Supported 3-D Pt nanostructures: the straightforward synthesis and enhanced electrochemical performance for methanol oxidation in an acidic medium

Noble metal nanostructures with branched morphologies [i.e., 3-D Pt nanoflowers (NFs)] by tridimensionally integrating onto conductive carbon materials are proved to be an efficient and durable electrocatalysts for methanol oxidation. The well-supported 3-D Pt NFs are readily achieved by an efficient cobalt-induced/carbon-mediated galvanic reaction approach. Due to the favorable nanostructures (3-D Pt configuration allowing a facile mass transfer) and supporting effects (including framework stabilization, spatially separate feature, and improved charge transport effects), these 3-D Pt NFs manifest much higher electrocatalytic activity and stability toward methanol oxidation than that of the commercial Pt/C and Pt-based electrocatalysts.Web of Scienc

Long-term postoperative quality of life in childhood survivors with cerebellar mutism syndrome

BackgroundTo investigate the long-term quality of life (QoL) of children with cerebellar mutism syndrome (CMS) and explore the risk factors for a low QoL.ProcedureThis cross-sectional study investigated children who underwent posterior fossa surgery using an online Pediatric Quality of Life Inventory questionnaire. CMS and non-CMS patients were included to identify QoL predictors.ResultsSixty-nine patients were included (male, 62.3%), 22 of whom had CMS. The mean follow-up time was 45.2 months. Children with CMS had a significantly lower mean QoL score (65.3 vs. 83.7, p < 0.001) and subdomain mean scores (physical; 57.8 vs. 85.3, p < 0.001; social: 69.5 vs. 85.1, p = 0.001; academic: p = 0.001) than those without CMS, except for the emotional domain (78.0 vs. 83.7, p = 0.062). Multivariable analysis revealed that CMS (coefficient = −14.748.61, p = 0.043), chemotherapy (coefficient = −7.629.82, p = 0.013), ventriculoperitoneal (VP) shunt placement (coefficient = −10.14, p = 0.024), and older age at surgery (coefficient = −1.1830, p = 0.007) were independent predictors of low total QoL scores. Physical scores were independently associated with CMS (coefficient = −27.4815.31, p = 0.005), VP shunt placement (coefficient = −12.86, p = 0.025), and radiotherapy (coefficient = −13.62, p = 0.007). Emotional score was negatively associated with age at surgery (coefficient = −1.92, p = 0.0337) and chemotherapy (coefficient = −9.11, p = 0.003). Social scores were negatively associated with male sex (coefficient = −13.68, p = 0.001) and VP shunt placement (coefficient = −1.36, p = 0.005), whereas academic scores were negatively correlated with chemotherapy (coefficient = −17.45, p < 0.001) and age at surgery (coefficient = −1.92, p = 0.002). Extent of resection (coefficient = 13.16, p = 0.021) was a good predictor of higher academic scores.ConclusionCMS results in long-term neurological and neuropsychological deficits, negatively affecting QoL, and warranting early rehabilitation

Epidemiological characteristics, clinical presentations, and prognoses of pediatric brain tumors: Experiences of national center for children’s health

BackgroundWe aimed to describe the epidemiological characteristics, clinical presentations, and prognoses in a national health center for children.MethodsFrom January 2015 to December 2020, 484 patients aged 0-16 years, who were diagnosed with brain tumors and received neurosurgery treatment, were enrolled in the study. Pathology was based on the World Health Organization 2021 nervous system tumor classification, and tumor behaviors were classified according to the International Classification of Diseases for Oncology, third edition.ResultsAmong the 484 patients with brain tumors, the median age at diagnosis was 4.62 [2.19, 8.17] years (benign tumors 4.07 [1.64, 7.13] vs. malignant tumors 5.36 [2.78, 8.84], p=0.008). The overall male-to-female ratio was 1.33:1(benign 1.09:1 vs. malignant 1.62:1, p=0.029). Nausea, vomiting, and headache were the most frequent initial symptoms. The three most frequent tumor types were embryonal tumors (ET, 22.8%), circumscribed astrocytic gliomas (20.0%), and pediatric-type diffuse gliomas (11.0%). The most common tumor locations were the cerebellum and fourth ventricle (38.67%), the sellar region (22.9%) and ventricles (10.6%). Males took up a higher proportion than females in choroid plexus tumors (63.6%), ET (61.1%), ependymal tumors (68.6%), and germ cell tumors (GCTs, 78.1%). Patients were followed for 1 to 82 months. The overall 5-year survival rate was 77.5%, with survival rates of 91.0% for benign tumors and 64.6% for malignant tumors.ConclusionBrain tumors presented particularly sex-, age-, and regional-dependent epidemiological characteristics. Our results were consistent with previous reports and might reflect the real epidemiological status in China

A Self-Learning Worm Using Importance Scanning

The use of side information by an attacker can help a worm speed up the propagation. This philosophy has been the basis for advanced worm scanning mechanisms such as hitlist scanning, routable scanning, and importance scanning. Some of these scanning methods use information on vulnerable hosts. Such information, however, may not be easy to collect before a worm is released. Questions then arise whether and how a worm can self-learn and use such information while propagating, and how virulent the resulting worm may be. In this paper, we design a self-learning worm using importance scanning. An optimal yet practical importancescanning strategy is derived based on a new metric. A selflearning worm is demonstrated to have the ability to accurately estimate the underlying vulnerable-host distribution if a su#cient number of infected hosts are observed. Experimental results based on parameters chosen from Code Red show that after accurately estimating the distribution of vulnerable hosts, a self-learning worm can spread much faster than a random-scanning worm, a permutation-scanning worm, and a Class A routing worm. Some guidelines for detecting and defending against such self-learning worms are also discussed

Spatial-temporal modeling of malware propagation in networks

Abstract—Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation. Index Terms—graphical models, malware, modeling, security, stochastic processes

Improved Video Anomaly Detection with Dual Generators and Channel Attention

Video anomaly detection is a crucial aspect of understanding surveillance videos in real-world scenarios and has been gaining attention in the computer vision community. However, a significant challenge is that the training data only include normal events, making it difficult for models to learn abnormal patterns. To address this issue, we propose a novel dual-generator generative adversarial network method that improves the model’s ability to detect unknown anomalies by learning the anomaly distribution in advance. Our approach consists of a noise generator and a reconstruction generator, where the former focuses on generating pseudo-anomaly frames and the latter aims to comprehensively learn the distribution of normal video frames. Furthermore, the integration of a second-order channel attention module enhances the learning capacity of the model. Experiments on two popular datasets demonstrate the superiority of our proposed method and show that it can effectively detect abnormal frames after learning the pseudo-anomaly distribution in advance