Estimation of Joint Angle Based on Surface Electromyogram Signals Recorded at Different Load Levels

To control upper-limb exoskeletons and prostheses, surface electromyogram (sEMG) is widely used for estimation of joint angles. However, the variations in the load carried by the user can substantially change the recorded sEMG and consequently degrade the accuracy of joint angle estimation. In this paper, we aim to deal with this problem by training classification models using a pool of sEMG data recorded from all different loads. The classification models are trained as either subject-specific or subject-independent, and their results are compared with the performance of classification models that have information about the carried load. To evaluate the proposed system, the sEMG signals are recorded during elbow flexion and extension from three participants at four different loads (i.e. 1, 2, 4 and 6 Kg) and six different angles (i.e. 0, 30, 60, 90, 120, 150 degrees). The results show while the loads were assumed unknown and the applied training data was relatively small, the proposed joint angle estimation model performed significantly above the chance level in both the subject-specific and subject-independent models. However, transferring from known to unknown load in the subject-specific classifiers leads to 20% to 32% loss in the average accuracy

Novel Tumor Suppressor Function of Glucocorticoid-Induced TNF Receptor GITR in Multiple Myeloma

Glucocorticoid-induced TNF receptor (GITR) plays a crucial role in modulating immune response and inflammation, however the role of GITR in human cancers is poorly understood. In this study, we demonstrated that GITR is inactivated during tumor progression in Multiple Myeloma (MM) through promoter CpG island methylation, mediating gene silencing in primary MM plasma cells and MM cell lines. Restoration of GITR expression in GITR deficient MM cells led to inhibition of MM proliferation in vitro and in vivo and induction of apoptosis. These findings were supported by the presence of induction of p21 and PUMA, two direct downstream targets of p53, together with modulation of NF-κB in GITR-overexpressing MM cells. Moreover, the unbalanced expression of GITR in clonal plasma cells correlated with MM disease progression, poor prognosis and survival. These findings provide novel insights into the pivotal role of GITR in MM pathogenesis and disease progression

Novel Li3ClO based glasses with superionic properties for lithium batteries

Three types of next generation batteries are currently being envisaged among the international community: metal-air batteries, multivalent cation batteries and all-solid-state batteries. These battery designs require high-performance, safe and cost effective electrolytes that are compatible with optimized electrode materials. Solid electrolytes have not yet been extensively employed in commercial batteries as they suffer from poor ionic conduction at acceptable temperatures and insufficient stability with respect to lithium-metal. Here we show a novel type of glasses, which evolve from an antiperovskite structure and that show the highest ionic conductivity ever reported for the Li-ion (25 mS cm-1 at 25 °C). These glassy electrolytes for lithium batteries are inexpensive, light, recyclable, non-flammable and non-toxic. Moreover, they present a wide electrochemical window (higher than 8 V) and thermal stability within the application range of temperatures

Efficacy and epigenetic interactions of novel DNA hypomethylating agent guadecitabine (SGI-110) in preclinical models of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a deadly malignancy characterized at the epigenetic level by global DNA hypomethylation and focal hypermethylation on the promoter of tumor suppressor genes. In most cases it develops on a background of liver steatohepatitis, fibrosis, and cirrhosis. Guadecitabine (SGI-110) is a second-generation hypomethylating agent, which inhibits DNA methyltransferases. Guadecitabine is formulated as a dinucleotide of decitabine and deoxyguanosine that is resistant to cytidine deaminase (CDA) degradation and results in prolonged in vivo exposure to decitabine following small volume subcutaneous administration of guadecitabine. Here we found that guadecitabine is an effective demethylating agent and is able to prevent HCC progression in pre-clinical models. In a xenograft HCC HepG2 model, guadecitabine impeded tumor growth and inhibited angiogenesis, while it could not prevent liver fibrosis and inflammation in a mouse model of steatohepatitis. Demethylating efficacy of guadecitabine on LINE-1 elements was found to be the highest 8 d post-infusion in blood samples of mice. Analysis of a panel of human HCC vs. normal tissue revealed a signature of hypermethylated tumor suppressor genes (CDKN1A, CDKN2A, DLEC1, E2F1, GSTP1, OPCML, E2F1, RASSF1, RUNX3, and SOCS1) as detected by methylation-specific PCR. A pronounced demethylating effect of guadecitabine was obtained also in the promoters of a subset of tumor suppressors genes (CDKN2A, DLEC1, and RUNX3) in HepG2 and Huh-7 HCC cells. Finally, we analyzed the role of macroH2A1, a variant of histone H2A, an oncogene upregulated in human cirrhosis/HCC that synergizes with DNA methylation in suppressing tumor suppressor genes, and it prevents the inhibition of cell growth triggered by decitabine in HCC cells. Guadecitabine, in contrast to decitabine, blocked growth in HCC cells overexpressing macroH2A1 histones and with high CDA levels, despite being unable to fully demethylate CDKN2A, RUNX3, and DLEC1 promoters altered by macroH2A1. Collectively, our findings in human and mice models reveal novel epigenetic anti-HCC effects of guadecitabine, which might be effective specifically in advanced states of the disease

