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Abstract Purpose Bulimia nervosa (BN) is characterized by recurrent binge-eating episodes and inappropriate compensatory behaviors. This study investigated alterations in resting-state surface-based neural activity in BN patients and explored correlations between brain activity and eating behavior. Methods A total of 26 BN patients and 28 healthy controls were enrolled. Indirect measurement of cerebral cortical activity and functional connectivity (FC) analyses were performed in Surfstat. A principal component analysis (PCA) model was used to capture the commonalities within the behavioral questionnaires from the BN group. Results Compared with the healthy control group, the BN group showed decreased surface-based two-dimensional regional homogeneity in the right superior parietal lobule (SPL). Additionally, the BN group showed decreased FC between the right SPL and the bilateral lingual gyrus and increased FC between the right SPL and the left caudate nucleus and right putamen. In the FC–behavior association analysis, the second principal component (PC2) was negatively correlated with FC between the right SPL and the left caudate nucleus. The third principal component (PC3) was negatively correlated with FC between the right SPL and the left lingual gyrus and positively correlated with FC between the right SPL and the right lingual gyrus. Conclusion We revealed that the right SPL undergoes reorganization with respect to specific brain regions at the whole-brain level in BN. In addition, our results suggest a correlation between brain reorganization and maladaptive eating behavior. These findings may provide useful information to better understand the neural mechanisms of BN. Level of evidence V, descriptive study.

PurposeMinimal hepatic encephalopathy (MHE) is characterized by mild neuropsychological and neurophysiological alterations that are not detectable by routine clinical examination. Abnormal brain activity (in terms of the amplitude of low-frequency fluctuation (ALFF) has been observed in MHE patients. However, little is known concerning temporal dynamics of intrinsic brain activity. The present study aimed to investigate the abnormal dynamics of brain activity (dynamic ALFF; dALFF) and static measures [static ALFF; (sALFF)] in MHE patients and to strive for a reliable imaging neuromarkers for distinguishing MHE patients from cirrhosis patients. In addition, the present study also investigated whether intrinsic brain activity predicted the severity of liver damage.MethodsThirty-four cirrhosis patients with MHE, 28 cirrhosis patients without MHE, and 33 age-, sex-, and education-matched healthy controls (HCs) underwent resting-state magnetic resonance imaging (rs-fMRI). dALFF was estimated by combining the ALFF method with the sliding-window method, in which temporal variability was quantized over the whole-scan timepoints and then compared among the three groups. Additionally, dALFF, sALFF and both two features were utilized as classification features in a support vector machine (SVM) to distinguish MHE patients from cirrhosis patients. The severity of liver damage was reflected by the Child–Pugh score. dALFF, sALFF and both two features were used to predict Child–Pugh scores in MHE patients using a general linear model.ResultsCompared with HCs, MHE patients showed significantly increased dALFF in the left inferior occipital gyrus, right middle occipital gyrus, and right insula; increased dALFF was also observed in the right posterior lobe of the cerebellum (CPL) and right thalamus. Compared with HCs, noMHE patients exhibited decreased dALFF in the right precuneus. In contrast, compared with noMHE patients, MHE patients showed increased dALFF in the right precuneus, right superior frontal gyrus, and right superior occipital gyrus. Furthermore, the increased dALFF values in the left precuneus were positively associated with poor digit-symbol test (DST) scores (r = 0.356, p = 0.038); however, dALFF in the right inferior temporal gyrus (ITG) was negatively associated with the number connection test–A (NCT-A) scores (r = -0.784, p = 0.000). A significant positive correlation was found between dALFF in the left inferior occipital gyrus (IOG) and high blood ammonia levels (r = 0.424, p = 0.012). Notably, dALFF values yielded a higher classification accuracy than sALFF values in distinguishing MHE patients from cirrhosis patients. Importantly, the dALFF values predicted the Child–Pugh score (r = 0.140, p = 0.030), whereas sALFF values did not in the current dataset. Combining two features had high accuracy in classification in distinguishing MHE patients from cirrhotic patients and yielded prediction in the severity of liver damage.ConclusionThese findings suggest that combining dALFF and sALFF features is a useful neuromarkers for distinguishing MHE patients from cirrhosis patients and highlights the important role of dALFF feature in predicting the severity of liver damage in MHE.

The management of eating behavior in bulimia nervosa (BN) patients is a complex process, and BN involves activity in multiple brain regions that integrate internal and external functional information. This functional information integration occurs in brain regions involved in reward, cognition, attention, memory, emotion, smell, taste, vision and so on. Although it has been reported that resting-state brain activity in BN patients is different from that of healthy controls, the neural mechanisms remain unclear and need to be further explored. The fractional amplitude of low-frequency fluctuation (fALFF) analyses are an important data-driven method that can measure the relative contribution of low-frequency fluctuations within a specific frequency band to the whole detectable frequency range. The fALFF is well suited to reveal the strength of interregional cooperation at the single-voxel level to investigate local neuronal activity power. FC is a brain network analysis method based on the level of correlated dynamics between time series, which establishes the connection between two spatial regions of interest (ROIs) with the assistance of linear temporal correlation. Based on the psychological characteristics of patients with BN and the abnormal brain functional activities revealed by previous neuroimaging studies, in this study, we investigated alterations in regional neural activity by applying fALFF analysis and whole-brain functional connectivity (FC) in patients with BN in the resting state and to explore correlations between brain activities and eating behavior. We found that the left insula and bilateral inferior parietal lobule (IPL), as key nodes in the reorganized resting-state neural network, had altered FC with other brain regions associated with reward, emotion, cognition, memory, smell/taste, and vision-related functional processing, which may have influenced restrained eating behavior. These results could provide a further theoretical basis and potential effective targets for neuropsychological treatment in patients with BN.

