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Wildfires occur frequently in various regions of the world, causing serious damage to natural and human resources. Traditional wildfire prevention and management methods are often hampered by monitoring challenges and low efficiency. Digital twin technology, as a highly integrated virtual simulation model, shows great potential in wildfire management and prevention. At the same time, the virtual–reality combination of digital twin technology can provide new solutions for wildfire management. This paper summarizes the key technologies required to establish a wildfire digital twin system, focusing on the technical requirements and research progress in fire detection, simulation, and prediction. This paper also proposes the wildfire digital twin (WFDT) model, which integrates real-time data and computational simulations to replicate and predict wildfire behavior. The synthesis of these techniques within the framework of a digital twin offers a comprehensive approach to wildfire management, providing critical insights for decision-makers to mitigate risks and improve emergency response strategies. [ABSTRACT FROM AUTHOR]

Under certain task conditions, error-related potential (ErrP) will be elicited, meaning that the subject is perceiving an error, responding to an external error, or engaging in a cognitive process of reinforcement learning. The detection of ErrP on a single trial basis has been studied and applied to improve all kinds of brain–computer interfaces (BCIs). However, the performance of this kind of detection is not currently good enough. In the paper, we proposed a novel method, called window-adjusted common spatial pattern (WACSP), for detecting ErrP in P300 BCI. In this method, the coefficient of determination was introduced to measure the difference of Electroencephalogram (EEG) signals on a channel at a moment and to guide the search of time windows in which EEG differences are significant, and common spatial pattern (CSP) was further used to capture the stable spatial patterns of EEG differences between correct and incorrect responses in each time window. WACSP and the commonly used methods were tested on the data sets that were built using the EEG signals acquired during the P300 BCI experiments with different feedback. The comparisons of accuracy, area under receiver operating characteristics curve (AUC) and F-measure show that WACSP significantly outperforms the commonly used methods. The proposed method can improve ErrP detection based on a single trial. [ABSTRACT FROM AUTHOR]

Health monitoring, rehabilitation, and fitness are just a few domains where human activity recognition can be applied. In this study, a deep learning approach has been proposed to recognise ambulation and fitness activities from data collected by five participants using smart insoles. Smart insoles, consisting of pressure and inertial sensors, allowed for seamless data collection while minimising user discomfort, laying the baseline for the development of a monitoring and/or rehabilitation system for everyday life. The key objective has been to enhance the deep learning model performance through several techniques, including data segmentation with overlapping technique (2 s with 50% overlap), signal down-sampling by averaging contiguous samples, and a cost-sensitive re-weighting strategy for the loss function for handling the imbalanced dataset. The proposed solution achieved an Accuracy and F1-Score of 98.56% and 98.57%, respectively. The Sitting activities obtained the highest degree of recognition, closely followed by the Spinning Bike class, but fitness activities were recognised at a higher rate than ambulation activities. A comparative analysis was carried out both to determine the impact that pre-processing had on the proposed core architecture and to compare the proposed solution with existing state-of-the-art solutions. The results, in addition to demonstrating how deep learning solutions outperformed those of shallow machine learning, showed that in our solution the use of data pre-processing increased performance by about 2%, optimising the handling of the imbalanced dataset and allowing a relatively simple network to outperform more complex networks, reducing the computational impact required for such applications. [ABSTRACT FROM AUTHOR]

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible. [ABSTRACT FROM AUTHOR]

Decision-making is a very important cognitive process in our daily life. There has been increasing interest in the discriminability of single-trial electroencephalogram (EEG) during decision-making. In this study, we designed a machine learning based framework to explore the discriminability of single-trial EEG corresponding to different decisions. For each subject, the framework split the decision-making trials into two parts, trained a feature model and a classifier on the first part, and evaluated the discriminability on the second part using the feature model and classifier. A proposed algorithm and five existing algorithms were applied to fulfill the feature models, and the algorithm Linear Discriminative Analysis (LDA) was used to implement the classifiers. We recruited 21 subjects to participate in Chicken Game (CG) experiments. The results show that there exists the discriminability of single-trial EEG between the cooperation and aggression decisions during the CG experiments, with the classification accuray of 75% (±6%), and the discriminability is mainly from the EEG information below 40 Hz. The further analysis indicates that the contributions of different brain regions to the discriminability are consistent with the existing knowledge on the cognitive mechanism of decision-making, confirming the reliability of the conclusions. This study exhibits that it is feasible to apply machine learning methods to EEG analysis of decision-making cognitive process. [ABSTRACT FROM AUTHOR]

