We retrospectively analysed 106 consecutive traumatic humeral shaft fractures over a five-year period. The mechanism of injury, age, gender, fracture types, associated injury and the presence of injury to the radial nerve were reviewed. The incidence was about 10 per 100,000 per year; most were closed fractures in young males which had been sustained as a result of traffic accidents. The age-gender distribution was characterised by gradually increased incidence from the fifth decade in women, while it reached a peak at the third decade and decreased after the fifth decade in men. The results revealed different epidemiological features from previous studies. The epidemiology differs between ethnicity and country, and updating the epidemiological features of humeral shaft fractures may provide information for appropriate treatment programmes. This study documents the epidemiology of humeral shaft fracture in Taiwan, probably for the first time in this Asian community.
Osteosarcoma is characterized by a high malignant and metastatic potential. The chemokine stromal-derived factor-1alpha (SDF-1alpha) and its receptor, CXCR4, play a crucial role in adhesion and migration of human cancer cells. Integrins are the major adhesive molecules in mammalian cells, and has been associated with metastasis of cancer cells. Here, we found that human osteosarcoma cell lines had significant expression of SDF-1 and CXCR4 (SDF-1 receptor). Treatment of osteosarcoma cells with SDF-1alpha increased the migration and cell surface expression of alphavbeta3 integrin. CXCR4-neutralizing antibody, CXCR4 specific inhibitor (AMD3100) or small interfering RNA against CXCR4 inhibited the SDF-1alpha-induced increase the migration and integrin expression of osteosarcoma cells. Pretreated of osteosarcoma cells with MAPK kinase (MEK) inhibitor PD98059 inhibited the SDF-1alpha-mediated migration and integrin expression. Stimulation of cells with SDF-1alpha increased the phosphorylation of MEK and extracellular signal-regulating kinase (ERK). In addition, NF-kappaB inhibitor (PDTC) or IkappaB protease inhibitor (TPCK) also inhibited SDF-1alpha-mediated cell migration and integrin up-regulation. Stimulation of cells with SDF-1alpha induced IkappaB kinase (IKKalpha/beta) phosphorylation, IkappaB phosphorylation, p65 Ser(536) phosphorylation, and kappaB-luciferase activity. Furthermore, the SDF-1alpha-mediated increasing kappaB-luciferase activity was inhibited by AMD3100, PD98059, PDTC and TPCK or MEK1, ERK2, IKKalpha and IKKbeta mutants. Taken together, these results suggest that the SDF-1alpha acts through CXCR4 to activate MEK and ERK, which in turn activates IKKalpha/beta and NF-kappaB, resulting in the activations of alphavbeta3 integrins and contributing the migration of human osteosarcoma cells.
The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the features were selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy. His research interests include web technology, pattern recognition, and applied soft computing and network security. Kai-FanCheng received the University degree from the department of Information Management of the Overseas Chinese College of Commerce, Taichung, Taiwan, in 2007. He is a Graduate student from the department of Information Management at Chaoyang University of Technology, Taichung, Taiwan. His research interests include the security issue (intrusion detection in particular) of ad hoc networks and wired networks. Chia-Fen Hsieh received the Graduate degree from department of Information Management at Chaoyang University of Technology in 2008. He is a candidate for doctor's degree from graduate institute of informatics at Chaoyang University of Technology, Taichung, Taiwan. His research interests include the security issue (intrusion detection in particular) of wireless sensor networks, ad hoc networks and wired networks.
This 12-year interval retrospective study revealed modern epidemiologic results for FES in long bone fracture. Compared with the available literature in the recent decade, the incidence of FES in long bone fracture in our institution is less and the mortality rate is similar.
The adverse effects of the COVID-19 vaccine have been discovered as the rapid application of the vaccines continues. Neurological complications such as transverse myelitis raise concerns as cases were observed in clinical trials. Transverse myelitis is a rare immune-mediated disease with spinal cord neural injury, resulting in neurological deficits in the motor, sensory, and autonomic system. Vaccine-related transverse myelitis is even rarer. We present a case of acute transverse myelitis after vaccination against COVID-19 with the ChAdOx1 nCOV-19 vaccine (AZD1222), which was the first case reported in Taiwan. Although it rarely occurs, post-vaccination neurological complications should not be ignored. As the pandemic of SARS-COV-2 continues to spread and concern about vaccination efficacy and safety rises, heterologous vaccination were implemented in health public policy in several countries. A literature review of several clinical trials shows promising effects of mix-and-match vaccination. Further study on different combinations of vaccines can be expected.
To investigate the physiological responses of poplars to amino acids as sole nitrogen (N) sources, Populus × canescens (Ait.) Smith plants were supplied with one of three nitrogen fertilizers (NH4NO3, phenylalanine (Phe) or the mixture of NH4NO3 and Phe) in sand culture. A larger root system, and decreased leaf size and CO2 assimilation rate was observed in Phe- versus NH4NO3-treated poplars. Consistently, a greater root biomass and a decreased shoot growth were detected in Phe-supplied poplars. Decreased enzymatic activities of nitrate reductase (NR), glutamate synthase (GOGAT) and glutamate dehydrogenase (GDH) and elevated activities of nitrite reductase (NiR), phenylalanine ammonia lyase (PAL), glutamine synthetase (GS) and asparagine synthase (AS) were found in Phe-treated roots. Accordingly, reduced concentrations of NH4+, NO3- and total N, and enhanced N-use efficiencies (NUEs) were detected in Phe-supplied poplars. Moreover, the transcript levels of putative Phe transporters ANT1 and ANT3 were upregulated, and the mRNA levels of NR, glutamine synthetase 2 (GS2), NADH-dependent glutamate synthase (NADH-GOGAT), GDH and asparagine synthetase 2 (ASN2) were downexpressed in Phe-treated roots and/or leaves. The 15N-labeled Phe was mainly allocated in the roots and only a small amount of 15N-Phe was translocated to poplar aerial parts. These results indicate that poplar roots can acquire Phe as an N source to support plant growth and that Phe-induced NUEs in the poplars are probably associated with NH4+ re-utilization after Phe deamination and the carbon bonus simultaneously obtained during Phe uptake.
The extreme learning machine (ELM) is a type of machine learning algorithm for training a single hidden layer feedforward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of the hidden layer can be calculated by the least squares method. The efficient learning ability in ELM makes it widely applicable in classification, regression, and more. However, owing to some unutilized information in the residual, there are relatively huge prediction errors involving ELM. In this paper, a deep residual compensation extreme learning machine model (DRC‐ELM) of multilayer structures applied to regression is presented. The first layer is the basic ELM layer, which helps in obtaining an approximation of the objective function by learning the characteristics of the sample. The other layers are the residual compensation layers in which the learned residual is corrected layer by layer to the predicted value obtained in the previous layer by constructing a feature mapping between the input layer and the output of the upper layer. This model is applied to two practical problems: gold price forecasting and airfoil self‐noise prediction. We used the DRC‐ELM with 50, 100, and 200 residual compensation layers respectively for experiments, which show that DRC‐ELM does better in generalization and robustness than classical ELM, improved ELM models such as GA‐RELM and OS‐ELM, and other traditional machine learning algorithms such as support vector machine (SVM) and back‐propagation neural network (BPNN).
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