Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Bioinformatics
Title: User-level psychological stress detection from social media using deep neural network
Abstract: It is of significant importance to detect and manage stress before it turns into severe problems. However, existing stress detection methods usually rely on psychological scales or physiological devices, making the detection complicated and costly. In this paper, we explore to automatically detect individuals' psychological stress via social media. Employing real online micro-blog data, we first investigate the correlations between users' stress and their tweeting content, social engagement and behavior patterns. Then we define two types of stress-related attributes: 1) low-level content attributes from a single tweet, including text, images and social interactions; 2) user-scope statistical attributes through their weekly micro-blog postings, leveraging information of tweeting time, tweeting types and linguistic styles. To combine content attributes with statistical attributes, we further design a convolutional neural network (CNN) with cross autoencoders to generate user-scope content attributes from low-level content attributes. Finally, we propose a deep neural network (DNN) model to incorporate the two types of user-scope attributes to detect users' psychological stress. We test the trained model on four different datasets from major micro-blog platforms including Sina Weibo, Tencent Weibo and Twitter. Experimental results show that the proposed model is effective and efficient on detecting psychological stress from micro-blog data. We believe our model would be useful in developing stress detection tools for mental health agencies and individuals.
Extracted statement — Aims: Stress detection
Research field: Bioinformatics
Title: Psychological stress detection from cross-media microblog data using Deep Sparse Neural Network
Abstract: Long-term stress may lead to many severe physical and mental problems. Traditional psychological stress detection usually relies on the active individual participation, which makes the detection labor-consuming, time-costing and hysteretic. With the rapid development of social networks, people become more and more willing to share moods via microblog platforms. In this paper, we propose an automatic stress detection method from cross-media microblog data. We construct a three-level framework to formulate the problem. We first obtain a set of low-level features from the tweets. Then we define and extract middle-level representations based on psychological and art theories: linguistic attributes from tweets' texts, visual attributes from tweets' images, and social attributes from tweets' comments, retweets and favorites. Finally, a Deep Sparse Neural Network is designed to learn the stress categories incorporating the cross-media attributes. Experiment results show that the proposed method is effective and efficient on detecting psychological stress from microblog data.
Extracted statement — Aims: Stress detection
Research field: Bioinformatics
Title: Detecting Stress Based on Social Interactions in Social Networks
Abstract: Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users’ stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model - a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9 percent in F1-score. By further analyzing the social interaction data, we also discover several intriguing phenomena, i.e., the number of social structures of sparse connections (i.e., with no delta connections) of stressed users is around 14 percent higher than that of non-stressed users, indicating that the social structure of stressed users’ friends tend to be less connected and less complicated than that of non-stressed users.
Extracted statement — Aims: Stress detection
Research field: Bioinformatics
Title: The Facial Stress Recognition Based on Multi-histogram Features and Convolutional Neural Network
Abstract: The health disorders due to stress and depression should not be considered trivial because it has a negative impact on health. Prolonged stress not only triggers mental fatigue but also affects physical health. Therefore, we must be able to identify stress early. In this paper, we proposed the new methods for stress recognition on three classes (neutral, low stress, high stress) from a facial frontal image. Each image divided into three parts, i.e. pairs of eyes, nose, and mouth. Facial features have extracted on each image pixel using DoG, HOG, and DWT. The strength of orthonormality features is considered by the RICA. The GDA distributes the nonlinear covariance. Furthermore, the histogram features of the image parts are applied at a depth-based learning of ConvNet to model the facial stress expression. The proposed method is used FERET databases for training and validation. The k-fold validation method is used as a validation with k=5. Based on the experiments result, the proposed method accuracy showing outperforms compared with other works.
Extracted statement — Aims: Stress recognition
Research field: Bioinformatics
Title: Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network
Abstract: Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multi-class classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other well-known machine learning algorithms.