This study aims to compare the performance of multiple linear regression and machine learning algorithms for predicting manure nitrogen excretion in lactating dairy cows, and to develop new machine learning prediction models for MN excretion. Dataset used were collated from 43 total diet digestibility studies with 951 lactating dairy cows. Prediction models for MN were developed and evaluated using MLR technique and three machine learning algorithms, artificial neural networks, random forest regression and support vector regression. The ANN model produced a lower RMSE and a higher CCC, compared to the MLR, RFR and SVR model, in the tenfold cross validation. Meanwhile, a hybrid knowledge-based and data-driven approach was developed and implemented to selecting features in this study. Results showed that the performance of ANN models were greatly improved by the turning process of selection of features and learning algorithms. The proposed new ANN models for prediction of MN were developed using nitrogen intake as the primary predictor. Alternative models were also developed based on live weight and milk yield for use in the condition where nitrogen intake data are not available (e.g., in some commercial farms). These new models provide benchmark information for prediction and mitigation of nitrogen excretion under typical dairy production conditions managed within grassland-based dairy systems. [ABSTRACT FROM AUTHOR]

Musculoskeletal disorders (MSDs) are associated with sitting postures. The assessment and prevention of risk factors for workplace exposure are indispensable aspects of reducing the occurrence of MSDs. This paper proposes an ergonomic assessment method of risk factors for MSDs associated with sitting postures in the actual working conditions. A Kinect sensor with the RULA method was primarily used to collect the data and evaluate the relevant postures. The results obtained were compared with the evaluation results by a human expert. Additionally, we verified the capability and effectiveness of this method. A program system for human joint recognition and acquisition was implemented. The results indicated that the Kinect joint data is generally accurate and can adequately complete the RULA evaluation table. The results from the front and right-hand side obtained by the Kinect were consistent with the results of the expert evaluation, and no significant difference was observed between them (p > 0. 0 5). However, when the participants faced the Kinect, the sensor performed better, and the evaluation result was more accurate. A high consistency was observed between the evaluation results obtained from the front and the expert (proportion agreement index = 0. 6 5 , Cohen's kappa = 0. 7 7). Only a slight consistency was observed between the evaluation results obtained from the right-hand side and the expert (proportion agreement index = 0. 4 1 , Cohen's kappa = 0. 0 8). This research created a new ergonomic method for the risk assessment of MSDs associated with sitting postures. The combination of theory and practice is crucial in the risk assessment of sitting postures in workplaces. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user's daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection technique has been applied to reduce the number of features from 272 to 227 to improve the robustness of the proposed system and to investigate the importance of features in the dataset. According to the findings, the inertial sensors are reliable for the recognition of dynamic activities, while pressure sensors are reliable for stationary activities; however, the highest accuracy ( 94.66 % ) was achieved by combining both types of sensors. [ABSTRACT FROM AUTHOR]

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful. [ABSTRACT FROM AUTHOR]

Introduction: As a direct bridge between the brain and the outer world, brain–computer interface (BCI) is expected to replace, restore, enhance, supplement, or improve the natural output of brain. The prospect of BCI serving humans is very broad. However, the extensive applications of BCI have not been fully achieved. One of reasons is that the cost of calibration reduces the convenience and usability of BCI. Methods: In this study, we proposed a calibration-free approach, which is based on the ideas of reinforcement learning and transfer learning, for P300-based BCI. This approach, composed of two algorithms: P300 linear upper confidence bound (PLUCB) and transferred PLUCB (TPLUCB), is able to learn during the usage by exploration and exploitation and allows P300-based BCI to start working without any calibration. Results: We tested the performances of PLUCB and TPLUCB using stepwise linear discriminant analysis (SWLDA), a commonly used method that needs calibration, as a baseline in simulated online experiments. The results showed the merits of PLUCB and TPLUCB. PLUCB can quickly increase the accuracies to the level of SWLDA. TPLUCB has surpassed SWLDA in the sample accuracy since it starts running. Both PLUCB and TPLUCB have the ability to keep improving the classification performance during the process. The overall sample accuracies ( 73.6 ± 4.8 % , 73.1 ± 4.9 % ), overall symbol accuracies ( 80.4 ± 12.8 % , 79.6 ± 14.0 % ), F-measures ( 0.45 ± 0.06 , 0.44 ± 0.06 ) and information transfer ratios (ITR) ( 36.4 ± 9.1 , 35.5 ± 9.8 ) of PLUCB and TPLUCB are significantly better than those of SWLDA (overall sample accuracy: 58.8 ± 3.8 % , overall symbol accuracy: 69.0 ± 18.3 % , F-measure: 0.38 ± 0.04 , ITR: 28.7 ± 10.7 ). Conclusions: The proposed approach, which does not need calibration but outperform SWLDA, is a very good option for the implementation of P300-based BCI. [ABSTRACT FROM AUTHOR]

Accurate quantification of volatile fatty acid (VFA) concentrations in rumen fluid are essential for research on rumen metabolism. The study comprehensively investigated the pros and cons of High-performance liquid chromatography (HPLC) and 1H Nuclear magnetic resonance (1H-NMR) analysis methods for rumen VFAs quantification. We also investigated the performance of several commonly used data pre-treatments for the two sets of data using correlation analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The molar proportion and reliability analysis demonstrated that the two approaches produce highly consistent VFA concentrations. In the pre-processing of NMR spectra, line broadening and shim correction may reduce estimated concentrations of metabolites. We observed differences in results using multiplet of different protons from one compound and identified "handle signals" that provided the most consistent concentrations. Different data pre-treatment strategies tested with both HPLC and NMR significantly affected the results of downstream data analysis. "Normalized by sum" pre-treatment can eliminate a large number of positive correlations between NMR-based VFA. A "Combine" strategy should be the first choice when calculating the correlation between metabolites or between samples. The PCA and PLS-DA suggest that except for "Normalize by sum", pre-treatments should be used with caution. [ABSTRACT FROM AUTHOR]

Selenium (Se) deficiency is a widespread and seasonally chronic phenomenon observed in Tibetan sheep (Ovis aries) traditionally grazed on the Qinghai–Tibet Plateau (QTP). Effects of the dietary addition of Se-enriched yeast (SeY) on the bacterial community in sheep rumen and rumen fermentation were evaluated with the aim of gaining a better understanding of the rumen prokaryotic community. Twenty-four yearling Tibetan rams [initial average body weight (BW) of 31.0 ± 0.64 kg] were randomly divided into four treatment groups, namely, control (CK), low Se (L), medium Se (M), and high Se (H). Each group comprised six rams and was fed a basic diet of fresh forage cut from the alpine meadow, to which SeY was added at prescribed dose rates. This feed trial was conducted for over 35 days. On the final day, rumen fluid was collected using a transesophageal sampler for analyzing rumen pH, NH3-N content, volatile fatty acid (VFA) level, and the rumen microbial community. Our analyses showed that NH3-N, total VFA, and propionate concentrations in the M group were significantly higher than in the other groups (P < 0.05). Both the principal coordinates analysis (PCoA) and the analysis of similarities revealed that the bacterial population structure of rumen differed among the four groups. The predominant rumen bacterial phyla were found to be Bacteroidetes and Firmicutes, and the three dominant genera in all the samples across all treatments were Christensenellaceae R7 group, Rikenellaceae RC9 gut group, and Prevotella 1. The relative abundances of Prevotella 1, Rikenellaceae RC9 gut group, Ruminococcus 2, Lachnospiraceae XPB1014 group, Carnobacterium , and Hafnia- Obesumbacterium were found to differ significantly among the four treatment groups (P < 0.05). Moreover, Tax4fun metagenome estimation revealed that gene functions and metabolic pathways associated with carbohydrate and other amino acids were overexpressed in the rumen microbiota of SeY-supplemented sheep. To conclude, SeY significantly affects the abundance of rumen bacteria and ultimately affects the rumen microbial fermentation. [ABSTRACT FROM AUTHOR]

Traditional approaches to the dynamics of the open quantum systems with high precision are often resource intensive. How to improve computation accuracy and efficiency for target systems is an extremely difficult challenge. In this work, combining unsupervised and supervised learning algorithms, a deep‐learning approach is introduced to simulate and predict Landau–Zenner dynamics. Data obtained from multiple Davydov D2 Ansatz with a low multiplicity of four are used for training, while the data from the trial state with a high multiplicity of ten are adopted as target data to assess the accuracy of prediction. After proper training, our method can successfully predict and simulate Landau–Zenner dynamics using only random noise and two adjustable model parameters. Compared to the high‐precision dynamics data from multiple Davydov D2 Ansatz with a multiplicity of ten, the error rate falls below 0.6%. [ABSTRACT FROM AUTHOR]