Dissociation constants and thermodynamic properties of amino acids used in CO2 absorption from (293 to 353) K

The second dissociation constants of the amino acids βalanine, taurine, sarcosine, 6-aminohexanoic acid, DL-methionine, glycine, L-phenylalanine, and L-proline and the third dissociation constants of L-glutamic acid and L-aspartic acid have been determined from electromotive force measurements at temperatures from (293 to 353) K. Experimental results are reported and compared to literature values. Values of the standard state thermodynamic properties are derived from the experimental results and compared to the values of commercially available amines used as absorbents for CO 2 capture.

Machine learning based botnet identification traffic

The continued growth of the Internet has resulted in the increasing sophistication of toolkit and methods to conduct computer attacks and intrusions that are easy to use and publicly available to download, such as Zeus botnet toolkit. Botnets are responsible for many cyber-attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of existence botnet toolkits release updates for new features, development and support. This presents challenges in the detection and prevention of bots. Current botnet detection approaches mostly ineffective as botnets change their Command and Control (C&C) server structures, centralized (e.g., IRC, HTTP), distributed (e.g., P2P), and encryption deterrent. In this paper, based on real world data sets we present our preliminary research on predicting the new bots before they launch their attack. We propose a rich set of features of network traffic using Classification of Network Information Flow Analysis (CONIFA) framework to capture regularities in C&C communication channels and malicious traffic. We present a case study of applying the approach to a popular botnet toolkit, Zeus. The experimental evaluation suggest that it is possible to detect effectively botnets during the botnet C&C communication generated from new updated Zeus botnet toolkit by building the classifier using machine learning from an earlier version and before they launch their attacks using traffic behaviors. Also, show that there is similarity in C&C structures various Botnet toolkit versions and that the network characteristics of botnet C&C traffic is different from legitimate network traffic. Such methods could reduce many different resources needed to identify C&C communication channels and malicious traffic

Weighted multi-task learning in classification domain for improving brain-computer interface

One of the major limitations of brain computer interface (BCI) is its long calibration time. Due to between sessions/subjects nonstationarity, typically a big amount of training data needs to be collected at the beginning of each session in order to tune the parameters of the system for the target user. In this paper, a number of novel weighted multi-task transfer learning algorithms are proposed in the classification domain to reduce the calibration time without sacrificing the classification accuracy of the BCI system. The proposed algorithms use data from other subjects and combine them to estimate the classifier parameters for the target subject. This combination is done based on how similar the data from each subject is to the few trials available from the target subject. The proposed algorithms are evaluated using dataset 2a from BCI competition IV. According to the results, the proposed algorithms lead to reduce the calibration time by 75% and enhance the average classification accuracy at the same time

Weighted transfer learning for improving motor imagery-based brain-computer interface

One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this study, a new similarity measure based on the kullback leibler divergence (KL) is used to measure similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared to the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results particularly when few subject-specific trials were available for training (p<0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms