of catalytic materials, transition metal dichalcogenides (TMCs) play important roles in the field of energy catalysis. As a representative of TMCs, cobalt disulfide (CoS2), recently has raised much research interest owing to its abundant reserves, environmental friendliness, and excellent electrochemical stability. Meanwhile, given the fact that doping is one of the effective methods to improve the electrochemical catalytic property, various means of doping have been researched. Here, we report for the first time that porous-like Se-CoS2-x (or Se:CoS2-x ) nanorod can be facilely synthesized via a controllable two-step strategy. It is demonstrated that doping Se can greatly improve the catalytic performance of CoS2 electrode. The electrode can obtain a current density of 10 mA cm-2 at overpotential of only ∼260 mV. And the current changes with the applied bias voltage in an obvious stepped pattern, in the chronopotential (CP) curve of Se-CoS2-x , indicating its outstanding mass transfer property and mechanical stability.

Corrigendum: Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma.

[This corrects the article DOI: 10.3389/fonc.2021.709137.].

Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma.

OBJECTIVE: The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS: Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SECT and SEPET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SECT and SEPET. RESULTS: The AUCs of the SECT and SEPET were 0.72 (95% CI, 0.62-0.80) and 0.74 (95% CI, 0.65-0.82) in the testing data set, respectively. After integrating SECT and SEPET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75-0.90), significantly higher than SECT (p<0.05). CONCLUSION: The stacking model based on 18F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR-targeted therapy.

Enhanced Information Flow From Cerebellum to Secondary Visual Cortices Leads to Better Surgery Outcome in Degenerative Cervical Myelopathy Patients: A Stochastic Dynamic Causal Modeling Study With Functional Magnetic Resonance Imaging.

Degenerative cervical myelopathy (DCM) damages the spinal cord, resulting in long-term neurological impairment including motor and visual deficits. Given that visual feedback is crucial in guiding movements, the visual disorder may be a cause of motor deficits in patients with DCM. It has been shown that increased functional connectivity between secondary visual cortices and cerebellum, which are functionally related to the visually guided movements, was correlated with motor function in patients with DCM. One possible explanation is that the information integration between these regions was increased to compensate for impaired visual acuity in patients with DCM and resulted in better visual feedback during motor function. However, direct evidence supporting this hypothesis is lacking. To test this hypothesis and explore in more detail the information flow within the "visual-cerebellum" system, we measured the effective connectivity (EC) among the "visual-cerebellum" system via dynamic causal modeling and then tested the relationship between the EC and visual ability in patients with DCM. Furthermore, the multivariate pattern analysis was performed to detect the relationship between the pattern of EC and motor function in patients with DCM. We found (1) significant increases of the bidirectional connections between bilateral secondary visual cortices and cerebellum were observed in patients with DCM; (2) the increased self-connection of the cerebellum was positively correlated with the impaired visual acuity in patients; (3) the amplitude of effectivity from the cerebellum to secondary visual cortices was positively correlated with better visual recovery following spinal cord decompression surgery; and (4) the pattern of EC among the visual-cerebellum system could be used to predict the pre-operative motor function. In conclusion, this study provided direct evidence that the increased information integration within the "visual-cerebellum" system compensated for visual impairments, which might have importance for sustaining better motor function in patients with DCM.

Interface engineering of cobalt-sulfide-selenium core-shell nanostructures as bifunctional electrocatalysts toward overall water splitting.

The number of active sites and stability of the structure of electrocatalysts are the key factors in the process of overall water splitting. In this paper, cobalt-sulfide-selenium (Se:CoS2-x) core-shell nanostructures are prepared by a simple two-step method, including hydrothermal reaction and chemical vapor deposition. The resulting product exhibits excellent electrochemical performance, owing to the synergistic effects between CoS2 and CoSe1-x, as well as the plentiful active sites in the electrode structure. The Se:CoS2-x material shows a more improved hydrogen evolution reaction activity compared to CoS2 and Co(OH)Cl precursor catalysts, with a low overpotential of only 240 mV achieved at 10 mA cm-2. Meanwhile, Se:CoS2-x as a bifunctional water splitting catalyst also shows remarkably improved oxygen evolution reaction activity, with a low overpotential of only 1.32 V at 10 mA cm-2. The above results show that selenide/sulfide materials provide a new research direction for discovering high-performance and cheap electrode materials.