Herein, we present a new strategy for the synthesis of 2D porous MoP/Mo2N heterojunction nanosheets based on the pyrolysis of 2D [PMo12O40]3−‐melamine (PMo12‐MA) nanosheet precursor from a polyethylene glycol (PEG)‐mediated assembly route. The heterostructure nanosheets are ca. 20 nm thick and have plentiful pores (<5 nm). These structure features offer advantages to promote the HER activity, including the favorable water dissociation kinetics around heterojunction as confirmed by theoretical calculations, large accessible surface of 2D nanosheets, and enhanced mass‐transport ability by pores. Consequently, the 2D porous MoP/Mo2N heterojunction nanosheets exhibit excellent HER activity with low overpotentials of 89, 91 and 89 mV to achieve a current density of 10 mA cm−2 in alkaline, neutral and acidic electrolytes, respectively. The HER performance is superior to the commercial Pt/C at a current density >55 mA cm−2 in neutral medium and >190 mA cm−2 in alkaline medium. [ABSTRACT FROM AUTHOR]

Herein, we present a new strategy for the synthesis of 2D porous MoP/Mo2N heterojunction nanosheets based on the pyrolysis of 2D [PMo12O40]3−‐melamine (PMo12‐MA) nanosheet precursor from a polyethylene glycol (PEG)‐mediated assembly route. The heterostructure nanosheets are ca. 20 nm thick and have plentiful pores (<5 nm). These structure features offer advantages to promote the HER activity, including the favorable water dissociation kinetics around heterojunction as confirmed by theoretical calculations, large accessible surface of 2D nanosheets, and enhanced mass‐transport ability by pores. Consequently, the 2D porous MoP/Mo2N heterojunction nanosheets exhibit excellent HER activity with low overpotentials of 89, 91 and 89 mV to achieve a current density of 10 mA cm−2 in alkaline, neutral and acidic electrolytes, respectively. The HER performance is superior to the commercial Pt/C at a current density >55 mA cm−2 in neutral medium and >190 mA cm−2 in alkaline medium. [ABSTRACT FROM AUTHOR]

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM. [ABSTRACT FROM AUTHOR]

To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently, most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then, a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively. [ABSTRACT FROM AUTHOR]

Keywords: omics; machine learning; drug; RNA; disease; biomarker; network analysis; deep learning EN omics machine learning drug RNA disease biomarker network analysis deep learning N.PAG N.PAG 4 12/29/20 20201223 NES 201223 Due to the high experimental cost and the exponential decline in the cost of high-throughput sequencing, computational models, and methods are preferred by scholars. In Li S. et al., on the strength of a matrix containing microbes similarity, disease similarity and a bipartite graph network of the two interactions, the potential microbe-disease associations are calculated by Katz centrality. Omics, machine learning, drug, RNA, disease, biomarker, network analysis, deep learning. [Extracted from the article]

Metabolites are the final production of biochemical reactions in the rumen micro-ecological system and are very sensitive to changes in rumen microbes. Nuclear magnetic resonance (NMR) spectroscopy could both identify and quantify the metabolic composition of the ruminal fluid, which reflects the interaction between rumen microbes and diet. The main challenge of untargeted metabolomics is the compound annotation. Based on non-linear and linear associations between microbial gene abundances and integrals derived from NMR spectra, combined with knowledge of enzymatic reaction from the KEGG database, this study developed a knowledge-driven network-based analytical framework for the inference of metabolites. There were 89 potential metabolites inferred from the integral co-occurrence network. The results are supported by dissimilarity network analysis. The coexistence of non-linear and linear associations between microbial gene abundances and spectral integrals was detected. The study successfully found the corresponding integrals for acetate, butyrate and propionate, which are the major volatile fatty acids (VFA) in the rumen. This novel framework could very efficiently infer metabolites to corresponding integrals from NMR spectra. [ABSTRACT FROM AUTHOR]

There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine. [ABSTRACT FROM AUTHOR]