The brain receives sensory information about food, evaluates its desirability and value, and responds with approach or withdrawal. The evaluation process of food in the brain with obesity may involve a variety of neurocircuit abnormalities in the integration of internal and external information processing. There is a lack of consistency of the results extant reported for aberrant changes in the brain with obesity that prohibits key brain alterations to be identified. Moreover, most studies focus on the observation of neural plasticity of function or structure, and the evidence for functional and structural correlations in the neuronal plasticity process of obesity is still insufficient. The aims of this article are to explore the key neural structural regions and the hierarchical activity pattern of key structural nodes and evaluate the correlation between changes in functional modulation and eating behavior. Forty-two participants with obesity and 33 normal-weight volunteers were recruited. Gray matter volume (GMV) and Granger causality analysis (GCA) were performed using the DPARSF, CAT12, and DynamicBC toolbox. Compared with the normal weight group, the obesity group exhibited significantly increased GMV in the left parahippocampal gyrus (PG). The obesity group showed decreased causal inflow to the left PG from the left orbitofrontal cortex (OFC), right calcarine, and bilateral supplementary motor area (SMA). Decreased causal outflow to the left OFC, right precuneus, and right SMA from the left PG, as well as increased causal outflow to the left middle occipital gyrus (MOG) were observed in the obesity group. Negative correlations were found between DEBQ-External scores and causal outflow from the left PG to the left OFC, and DEBQ-Restraint scores and causal inflow from the left OFC to the left PG in the obesity group. Positive correlation was found between DEBQ-External scores and causal outflow from the left PG to the left MOG. These results show that the increased GMV in the PG may play an important role in obesity, which may be related to devalued reward system, altered behavioral inhibition, and the disengagement of attentional and visual function for external signals. These findings have important implications for understanding neural mechanisms in obesity and developing individual-tailored strategies for obesity prevention.

The widespread use of software public component libraries increases the speed of software development while expanding the attack surface of software.Vulnerabilities that exist in public component libraries are widely distributed in software that uses the library files,and the compatibility,stability,and development delays make it difficult to fix such vulnerabilities and the patching period is long.Software component analysis is an important tool to solve such problems,but limited by the problem of ineffective feature selection and difficulties in extracting accurate features from public component libraries,the accuracy of component analysis is not high and generally stays at the level of kind location.In this paper,we propose a public component library feature extraction method based on cross-fingerprint analysis,build a fingerprint library based on 25 000 open source projects on GitHub platform,propose source string role classification,export function fingerprint analysis,binary compilation fingerprint analysis,etc.to extract cross-fingerprints of component libraries,realize the accurate localization of public component libraries,develop a prototype tool LVRecognizer,test and evaluate 516 real softwares,and obtain a accuracy rate of 94.74%.

Background and purpose: Oral mucosal malignant melanoma (OMM) is a highly malignant solid tumor with a distant metastasis rate of about 40%. The lung is the most common metastatic site. This study aimed to investigate the characteristics and prognostic analysis of OMM with lung metastasis, in order to find the best treatment mode for OMM with lung metastasis. Methods: The data of the patients with lung metastasis diagnosed in the Ninth People’s Hospital Affiliated to the Medical College of Shanghai Jiao Tong University from January 2017 to January 2021 were retrospectively analyzed. The imaging characteristics of chest computed tomography (CT) were summarized, and Kaplan-Meier method was used for survival analysis. Results: In this study, 88% of patients with OMM were diagnosed with lung metastasis within 2 years after operation, including 22 cases (52%) in the first year and 15 cases (36%) in the second year; 71% of patients showed multiple, round or oval nodules of different sizes on chest CT, and few single metastases (10%). Non-scheduled follow-up (P = 0.009), concurrent local recurrence (P = 0.037), concurrent pleural effusion (P = 0.042) and no immunotherapy (P = 0.000) could significantly reduce the survival time of patients. The response of patients with relapse to programmed death-1 (PD-1) immunotherapy was significantly reduced (P = 0.009), and the median overall survival (OS) of PD-1 single drug was only 10 months. After combination therapy with PD-1 and anti-vascular targeted drugs, the median OS could be increased to about 19 months (P = 0.019). Conclusion: OMM is prone to lung metastasis, and even tiny nodules less than 1cm can be metastatic foci. The metastasis of OMM most often occurs within 1-2 years after operation. Regular follow-up can detect early metastasis and significantly prolong the survival. The efficacy of immunotherapy alone for recurrent and metastatic OMM is also poor, and immunotherapy combined with anti-vascular targeted therapy is required.

China’s “carbon peaking and carbon neutrality” goal relies greatly on the low-carbon transition of the power system, but the existing research rarely explores the low-carbon transition of the regional power system. By using the intergovernmental panel on climate change (IPCC) greenhouse gas inventory compilation method and the network model analysis, the carbon emissions caused by the power generation and the power consumption in Guangdong-Hong Kong-Macao Greater Bay Area (the Greater Bay Area) was quantified. The logarithmic mean Divisia index (LMDI) method was used to quantify the influence of socio-economic factors on the electricity-related carbon emissions in the Greater Bay Area. The results show that Hong Kong and Macao have made slow progress in the low-carbon transition of the power system, and Guangdong’s share of the low-carbon power continues to increase. The rapidly expanding economic scale and the power demand were the most important drivers of the emissions growth in the Greater Bay Area. The low-carbon electricity imported from outside regions and the improved efficiency in the sectoral electricity consumption offset part of the emission growth.