Feedforward and feedback pathways of nociceptive and tactile processing in human somatosensory system: A study of dynamic causal modeling of fMRI data.

Nociceptive and tactile information is processed in the somatosensory system via reciprocal (i.e., feedforward and feedback) projections between the thalamus, the primary (S1) and secondary (S2) somatosensory cortices. The exact hierarchy of nociceptive and tactile information processing within this 'thalamus-S1-S2' network and whether the processing hierarchy differs between the two somatosensory submodalities remains unclear. In particular, two questions related to the ascending and descending pathways have not been addressed. For the ascending pathways, whether tactile or nociceptive information is processed in parallel (i.e., 'thalamus-S1' and 'thalamus-S2') or in serial (i.e., 'thalamus-S1-S2') remains controversial. For the descending pathways, how corticothalamic feedback regulates nociceptive and tactile processing also remains elusive. Here, we aimed to investigate the hierarchical organization for the processing of nociceptive and tactile information in the 'thalamus-S1-S2' network using dynamic causal modeling (DCM) combined with high-temporal-resolution fMRI. We found that, for both nociceptive and tactile information processing, both S1 and S2 received inputs from thalamus, indicating a parallel structure of ascending pathways for nociceptive and tactile information processing. Furthermore, we observed distinct corticothalamic feedback regulations from S1 and S2, showing that S1 generally exerts inhibitory feedback regulation independent of external stimulation whereas S2 provides additional inhibition to the thalamic activity during nociceptive and tactile information processing in humans. These findings revealed that nociceptive and tactile information processing have similar hierarchical organization within the somatosensory system in the human brain.

Community composition of cecal microbiota in commercial yellow broilers with high and low feed efficiencies.

The cecal microbiota plays important roles in host food digestion and nutrient absorption, which may in part affect feed efficiency (FE). To investigate the composition and functional differences of cecal microbiota between high (n = 30) and low (n = 29) feed conversion ratio (FCR; metric for FE) groups, we performed 16S rRNA gene sequencing and predicted the metagenome function using Phylogenetic Investigation of Communities by Reconstruction of Unobserved Species in yellow broilers. The results showed that the 2 groups had the same prominent microbes but with differing abundance. Firmicutes, Bacteroidetes, and Actinobacteria were 3 prominent bacterial phyla in the cecal microbial community. Although there were no differences in microbial diversity, compositional differences related to FCR were found via linear discriminant analysis (LDA) effect size; the genus Bacteroides had a significantly higher abundance (LDA >2) in the high FE (HFE) group than in the low FE group. Furthermore, genus Bacteroides had a negative FCR-associated correlation (P < 0.05). Oscillospira was positively correlated with Bacteroides in both groups, whereas Dorea was negatively correlated with Bacteroides in the HFE group. Predictive functional analysis revealed that metabolic pathways such as "starch and sucrose metabolism," "phenylalanine, tyrosine and tryptophan biosynthesis," and "carbohydrate metabolism" were significantly enriched in the HFE group. The relatively subtle differences in FE-associated cecal microbiota composition suggest a possible link between cecal microbiota and FE. Moreover, Bacteroides may potentially be used as biomarkers for FE to improve growth performance in yellow broilers.

Stability test of canonical correlation analysis for studying brain-behavior relationships: The effects of subject-to-variable ratios and correlation strengths.

Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain-behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject-to-variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first-mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain-behavior correlation strengths. The same tests were repeated using an independent data set (n = 700) for validation. The results confirmed that both SVR and correlation strength affect greatly the CCA stability-the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain-behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain-behavior relationships.

Prediction of mediastinal lymph node metastasis based on 18F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer.