High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomic functions. Machine learning (ML) models can be trained using microbial features which are then used to classify microbial data into different functional classes. For example, ML analyses over the human microbiome data has been linked to the prediction of important biological states. For analysing omics data, integrating abundance count of taxonomical features with their biological relationships is important. These relationships can potentially be uncovered from the phylogenetic tree of microbial taxa. In this paper, we propose a novel integrative framework Phy-PMRFI. This framework is driven by the phylogeny-based modeling of omics data to predict metagenomic functions using important features selected by a random forest importance (RFI) strategy. The proposed framework integrates the underlying phylogenetic tree information with abundance measures of microbial species (features) by creating a novel phylogeny and abundance aware matrix structure (PAAM). Phy-PMRFI progresses by ranking the microbial features using an RFI measure. This is then used as input for microbiome classification. The resultant feature set enhances the performance of the state-of-art methods such as support vector machines. Our proposed integrative framework also outperforms the state-of-the-art pipeline of phylogenetic isometric log-ratio transform (PhILR) and MetaPhyl. Prediction accuracy of 90 % is obtained with Phy-PMRFI over human throat microbiome in comparison to other approaches of PhILR with 53% and MetaPhyl with 71% accuracy. [ABSTRACT FROM AUTHOR]

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine. [ABSTRACT FROM AUTHOR]

"Metagenomics" is the study of genomic sequences obtained directly from environmental microbial communities with the aim to linking their structures with functional roles. The field has been aided in the unprecedented advancement through high-throughput omics data sequencing. The outcome of sequencing are biologically rich data sets. Metagenomic data consisting of microbial species which outnumber microbial samples, lead to the "curse of dimensionality" in datasets. Hence, the focus in metagenomics studies has moved towards developing efficient computational models using Machine Learning (ML), reducing the computational cost. In this paper, we comprehensively assessed various ML approaches to classifying high-dimensional human microbiota effectively into their functional phenotypes. We propose the application of embedded feature selection methods, namely, Extreme Gradient Boosting and Penalized Logistic Regression to determine important microbial species. The resultant feature set enhanced the performance of one of the most popular state-of-the-art methods, Random Forest (RF) over metagenomic studies. Experimental results indicate that the proposed method achieved best results in terms of accuracy, area under the Receiver Operating Characteristic curve (ROC-AUC), and major improvement in processing time. It outperformed other feature selection methods of filters or wrappers over RF and classifiers such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), and k- Nearest Neighbors (k-NN). [ABSTRACT FROM AUTHOR]

Background Research into the use of digital technology for weight loss maintenance (intentionally losing at least 10% of initial body weight and actively maintaining it) is limited. The aim of this article was to systematically review randomized controlled trials (RCTs) reporting on the use of digital technologies for communicating on weight loss maintenance to determine its' effectiveness, and identify gaps and areas for further research. Methods A systematic literature review was conducted by searching electronic databases to locate publications dated between 2006 and February 2018. Criteria were applied, and RCTs using digital technologies for weight loss maintenance were selected. Results Seven RCTs were selected from a total of 6541 hits after de-duplication and criteria applied. Three trials used text messaging, one used e-mail, one used a web-based system and two compared such a system with face-to-face contact. From the seven RCTs, one included children (n = 141) and reported no difference in BMI Standard Deviation between groups. From the seven trials, four reported that technology is effective for significantly aiding weight loss maintenance compared with control (no contact) or face-to face-contact in the short term (between 3 and 24 months). Conclusions It was concluded that digital technologies have the potential to be effective communication tools for significantly aiding weight loss maintenance, especially in the short term (from 3 to 24 months). Further research is required into the long-term effectiveness of contemporary technologies. [ABSTRACT FROM AUTHOR]

Forest fire is one of the most frequent, fast spreading and destructive natural disasters. Many countries have developed their own fire prediction model and computational systems to predict the fire spreading, however, the user interaction, display effect and prediction accuracy have not yet met the requirements for firefighting in real forest fire events. The forest fire spreading is a complex process affected by multi-factors. Understanding the relationships between these multi-factors and the forest fire spreading trend is vital to predicting the fire spreading promptly and accurately to make the strategy in extinguishing the forest fire. In this paper, we propose and develop a three-dimensional (3D) forest fire spreading simulation system, FFSimulator, to visualize the impact of multi-factors to the fire spread. FFSimultor integrates the multi-factor analysis approach with the FARSITE prediction model to improve the prediction. The FFSimulator developed applies 3D scene organization, template-based vector data mapping and overlaps visualization techniques to provide a 3D dynamic visualization of large-scale forest fire. The 3D multi-factors superposition analysis simulates the impacts of individual factor and multi-factors on the trend of surface fire spreading, which can be used to identify the key sites for the prevention and the control of forest fires. The system has been tested and evaluated using real data of Shanghan forest fire. [ABSTRACT FROM AUTHOR]

