| 1. |
|
|
| 2. |
- Kehoe, Laura, et al.
(author)
-
Make EU trade with Brazil sustainable
- 2019
-
In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
-
Journal article (other academic/artistic)
|
|
| 3. |
- de Klerk, Nele, et al.
(author)
-
Lactobacilli Reduce Helicobacter pylori Attachment to Host Gastric Epithelial Cells by Inhibiting Adhesion Gene Expression
- 2016
-
In: Infection and Immunity. - 0019-9567 .- 1098-5522. ; 84:5, s. 1526-1535
-
Journal article (peer-reviewed)abstract
- The human gastrointestinal tract, including the harsh environment of the stomach, harbors a large variety of bacteria, of which Lactobacillus species are prominent members. The molecular mechanisms by which species of lactobacilli interfere with pathogen colonization are not fully characterized. In this study, we aimed to study the effect of lactobacillus strains upon the initial attachment of Helicobacter pylori to host cells. Here we report a novel mechanism by which lactobacilli inhibit adherence of the gastric pathogen H. pylori. In a screen with Lactobacillus isolates, we found that only a few could reduce adherence of H. pylori to gastric epithelial cells. Decreased attachment was not due to competition for space or to lactobacillus-mediated killing of the pathogen. Instead, we show that lactobacilli act on H. pylori directly by an effector molecule that is released into the medium. This effector molecule acts on H. pylori by inhibiting expression of the adhesin-encoding gene sabA. Finally, we verified that inhibitory lactobacilli reduced H. pylori colonization in an in vivo model. In conclusion, certain Lactobacillus strains affect pathogen adherence by inhibiting sabA expression and thereby reducing H. pylori binding capacity.
|
|
| 4. |
- Åberg, Anna, et al.
(author)
-
Helicobacter pylori adapts to chronic infection and gastric disease via ph-responsive baba-mediated adherence
- 2017
-
In: Cell Host and Microbe. - : Elsevier BV. - 1931-3128 .- 1934-6069. ; 21:3, s. 376-389
-
Journal article (peer-reviewed)abstract
- The BabA adhesin mediates high-affinity binding of Helicobacter pylori to the ABO blood group antigen-glycosylated gastric mucosa. Here we show that BabA is acid responsive-binding is reduced at low pH and restored by acid neutralization. Acid responsiveness differs among strains; often correlates with different intragastric regions and evolves during chronic infection and disease progression; and depends on pH sensor sequences in BabA and on pH reversible formation of high-affinity binding BabA multimers. We propose that BabA's extraordinary reversible acid responsiveness enables tight mucosal bacterial adherence while also allowing an effective escape from epithelial cells and mucus that are shed into the acidic bactericidal lumen and that bio-selection and changes in BabA binding properties through mutation and recombination with babA-related genes are selected by differences among individuals and by changes in gastric acidity over time. These processes generate diverse H. pylori subpopulations, in which BabA's adaptive evolution contributes to H. pylori persistence and overt gastric disease.
|
|
| 5. |
- Abbaspour Asadollah, Sara, et al.
(author)
-
Evaluation of surface EMG-based recognition algorithms for decoding hand movements
- 2019
-
In: Medical and Biological Engineering and Computing. - : Springer. - 0140-0118 .- 1741-0444. ; 58:1, s. 83-100
-
Journal article (peer-reviewed)abstract
- Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands. [Figure not available: see fulltext.].
|
|
| 6. |
- Abbaspour, Sara, et al.
(author)
-
A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet
- 2016
-
In: Journal of Electromyography & Kinesiology. - : Elsevier BV. - 1050-6411 .- 1873-5711. ; 26, s. 52-59
-
Journal article (peer-reviewed)abstract
- In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).
|
|
| 7. |
- Abbaspour, Sara, 1984-, et al.
(author)
-
ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA
- 2015
-
In: Studies in Health Technology and Informatics, Volume 211. - 9781614995159 ; , s. 91-97
-
Conference paper (peer-reviewed)abstract
- This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.
|
|
| 8. |
- Abbaspour, Sara, 1984-
(author)
-
Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition
- 2019
-
Doctoral thesis (other academic/artistic)abstract
- Losing a limb causes difficulties in our daily life. To regain the ability to live an independent life, artificial limbs have been developed. Hand prostheses belong to a group of artificial limbs that can be controlled by the user through the activity of the remnant muscles above the amputation. Electromyogram (EMG) is one of the sources that can be used for control methods for hand prostheses. Surface EMGs are powerful, non-invasive tools that provide information about neuromuscular activity of the subjected muscle, which has been essential to its use as a source of control for prosthetic limbs. However, the complexity of this signal introduces a big challenge to its applications. EMG pattern recognition to decode different limb movements is an important advancement regarding the control of powered prostheses. It has the potential to enable the control of powered prostheses using the generated EMG by muscular contractions as an input. However, its use has yet to be transitioned into wide clinical use. Different algorithms have been developed in state of the art to decode different movements; however, the challenge still lies in different stages of a successful hand gesture recognition and improvements in these areas could potentially increase the functionality of powered prostheses. This thesis firstly focuses on improving the EMG signal’s quality by proposing novel and advanced filtering techniques. Four efficient approaches (adaptive neuro-fuzzy inference system-wavelet, artificial neural network-wavelet, adaptive subtraction and automated independent component analysis-wavelet) are proposed to improve the filtering process of surface EMG signals and effectively eliminate ECG interferences. Then, the offline performance of different EMG-based recognition algorithms for classifying different hand movements are evaluated with the aim of obtaining new myoelectric control configurations that improves the recognition stage. Afterwards, to gain proper insight on the implementation of myoelectric pattern recognition, a wide range of myoelectric pattern recognition algorithms are investigated in real time. The experimental result on 15 healthy volunteers suggests that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) outperform other classifiers. The real-time investigation illustrates that in addition to the LDA and MLE, multilayer perceptron also outperforms the other algorithms when compared using classification accuracy and completion rate.
|
|
| 9. |
|
|
| 10. |
- Abbaspour, Sara, et al.
(author)
-
Evaluation of wavelet based methods in removing motion artifact from ECG signal
- 2015
-
In: IFMBE Proceedings. - Cham : Springer International Publishing. - 9783319129662 ; , s. 1-4
-
Conference paper (peer-reviewed)abstract
- Accurate recording and precise analysis of the electrocardiogram (ECG) signals are crucial in the pathophysiological study and clinical treatment. These recordings are often corrupted by different artifacts. The aim of this study is to propose two different methods, wavelet transform based on nonlinear thresholding and a combination method using wavelet and independent component analysis (ICA), to remove motion artifact from ECG signals. To evaluate the performance of the proposed methods, the developed techniques are applied to the real and simulated ECG data. The results of this evaluation are presented using quantitative and qualitative criteria. The results show that the proposed methods are able to reduce motion artifacts in ECG signals. Signal to noise ratio (SNR) of the wavelet technique is equal to 13.85. The wavelet-ICA method performed better with SNR of 14.23.
|
|
