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Background Conventional magnetic resonance imaging (MRI) is adversely affected by thick slices, small intersection gaps, and the partial volume effect, leading to the missed diagnosis or misdiagnosis of pituitary micro-lesions. Purpose To evaluate the diagnostic yield of three-dimensional sampling perfection with application-optimized contrasts using different flip-angle evolutions (3D-T2 SPACE) sequences compared with a standard MRI protocol for the diagnosis of pituitary micro-lesions. Material and Methods The MRI findings of 664 patients with clinically suspected pituitary lesions were retrospectively analyzed. All patients underwent coronal 3D-T2 SPACE sequences followed by T1-weighted (T1W) imaging. Conventional scanning sequences included coronal and sagittal T1W imaging and post-contrast enhanced coronal and sagittal T1 imaging. All images were independently evaluated by two experienced neuroradiologists. The inter-observer agreement was analyzed using kappa statistics. Results Compared with conventional sequences, there was an increase in diagnostic confidence of 60.3% for the diagnosis of pituitary micro-lesions with the addition of 3D-T2 SPACE sequences. The lesion conspicuity scores of combined conventional and 3D-T2 SPACE sequences were significantly higher than those of conventional imaging (z = -6.403, P < 0.01) and 3D-T2 SPACE sequences (z = -4.243, P < 0.01). In addition, the inter-observer agreement of 3D-T2 SPACE sequences was good (κ = 0.826). Conclusion Combined with routine sequences, post-contrast enhanced 3D-T2 SPACE sequences effectively improve diagnostic confidence in the diagnosis of pituitary micro-lesions. Post-contrast enhanced 3D-T2 SPACE is suitable for detecting pico-adenomas, micro-lesions adjacent to the cavernous sinuses or sellar floor, lesions between the anterior and posterior lobes, and lesions with early phase enhancement.

With the trend of digital transformation of enterprises, the use of Internet of Things (IoT) devices is increasing. IoT devices that are not protected by security measures have gradually become targets of attackers. Attackers use weak passwords and software vulnerabilities in the device to invade the device and control it to become a node of the botnet. The Mozi botnet was discovered in December 2019, and its attention has increased day by day, and its influence once exceeded Mirai. After a preliminary reverse analysis of the Mozi samples, we have continued to track the development and changes of the Mozi botnet since February 2021. First, through the in-depth analysis of the communication principles of the Mozi botnet and the distributed sloppy hash table protocol, we have proposed an in-depth analysis of the Mozi botnet. The active detection method of Mozi, through daily and continuous tracking of the number of Mozi nodes, is infinitely close to the boundary of the Mozi network. On the basis of the collected detection data, we give our conclusions on Mozi's node size, global geographic distribution, 24-hour global activity, equipment composition, and Mozi botnet countermeasures.Through this study, we found that the security of IoT devices around the world is not optimistic, and there is an urgent need to increase the security protection

Electrochemical detection of dopamine (DA) in the presence of a large excess of ascorbic acid (AA) was investigated with a novel all-carbon nanocomposite film of C 60 -MWCNTs (C 60 -functionalized multi-walled carbon nanotubes) using a bare MWCNTs film as control. Although both films can selectively detect DA from AA by separating their oxidation potentials, the C 60 -MWCNTs film shows special selectivity and good sensitivity for detecting DA. On one hand, the C 60 -MWCNTs composite film shows a higher activity for DA oxidation with enhanced peak current. On the other hand, the C 60 -MWCNTs composite film effectively suppresses the oxidation of AA. Remarkably, it is found that the oxidation current of DA is over 2 times higher than that of AA even when the concentration of AA is about 3 to 4 orders of magnitude higher than that of DA. This offers a tremendous advantage for the simple and clean detection of DA free of the interfering AA signal in a real assay. Cyclic voltammetry, differential pulse voltammetry, and electrochemical impedance spectrometry are used to characterize the C 60 -MWCNTs composite film. These novel properties are interpreted to arise from the facile electron transfer between C 60 and MWCNTs in the C 60 -MWCNTs nanocomposite film.

Graph neural networks (GNN) have been widely used in many machine learning tasks, such as text classification, sequence labeling, protein interface prediction, and knowledge graph. With increasing security concerns, GNN have been proved to be vulnerable and unreliable. Recent years, inspired by adversarial model in computer vision, various attacks on graph data begin to emerge. However, the exist attacks mostly focus on security violation and attack specificity, and very few attacks concern error specificity. In this paper, we focus on dealing with this kind of attack on one node of the graph by slightly manipulating the graph structure. Our goal is to change the label of the node to what we want after attack. We formulate this case as label specificity attack problem. The biggest challenge in solving this problem is the lack of theoretical guidance to perform this attack. For this, we reinterpret structural entropy and define differential structural entropy (DS-entropy) to guide the manipulation. Based on DS-entropy, we propose the target-label principle and max-degree principle to execute our attack, and then design the corresponding algorithm DSEM. We compare our algorithm DSEM with two benchmarks on three classical graph data

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