OBJECTIVE: The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC). METHOD: Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application. RESULTS: The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected. CONCLUSION: An SVM model based on 18F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier. KEY POINTS: • The SVM model based on 18F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC. • The SURblood plays a major role in the SVM model. • The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.

A multiple-channel and atrous convolution network for ultrasound image segmentation.

PURPOSE: Ultrasound image segmentation is a challenging task due to a low signal-to-noise ratio and poor image quality. Although several approaches based on the convolutional neural network (CNN) have been applied to ultrasound image segmentation, they have weak generalization ability. We propose an end-to-end, multiple-channel and atrous CNN designed to extract a greater amount of semantic information for segmentation of ultrasound images. METHOD: A multiple-channel and atrous convolution network is developed, referred to as MA-Net. Similar to U-Net, MA-Net is based on an encoder-decoder architecture and includes five modules: the encoder, atrous convolution, pyramid pooling, decoder, and residual skip pathway modules. In the encoder module, we aim to capture more information with multiple-channel convolution and use large kernel convolution instead of small filters in each convolution operation. In the last layer, atrous convolution and pyramid pooling are used to extract multi-scale features. The architecture of the decoder is similar to that of the encoder module, except that up-sampling is used instead of down-sampling. Furthermore, the residual skip pathway module connects the subnetworks of the encoder and decoder to optimize learning from the deeper layer and improve the accuracy of segmentation. During the learning process, we adopt multi-task learning to enhance segmentation performance. Five types of datasets are used in our experiments. Because the original training data are limited, we apply data augmentation (e.g., horizontal and vertical flipping, random rotations, and random scaling) to our training data. We use the Dice score, precision, recall, Hausdorff distance (HD), average symmetric surface distance (ASD), and root mean square symmetric surface distance (RMSD) as the metrics for segmentation evaluation. Meanwhile, Friedman test was performed as the nonparametric statistical analysis to evaluate the algorithms. RESULTS: For the datasets of brachia plexus (BP), fetal head, and lymph node segmentations, MA-Net achieved average Dice scores of 0.776, 0.973, and 0.858, respectively; with average precisions of 0.787, 0.968, and 0.854, respectively; average recalls of 0.788, 0.978, and 0.885, respectively; average HDs (mm) of 13.591, 10.924, and 19.245, respectively; average ASDs (mm) of 4.822, 4.152, and 4.312, respectively; and average RMSDs (mm) of 4.979, 4.161, and 4.930, respectively. Compared with U-Net, U-Net++, M-Net, and Dilated U-Net, the average performance of the MA-Net increased by approximately 5.68%, 2.85%, 6.59%, 36.03%, 23.64%, and 31.71% for Dice, precision, recall, HD, ASD, and RMSD, respectively. Moreover, we verified the generalization of MA-Net segmentation to lower grade brain glioma MRI and lung CT images. In addition, the MA-Net achieved the highest mean rank in the Friedman test. CONCLUSION: The proposed MA-Net accurately segments ultrasound images with high generalization, and therefore, it offers a useful tool for diagnostic application in ultrasound images.

A Super-Stretchable Liquid Metal Foamed Elastomer for Tunable Control of Electromagnetic Waves and Thermal Transport.

It is remarkably desirable and challenging to design a stretchable conductive material with tunable electromagnetic-interference (EMI) shielding and heat transfer for applications in flexible electronics. However, the existing materials sustained a severe attenuation of performances when largely stretched. Here, a super-stretchable (800% strain) liquid metal foamed elastomer composite (LMF-EC) is reported, achieving super-high electrical (≈104 S cm-1) and thermal (17.6 W mK-1) conductivities under a large strain of 400%, which also exhibits unexpected stretching-enhanced EMI shielding effectiveness of 85 dB due to the conductive network elongation and reorientation. By varying the liquid and solid states of LMF, the stretching can enable a multifunctional reversible switch that simultaneously regulates the thermal, electrical, and electromagnetic wave transport. Novel flexible temperature control and a thermoelectric system based on LMF-EC is furthermore developed. This work is a significant step toward the development of smart electromagnetic and thermal regulator for stretchable electronics.