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Objectives –Mismatch negativity (MMN) has been demonstrated as a potential biomarker for pre-attentive processing and prognosis in patients with psychosis. However, previous studies mainly evaluated the reliability of MMN across only two repeated sessions, which is inadequate to draw a convincing conclusion. The current study aimed to assess multi-session test-retest reliability in duration-related MMN (dMMN) and frequency-related MMN (fMMN). Methods –We recorded four repeated sessions of electroencephalography (EEG) from 16 healthy participants in an oddball task. MMNs were extracted and their reliability was evaluated by intra-class coefficient (ICC). We also analyzed the correlation between fMMN and dMMN. Results –Both dMMN and fMMN amplitudes exhibited good test-retest reliability, and fMMN had better reliability (average ICC = 0.7279) than dMMN (average ICC = 0.6974). Moreover, dMMN and fMMN showed more than moderate linear correlation in amplitudes (r = 0.598, CI: [0.100, 0.857]). Conclusion –Both the duration- and frequency-related MMN amplitudes were highly reliable across four-session experiments. These results provide further evidence for the potential utility of MMNs as biomarkers in research into brain function, and prognosis in psychotic illness.

Mismatch negativity (MMN) is an intensively studied event-related potential component that reflects pre-attentive auditory processing. Existing spatial MMN (sMMN) studies usually use loud-speakers in different locations or deliver sound with binaural localization cues through earphones to elicit MMN, which either was practically complicated or sounded unnatural to the subjects. In the present study, we generated head related transfer function (HRTF)-based spatial sounds and verified that the HRTF-based sounds retained the left and the right spatial localization cues. We further used them as deviants to elicit sMMN with conventional oddball paradigm. Results showed that sMMN was successfully elicited by the HRTF-based deviants in 18 of 21 healthy subjects in two separate sessions. Furthermore, the left deviants elicited higher sMMN amplitudes in the right hemisphere compared to the left hemisphere, while the right deviants elicited sMMN with similar amplitudes in both hemispheres, which supports a combination of contralateral and right-hemispheric dominance in spatial auditory information processing. In addition, the sMMN in response to the right deviants showed good test-retest reliability, while the sMMN in response to the left deviants had weak test-retest reliability. These findings implicate that HRTF-based sMMN could be a robust paradigm to investigate spatial localization and discrimination abilities.

Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the performance of the traditional radiomics model, deep learning radiomics model, and the fusion models in evaluating breast cancer ALN status based on dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) images.The handcrafted radiomics features and deep features were extracted from 3062 DCE-MRI images. The feature selection was performed by applying mutual information and feature recursive elimination algorithms. The traditional radiomics model and deep learning radiomics model were built using the optimal features and machine learning classifiers, respectively. The fusion models for distinguishing axillary lymph node status were constructed using two fusion strategies. The performance of the models with MRI-reported lymphadenopathy or suspicious nodes to evaluate axillary lymph node status was also compared.The decision fusion model, with the integration of the radiomics features and deep learning features at the decision level, achieved an area under the curve (AUC) of 0.91 (95% confidence interval (CI): 0.879-0.937), which was higher than that of the traditional radiomics model and deep learning radiomics model. The results of the decision fusion model with clinical characteristic yielded an AUC of 0.93 (95% CI: 0.899-0.951), which was also superior to other models incorporating clinical characteristic.This study demonstrates the effectiveness of the fusion models for predicting axillary lymph node metastasis in breast cancer.

Biological signal transduction of protein-protein interaction (PPI) networks occupies an important position in life activities because it affects the regulation mechanisms of growth, development and metabolism. To further understand the realization of cellular biological functions, we need to predict the directionality of PPIs. This study combines protein semantic similarity and overlapping clustering algorithm to improve the network propagation method for orientating signal transduction networks. This orientation algorithm was applied to an integrin-mediated PPI network (containing 1166 PPIs). As a result, 484 unoriented PPIs (forming protein complexes) and 682 oriented PPIs (interactions involving signal transmission) were obtained. Finally, in order to evaluate the effectiveness of this method, it was compared with the original method based on network propagation, and acceptable results were obtained.

Light-assisted antibacterial therapy is a promising alternative to antibiotic therapy due to the high antibacterial efficacy without bacterial resistance. Recent research has mainly focused on the use of near-infrared light irradiation to kill bacteria by taking advantage of the synergistic effects rendered by hyperthermia and radical oxygen species. However, photocatalytic antibacterial therapy excited by visible light is more convenient and practical, especially for wounds. Herein, a visible light responsive organic-inorganic hybrid of ZnTCPP/Ti

Background. Low-intensity transcranial ultrasound stimulation (TUS) could induce both immediate and long-lasting neuromodulatory effects in human brains. Interhemispheric imbalance at prefrontal or motor cortices generally associates with various cognitive decline in aging and mental disorders. However, whether TUS could modulate the interhemispheric balance of excitability in human brain remains unknown. Objective. This study aims to explore whether repetitive TUS (rTUS) intervention can modulate the interhemispheric balance of excitability between bilateral motor cortex (M1) in healthy subjects. Approach. Motor evoked potentials (MEPs) at bilateral M1 were measured at 15 min and 0 min before a 15 min active or sham rTUS intervention on left M1 and at 0 min, 15 min and 30 min after the intervention, and the Chinese version of brief neurocognitive test battery (C-BCT) was conducted before and after the intervention respectively. Cortical excitability was quantified by MEPs, and the long-lasting changes of MEP amplitude was used as an index of plasticity. Results. In the active rTUS group (n = 20), the ipsilateral MEP amplitude increased significantly compared with baselines and lasted for up to 30 min after intervention, while the contralateral MEP amplitude decreased lasting for 15 min, yielding increased laterality between bilateral MEPs. Furthermore, rTUS intervention induced changes in some C-BCT scores, and the changes of scores correlated with the changes of MEP amplitudes induced by rTUS intervention. The sham rTUS group (n = 20) showed no significant changes in MEPs and C-BCT scores. In addition, no participants reported any adverse effects during and after the rTUS intervention, and no obvious temperature increase appeared in skull or brain tissues in simulation. Significance. rTUS intervention modulated the plasticity of ipsilateral M1 and the interhemispheric balance of M1 excitability in human brain, and improved cognitive performance, suggesting a considerable potential of rTUS in clinical interventions.