Advances in high-throughput technologies along with the curation of small-scale experiments has aided in the construction of reference maps of the interactome. These maps are critical to our understanding of genotype-phenotype relationships and disease. However, our knowledge of disease associated genes and the map of the human interactome still remains incomplete. In this study we investigate whether protein–protein interaction networks (PPINs) constructed from either experimental or curated data have an impact upon disease network analysis. An integrative network-driven framework is implemented to integrate diverse heterogeneous data including: gene-expression, PPIN, ontology-based similarity, degree connectivity and betweenness centrality measures to uncover potential Alzhemier disease (AD) candidate genes. Two PPINs have been selected and constructed from (1) experimental high-throughput data and (2) literature-curated sources. Only a marginal overlap of protein pairs between the two PPINs (305 protein pairs) was observed. A total of 17 significant AD gene candidate genes were identified using the literature derived PPIN compared to 20 genes using the PPIN constructed from high-throughput data. Both approaches correctly identified the AD susceptible TRAF1, a critical regulator of cerebral ischaemia–reperfusion injury and neuronal death. Biological process enrichment analysis revealed genes candidates from the literature based PPIN are modulated in AD pathogenesis such as neuron differentiation and involved in KEGG pathways such as neurotrophin signaling pathways. Tissue specific analysis revealed 48 % of AD gene candidates obtained from the literature curated PPIN were expressed in tissues where AD is observed compared to 19 % of gene candidates extracted using the high-throughput PPIN. [ABSTRACT FROM AUTHOR]

In this study, a minimum dominating set based approach was developed and implemented as a Cytoscape plugin to identify critical and redundant proteins in a protein interaction network. We focused on the investigation of the properties associated with critical proteins in the context of the analysis of interaction networks specific to cell cycle in both yeast and human. A total of 132 yeast genes and 129 human proteins have been identified as critical nodes while 950 in yeast and 980 in human have been categorized as redundant nodes. A clear distinction between critical and redundant proteins was observed when examining their topological parameters including betweenness centrality, suggesting a central role of critical proteins in the control of a network. The significant differences in terms of gene coexpression and functional similarity were observed between the two sets of proteins in yeast. Critical proteins were found to be enriched with essential genes in both networks and have a more deleterious effect on the network integrity than their redundant counterparts. Furthermore, we obtained statistically significant enrichments of proteins that govern human diseases including cancer-related and virus-targeted genes in the corresponding set of critical proteins. [ABSTRACT FROM PUBLISHER]

Comprehensive characterization and identification of cancer subtypes have a number of applications and implications in life science and cancer research. Technologies centered on the integration of omics data hold great promise in this endeavor. This paper proposed a multiplex network-based approach for integrative analysis of heterogeneous omics data. It represents a useful alternative network-based solution to the problem and a significant step forward to the methods in which each type of data is treated independently. It has been tested on the identification of the subtypes of glioblastoma multiforme and breast invasive carcinoma from three omics data. The results obtained have shown that it has achieved the performance comparable to state-of-the-art techniques (Normalized Mutual Information >0.8). In comparison to traditional systems biology tools, the proposed methodology has several significant advantages. It has the ability to correlate and integrate multiple data levels in a holistic manner which may be useful to facilitate our understanding of the pathogenesis of diseases and to capture the heterogeneity of biological processes and the complexity of phenotypes. [ABSTRACT FROM PUBLISHER]

Systematic identification of protein complexes from protein-protein interaction networks (PPIs) is an important application of data mining in life science. Over the past decades, various new clustering techniques have been developed based on modelling PPIs as binary relations. Non-binary information of co-complex relations (prey/bait) in PPIs data derived from tandem affinity purification/mass spectrometry (TAP-MS) experiments has been unfairly disregarded. In this paper, we propose a Biased Random Walk based algorithm for detecting protein complexes from TAP-MS data, resulting in the random walk with restarting baits (RWRB). RWRB is developed based on Random walk with restart. The main contribution of RWRB is the incorporation of co-complex relations in TAP-MS PPI networks into the clustering process, by implementing a new restarting strategy during the process of random walk. Through experimentation on un-weighted and weighted TAP-MS data sets, we validated biological significance of our results by mapping them to manually curated complexes. Results showed that, by incorporating non-binary, co-membership information, significant improvement has been achieved in terms of both statistical measurements and biological relevance. Better accuracy demonstrates that the proposed method outperformed several state-of-the-art clustering algorithms for the detection of protein complexes in TAP-MS data. [ABSTRACT FROM PUBLISHER]