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General General

Identification of key proteins involved in stickleback environmental adaptation with system-level analysis.

In Physiological genomics

Using abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish's population and morphotype. We identified robust and accurate fingerprints that classify environmental salinity, temperature, morphotype and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments. Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e. the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis. The strength of these analyses is that the results are purely data-driven. With such an unbiased approach applied on a large proteomic dataset, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.

Hall Martina, Kültz Dietmar, Almaas Eivind


Environmental adaptation, Machine Learning, Network Biology

General General

Comparison of Automated Acoustic Methods for Oral Diadochokinesis Assessment in Amyotrophic Lateral Sclerosis.

In Journal of speech, language, and hearing research : JSLHR

Purpose The purpose of this research note is to provide a performance comparison of available algorithms for the automated evaluation of oral diadochokinesis using speech samples from patients with amyotrophic lateral sclerosis (ALS). Method Four different algorithms based on a wide range of signal processing approaches were tested on a sequential motion rate /pa/-/ta/-/ka/ syllable repetition paradigm collected from 18 patients with ALS and 18 age- and gender-matched healthy controls (HCs). Results The best temporal detection of syllable position for a 10-ms tolerance value was achieved for ALS patients using a traditional signal processing approach based on a combination of filtering in the spectrogram, Bayesian detection, and polynomial thresholding with an accuracy rate of 74.4%, and for HCs using a deep learning approach with an accuracy rate of 87.6%. Compared to HCs, a slow diadochokinetic rate (p < .001) and diadochokinetic irregularity (p < .01) were detected in ALS patients. Conclusions The approaches using deep learning or multiple-step combinations of advanced signal processing methods provided a more robust solution to the estimation of oral DDK variables than did simpler approaches based on the rough segmentation of the signal envelope. The automated acoustic assessment of oral diadochokinesis shows excellent potential for monitoring bulbar disease progression in individuals with ALS.

Novotny Michal, Melechovsky Jan, Rozenstoks Kriss, Tykalova Tereza, Kryze Petr, Kanok Martin, Klempir Jiri, Rusz Jan


General General

Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.

In Computer methods in biomechanics and biomedical engineering

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.

Çınar Ahmet, Tuncer Seda Arslan


CNN, Electrocardiography, LSTM, arrhythmia, congestive heart failure, normal sinus rhythm

General General

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery.

In The journal of physical chemistry letters ; h5-index 129.0

Theoretical structure prediction method via quantum mechanical atomistic simulations such as density functional theory (DFT), solely based on chemical composition, already becomes a routine tool to determine the structures of physical and chemical systems, e.g. solids and clusters. However, the application of DFT to more realistic simulations, to a large extent, is impeded owing to the unfavourable scaling of the computational cost with respective to the system size. During recent years, machine learning potential (MLP) method has been rapidly rising as an accurate and efficient tool for atomistic simulations. In this Perspective, we provide an introduction on the basic principles and advantages for the combination of structure prediction and MLP, as well as challenges and opportunities along this promising direction.

Tong Qunchao, Gao Pengyue, Liu Hanyu, Xie Yu, Lv Jian, Wang Yanchao, Zhao Jijun


General General

Technological advancements and opportunities in Neuromarketing: a systematic review.

In Brain informatics

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.

Rawnaque Ferdousi Sabera, Rahman Khandoker Mahmudur, Anwar Syed Ferhat, Vaidyanathan Ravi, Chau Tom, Sarker Farhana, Mamun Khondaker Abdullah Al


Brain computer interface, Machine learning algorithm, Marketing, Neural recording, Neuromarketing

General General

Accurate detection of lameness in dairy cattle with computer vision: A new and individualized detection strategy based on the analysis of the supporting phase.

In Journal of dairy science

Lameness has a considerable influence on the welfare and health of dairy cows. Many attempts have been made to develop automatic lameness detection systems using computer vision technology. However, these detection methods are easily affected by the characteristics of individual cows, resulting in inaccurate detection of lameness. Therefore, this study explores an individualized lameness detection method for dairy cattle based on the supporting phase using computer vision. This approach is applied to eliminate the influence of the characteristics of individual cows and to detect lame cows and lame hooves. In this paper, the correlation coefficient between lameness and the supporting phase is calculated, a lameness detection algorithm based on the supporting phase is proposed, and the accuracy of the algorithm is verified. Additionally, the reliability of this method using computer vision technology is verified based on deep learning. One hundred naturally walking cows are selected from video data for analysis. The results show that the correlation between lameness and the supporting phase was 0.864; 96% of cows were correctly classified, and 93% of lame hooves were correctly detected using the supporting phase-based lameness detection algorithm. The mean average precision is 87.0%, and the number of frames per second is 83.3 when the Receptive Field Block Net Single Shot Detector deep learning network was used to detect the locations of cow hooves in the video. The results show that the supporting phase-based lameness detection method proposed in this paper can be used for the detection and classification of cow lameness and the detection of lame hooves with high accuracy. This approach eliminates the influence of individual cow characteristics and could be integrated into an automatic detection system and widely applied for the detection of cow lameness.

Kang X, Zhang X D, Liu G


computer vision technology, dairy cattle, deep learning, lameness detection, supporting phase