Trait-based approaches are increasingly used to improve ecological restoration in degraded ecosystems. The aim of this study was to evaluate how vegetation controls soil detachment capacity (Dc) by concentrated flow based on the linkages between root traits and soil physics. We selected 60 plots along a land degradation gradient caused by long-term water erosion in a small catchment, central China. These plots consisted of woodlands, scrublands, grasslands and bare lands. And soil physical properties, root traits and Dc were measured in each plot. Fine roots (0.2 mm

Summary Objectives Studies have reported that depressed patients have difficulties in disengaging attention from negative information, but knowledge of the temporal characteristics of this disengagement is still rudimentary. Our objective is to reveal the temporal characteristics of attentional disengagement from emotional facial cues in depression. Methods We recruited 22 depressed patients and 22 healthy controls to participate in a cued target-response task with emotional facial expressions (happy, natural, and sad) as cues and three types of cue-target intervals (CTIs: 350 ms, 1000 ms, and 1500 ms). Both behavioral and electroencephalography (EEG) data were collected from each subject while performing the task. Then, both behavioral results and event-related potentials (ERPs) were analyzed across groups (depressed patients and normal controls), emotional types of facial cues (happy, natural, and sad), and CTIs. Results Both depressed patients and healthy controls had shorter response times in the conditions of CTI = 1000 ms and 1500 ms than in the condition of CTI = 350 ms but had no significant difference in response time between the conditions of CTI = 1000 ms and CTI = 1500 ms. The contingent negative variation (CNV), a well-documented ERP marker of cue-induced expectation of the forthcoming target, clearly appeared about 1000 ms following cue onset for normal controls and 1300 ms following cue onset for depressed patients. Statistical analysis by repeated-measures Anova showed that a main group effect exists for the average amplitudes of ERPs at electrode Cz 930 ms after cue onset, while there was no main effect of cue or interaction effect between cue and group. Discussion These results suggest that normal controls complete their attentional disengagement from emotional facial expression between 350 ms to 1000 ms after cue offset, while depressed patients complete their attentional disengagement later than that of normal controls but earlier than 1500 ms from the perspective of CNV onset, though the two groups have no significant difference in response time in the conditions of CTI = 1000 ms and CTI = 1500 ms respectively.

Patients with schizophrenia show widespread impairments in clinical, cognitive and psychosocial functioning. Mismatch negativity (MMN) and gamma-band auditory steady-state response (ASSR) are two neurophysiological biomarkers widely used to inform diagnosis, guide treatments and track response to interventions in schizophrenia. However, evidence for the test-retest reliability of these indices across multiple sessions in schizophrenia patients remains scarce. In the present study, we included 34 schizophrenia patients (17 females) and obtained duration MMN (dMMN), frequency MMN (fMMN) and 40-Hz ASSR data across three sessions with intervals of 2 days. Event-related spectrum perturbation (ERSP) and inter-trial coherence (ITC) were calculated following Morlet wavelet time-frequency decomposition of ASSR data. The intra-class correlation coefficient (ICC) was used to quantify the reliability of MMN and ASSR measures among the three sessions. We found fair to good reliability for dMMN amplitudes but poor reliability for fMMN amplitudes. For the ASSR measures, ERSP showed good to excellent test-retest reliability while ITC had poor to fair test-retest reliability. In addition, the average of dMMN amplitudes was significantly correlated with that of ERSP across the three sessions. In summary, we established for the first time the short-term test-retest reliability of MMN and ASSR measures in schizophrenia patients. These findings demonstrate that dMMN amplitudes and ERSP of ASSR are reliable indices which may be used in longitudinal observational studies.

Background and Objective: Identifying the KRAS mutation status accurately in medical images is very important for the diagnosis and treatment of colorectal cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to limited annotated dataset, large intra-class variances, and a high degree of inter-class similarities. Methods: To tackle these challenges, we propose a spatial-frequency dual-branch attention model (SF-DBAM) to determine the KRAS mutation status of colorectal cancer patients using a limited T2-weighted MRI dataset. The dataset contains 169 wild-type patients (2151 images) and 137 mutation-type patients (1666 images). The first branch utilizes part of the pre-trained Xception model to capture spatial-domain information and alleviate the small-scale dataset problem. The second branch builds frequency-domain information into cube columns using block-based discrete cosine transform and channel rearrangement. Then the cube columns are fed into convolutional long short-term memory (convLSTM) to explore the effective information between the reconstructed frequency-domain channels. Also, we design a channel enhanced attention module (CEAM) at the end of each branch to make them focus on the lesion areas. Finally, we concatenate the two branches and output the classified results through fully connected layers. Results: The proposed method achieves 88.03% overall accuracy with AUC of 94.27% and specificity of 90.75% in 10-fold cross-validation, which is better than the current non-invasive methods for determining KRAS mutation status. Conclusions: We believe that the proposed method can assist physicians to diagnose the KRAS mutation status in patients with colorectal cancer, and other medical problems can benefit from the spatial and frequency domains information.