Fabrication of multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors by using CF4 plasma treatment

Multianalyte CeO2 biosensors have been demonstrated to detect pH, glucose, and urine concentrations. To enhance the multianalyte sensing capability of these biosensors, CF4 plasma treatment was applied to create nanograin structures on the CeO2 membrane surface and thereby increase the contact surface area. Multiple material analyses indicated that crystallization or grainization caused by the incorporation of flourine atoms during plasma treatment might be related to the formation of the nanograins. Because of the changes in surface morphology and crystalline structures, the multianalyte sensing performance was considerably enhanced. Multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors exhibit potential for use in future biomedical sensing device applications

Design and Development of the Reactive BGP peering in Software-Defined Routing Exchanges

The Software-Defined Networking (SDN) is considered to be an improved solution for applying flexible control and operation recently in the network. Its characteristics include centralized management, global view, as well as fast adjustment and adaptation. Many experimental and research networks have already migrated to the SDN-enabled architecture. As the global network continues to grow in a fast pace, how to use SDN to improve the networking fields becomes a popular topic in research. One of the interesting topics is to enable routing exchanges among the SDN-enabled network and production networks. However, considering that many production networks are still operated on legacy architecture, the enabled SDN routing functionalities have to support hybrid mode in operation. In this paper, we propose a routing exchange mechanism by enabling reactive BGP peering actions among the SDN and legacy network components. The results of experiments show that our SDN controller is able to mask as an Autonomous System (AS) to exchange routing information with other BGP routers

Oxaliplatin-induced acquired long QT syndrome with torsades de pointes and myocardial injury in a patient with dilated cardiomyopathy and rectal cancer

AbstractA 67-year-old woman presented with a history of dilated cardiomyopathy with congestive heart failure since 2003, who subsequently developed lower rectal cancer (adenocarcinoma) with liver, bone, and lymph node metastasis. Abdominoperineal resection and hepatectomy were performed. The patient received two rounds of intravenous chemotherapy, including 12 and six courses of FOLFOX4 (5-fluorouracil, leucovorin, and oxaliplatin; 85 mg/m2 per cycle). She underwent a third round of intravenous FOLFOX4 because of tumor progression. During the 21st course of FOLFOX4 regimen, the patient developed ST segment depression in lead II and prolongation of QT interval with polymorphic ventricular tachycardia, torsades de pointes right after the start of oxaliplatin infusion. Immediate defibrillation and cardiopulmonary resuscitation were administered, and the patient regained spontaneous circulation and consciousness. Twelve-lead electrocardiogram showed ST segment elevation in III, aVF, and ST segment depression in V4–6 after resuscitation. To our knowledge, prolongation of QT interval with torsades de pointes and coronary spasm with myocardial injury that were stabilized in one patient following oxaliplatin infusion has not been reported. We present a patient with these rare complications

Learning Structural Kernels for Natural Language Processing

Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods

Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search

Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.g. FastText) is widely used for efficient online inference. However, the representation ability of the FastText model is insufficient, resulting in poor classification performance, especially on some low-frequency queries and tailed categories. Using a deeper and more complex model (e.g. BERT) is an effective solution, but it will cause a higher online inference latency and more expensive computing costs. Thus, how to juggle both inference efficiency and classification performance is obviously of great practical importance. To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints. Specifically, we propose to train an offline BERT model to retrieve more potentially relevant data. Benefiting from its powerful semantic representation, more relevant labels not exposed in the historical data will be added into the training set for better FastText model training. Moreover, a novel distribution-diverse multi-expert learning strategy is proposed to further improve the mining ability of relevant data. By training multiple BERT models from different data distributions, it can respectively perform better at high, middle, and low-frequency search queries. The model ensemble from multi-distribution makes its retrieval ability more powerful. We have deployed two versions of this framework in JD search, and both offline experiments and online A/B testing from multiple datasets have validated the effectiveness of the proposed approach