Extracted statement — Used models: support vector machine
Research field: Bioinformatics
Title: Implementing LOINC – Current Status and Ongoing Work at a Medical University
Abstract: The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the consortium of the HiGHmed project, LOINC is one of the central terminologies used for health data sharing across all university sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing efforts in implementing LOINC to our laboratory information system and research infrastructure, as well as our challenges and the lessons learned. 407 local terms could be mapped to 376 LOINC codes of which 209 are already available to routine laboratory data. In our experience, mapping of local terms to LOINC is a widely manual and time consuming process for reasons of language and expert knowledge of local laboratory procedures.
Extracted statement — Material: the central terminologies
Research field: Bioinformatics
Title: DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings
Abstract: We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost thermal camera, we track a person's breathing patterns as temperature changes around his/her nostril. The paper's technical contribution is threefold. First of all, instead of creating handcrafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexity of the breathing dynamics. Second, a spatial pattern analysis based on a deep Convolutional Neural Network (CNN) is directly applied to the spectrogram sequences without the need of hand-crafting features. Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e.g., up to a few hours). The model is trained and tested with data collected from people exposed to two types of cognitive tasks (Stroop Colour Word Test, Mental Computation test) with sessions of different difficulty levels. Using normalised self-report as ground truth, the CNN reaches 84.59% accuracy in discriminating between two levels of stress and 56.52% in discriminating between three levels. In addition, the CNN outperformed powerful shallow learning methods based on a single layer neural network. Finally, the dataset of labelled thermal images will be open to the community.
Extracted statement — Data: Thermal images
Research field: Bioinformatics
Title: Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics
Abstract: As a severe psychiatric disorder disease, depression is a state of low mood and aversion to activity, which prevents a person from functioning normally in both work and daily lives. The study on automated mental health assessment has been given increasing attentions in recent years. In this paper, we study the problem of automatic diagnosis of depression. A new approach to predict the Beck Depression Inventory II (BDI-II) values from video data is proposed based on the deep networks. The proposed framework is designed in a two stream manner, aiming at capturing both the facial appearance and dynamics. Further, we employ joint tuning layers that can implicitly integrate the appearance and dynamic information. Experiments are conducted on two depression databases, AVEC2013 and AVEC2014. The experimental results show that our proposed approach significantly improve the depression prediction performance, compared to other visual-based approaches.
Extracted statement — Data: Video data
Research field: Bioinformatics
Title: Biotea-2-Bioschemas, facilitating structured markup for semantically annotated scholarly publications
Abstract: The total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata. Schema.org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results. Bioschemas is based on the standards of schema.org, providing new types, properties and guidelines for metadata, i.e., providing metadata profiles tailored to the Life Sciences domain. Here we present our proposed contribution to Bioschemas (from the project “Biotea”), which supports metadata contributions for scholarly publications via profiles and web components. Biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our Biotea model has been mapped to schema.org following Bioschemas standards.
Extracted statement — programming language: Web components
Research field: Biomedical Engineering and Bioengineering
Title: Tensor gradient based discriminative region analysis for cognitive state classification
Abstract: Extraction of relevant features from high-dimensional multi-way functional MRI (fMRI) data is essential for the classification of a cognitive task. In general, fMRI records a combination of neural activation signals and several other noisy components. Alternatively, fMRI data is represented as a high dimensional array using a number of voxels, time instants, and snapshots. The organisation of fMRI data includes a number of Region Of Interests (ROI), snapshots, and thousand of voxels. The crucial step in cognitive task classification is a reduction of feature size through feature selection. Extraction of a specific pattern of interest within the noisy components is a challenging task. Tensor decomposition techniques have found several applications in the scientific fields. In this paper, a novel tensor gradient-based feature extraction technique for cognitive task classification is proposed. The technique has efficiently been applied on StarPlus fMRI data. Also, the technique has been used to discriminate the ROIs in fMRI data in terms of cognitive state classification. The method has been achieved a better average accuracy when compared to other existing feature extraction methods.
Extracted statement — dataset: StarPlus fMRI data
Research field: Biophysics
Title: Differential Interaction of Antimicrobial Peptides with Lipid Structures Studied by Coarse-Grained Molecular Dynamics Simulations
Abstract: In this work; we investigated the differential interaction of amphiphilic antimicrobial peptides with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid structures by means of extensive molecular dynamics simulations. By using a coarse-grained (CG) model within the MARTINI force field; we simulated the peptide–lipid system from three different initial configurations: (a) peptides in water in the presence of a pre-equilibrated lipid bilayer; (b) peptides inside the hydrophobic core of the membrane; and (c) random configurations that allow self-assembled molecular structures. This last approach allowed us to sample the structural space of the systems and consider cooperative effects. The peptides used in our simulations are aurein 1.2 and maculatin 1.1; two well-known antimicrobial peptides from the Australian tree frogs; and molecules that present different membrane-perturbing behaviors. Our results showed differential behaviors for each type of peptide seen in a different organization that could guide a molecular interpretation of the experimental data. While both peptides are capable of forming membrane aggregates; the aurein 1.2 ones have a pore-like structure and exhibit a higher level of organization than those conformed by maculatin 1.1. Furthermore; maculatin 1.1 has a strong tendency to form clusters and induce curvature at low peptide–lipid ratios. The exploration of the possible lipid–peptide structures; as the one carried out here; could be a good tool for recognizing specific configurations that should be further studied with more sophisticated methodologies.
Extracted statement — Has method: Molecular Dynamics Simulations
Research field: Biophysics
Title: Differential Stability of Aurein 1.2 Pores in Model Membranes of Two Probiotic Strains
Abstract: Aurein 1.2 is an antimicrobial peptide from the skin secretion of an Australian frog. In previous experimental work, we reported a differential action of aurein 1.2 on two probiotic strains Lactobacillus delbrueckii subsp. Bulgaricus (CIDCA331) and Lactobacillus delbrueckii subsp. Lactis (CIDCA133). The differences found were attributed to the bilayer compositions. Cell cultures and CIDCA331-derived liposomes showed higher susceptibility than the ones derived from the CIDCA133 strain, leading to content leakage and structural disruption. Here, we used Molecular Dynamics simulations to explore these systems at atomistic level. We hypothesize that if the antimicrobial peptides organized themselves to form a pore, it will be more stable in membranes that emulate the CIDCA331 strain than in those of the CIDCA133 strain. To test this hypothesis, we simulated pre-assembled aurein 1.2 pores embedded into bilayer models that emulate the two probiotic strains. It was found that the general behavior of the systems depends on the composition of the membrane rather than the pre-assemble system characteristics. Overall, it was observed that aurein 1.2 pores are more stable in the CIDCA331 model membranes. This fact coincides with the high susceptibility of this strain against antimicrobial peptide. In contrast, in the case of the CIDCA133 model membranes, peptides migrate to the water-lipid interphase, the pore shrinks and the transport of water through the pore is reduced. The tendency of glycolipids to make hydrogen bonds with peptides destabilize the pore structures. This feature is observed to a lesser extent in CIDCA 331 due to the presence of anionic lipids. Glycolipid transverse diffusion (flip-flop) between monolayers occurs in the pore surface region in all the cases considered. These findings expand our understanding of the antimicrobial peptide resistance properties of probiotic strains.
Extracted statement — Has method: Molecular Dynamics Simulations
Research field: Biophysics
Title: Could Cardiolipin Protect Membranes against the Action of Certain Antimicrobial Peptides? Aurein 1.2, a Case Study
Abstract: The activity of a host of antimicrobial peptides has been examined against a range of lipid bilayers mimicking bacterial and eukaryotic membranes. Despite this, the molecular mechanisms and the nature of the physicochemical properties underlying the peptide–lipid interactions that lead to membrane disruption are yet to be fully elucidated. In this study, the interaction of the short antimicrobial peptide aurein 1.2 was examined in the presence of an anionic cardiolipin-containing lipid bilayer using molecular dynamics simulations. Aurein 1.2 is known to interact strongly with anionic lipid membranes. In the simulations, the binding of aurein 1.2 was associated with buckling of the lipid bilayer, the degree of which varied with the peptide concentration. The simulations suggest that the intrinsic properties of cardiolipin, especially the fact that it promotes negative membrane curvature, may help protect membranes against the action of peptides such as aurein 1.2 by counteracting the tendency of the peptide to induce positive curvature in target membranes.
Extracted statement — Has method: Molecular Dynamics Simulations
Research field: Biophysics
Title: Adaptive behaviour and learning in slime moulds: the role of oscillations
Abstract: The slime mould Physarum polycephalum, an aneural organism, uses information from previous experiences to adjust its behaviour, but the mechanisms by which this is accomplished remain unknown. This article examines the possible role of oscillations in learning and memory in slime moulds. Slime moulds share surprising similarities with the network of synaptic connections in animal brains. First, their topology derives from a network of interconnected, vein-like tubes in which signalling molecules are transported. Second, network motility, which generates slime mould behaviour, is driven by distinct oscillations that organize into spatio-temporal wave patterns. Likewise, neural activity in the brain is organized in a variety of oscillations characterized by different frequencies. Interestingly, the oscillating networks of slime moulds are not precursors of nervous systems but, rather, an alternative architecture. Here, we argue that comparable information-processing operations can be realized on different architectures sharing similar oscillatory properties. After describing learning abilities and oscillatory activities of P. polycephalum, we explore the relation between network oscillations and learning, and evaluate the organism's global architecture with respect to information-processing potential. We hypothesize that, as in the brain, modulation of spontaneous oscillations may sustain learning in slime mould. This article is part of the theme issue ‘Basal cognition: conceptual tools and the view from the single cell’.
Extracted statement — about species: Physarum polycephalum
Research field: Biophysics
Title: Direct Visualization of Membrane Leakage Induced by the Antibiotic Peptides: Maculatin, Citropin, and Aurein
Abstract: Membrane lysis caused by antibiotic peptides is often rationalized by means of two different models: the so-called carpet model and the pore-forming model. We report here on the lytic activity of antibiotic peptides from Australian tree frogs, maculatin 1.1, citropin 1.1, and aurein 1.2, on POPC or POPC/POPG model membranes. Leakage experiments using fluorescence spectroscopy indicated that the peptide/lipid mol ratio necessary to induce 50% of probe leakage was smaller for maculatin compared with aurein or citropin, regardless of lipid membrane composition. To gain further insight into the lytic mechanism of these peptides we performed single vesicle experiments using confocal fluorescence microscopy. In these experiments, the time course of leakage for different molecular weight (water soluble) fluorescent markers incorporated inside of single giant unilamellar vesicles is observed after peptide exposure. We conclude that maculatin and its related peptides demonstrate a pore-forming mechanism (differential leakage of small fluorescent probe compared with high molecular weight markers). Conversely, citropin and aurein provoke a total membrane destabilization with vesicle burst without sequential probe leakage, an effect that can be assigned to a carpeting mechanism of lytic action. Additionally, to study the relevance of the proline residue on the membrane-action properties of maculatin, the same experimental approach was used for maculatin-Ala and maculatin-Gly (Pro-15 was replaced by Ala or Gly, respectively). Although a similar peptide/lipid mol ratio was necessary to induce 50% of leakage for POPC membranes, the lytic activity of maculatin-Ala and maculatin-Gly decreased in POPC/POPG (1:1 mol) membranes compared with that observed for the naturally occurring maculatin sequence. As observed for maculatin, the lytic action of Maculatin-Ala and maculatin-Gly is in keeping with the formation of pore-like structures at the membrane independently of lipid composition.
Extracted statement — has target: POPC/POPG model membranes