Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variability in developmental progression and distinct neuroanatomical features influenced by sex and age. Recent advances in deep learning models based on functional connectivity (FC) graphs have produced promising results, but they have focused on generalized global activation patterns and failed to capture specialized regional characteristics and accurately assess disease indications. METHODS: To overcome these limitations, we propose a novel deep learning method that models FC with multi-head attention, which enables simultaneous modeling of the intricate and variable patterns of brain connectivity associated with ASD, effectively extracting abnormal patterns of brain connectivity. The proposed method not only identifies region-specific correlations but also emphasizes connections at specific, transient time points from diverse perspectives. The extracted FC is transformed into a graph, assigning weighted labels to the edges to reflect the degree of correlation, which is then processed using a graph neural network capable of handling edge labels. RESULTS: Experiments on the autism brain imaging data exchange (ABIDE) I and II datasets, which include a heterogeneous cohort, showed superior performance over the state-of-the-art methods, improving accuracy by up to 3.7%p. The incorporation of multi-head attention in FC analysis markedly improved the distinction between typical brains and those affected by ASD. Additionally, the ablation study validated diverse brain characteristics in ASD patients across different ages and sexes, offering insightful interpretations. CONCLUSION: These results emphasize the effectiveness of the method in enhancing diagnostic accuracy and its potential in advancing neurological research for ASD diagnosis.

Association Between Clinicopathological Parameters and S100A8/A9 Expression According to Smoking History in Patients With Non-small Cell Lung Cancer.

BACKGROUND/AIM: Lung cancer is a major cause of cancer-related deaths worldwide, and chronic inflammation caused by cigarette smoke plays a crucial role in the development and progression of this disease. S100A8/9 and RAGE are associated with chronic inflammatory diseases and cancer. This study aimed to investigate the expression of S100A8/9, HMBG1, and other related pro-inflammatory molecules and clinical characteristics in patients with non-small cell lung cancer (NSCLC). PATIENTS AND METHODS: We obtained serum and bronchoalveolar lavage (BAL) fluid samples from 107 patients and categorized them as never or ever-smokers. We measured the levels of S100A8/9, RAGE, and HMGB1 in the collected samples using enzyme-linked immunosorbent kits. Immunohistochemical staining was also performed to assess the expression of S100A8/9, CD11b, and CD8 in lung cancer tissues. The correlation between the expression of these proteins and the clinical characteristics of patients with NSCLC was also explored. RESULTS: The expression of S100A8/A9, RAGE, and HMGB was significantly correlated with smoking status and was higher in people with a history of smoking or who were currently smoking. There was a positive correlation between serum and BAL fluid S100A8/9 levels. The expression of S100A8/A9 and CD8 in lung tumor tissues was significantly correlated with smoking history in patients with NSCLC. Ever-smokers, non-adenocarcinoma histology, and high PD-L1 expression were significant factors predicting high serum S100A8/9 levels in multivariate analysis. CONCLUSION: The S100A8/9-RAGE pathway and CD8 expression were increased in smoking-related NSCLC patients. The S100A8/9-RAGE pathway could be a promising biomarker for chronic airway inflammation and carcinogenesis in smoking-related lung diseases.

Ensemble of diverse deep neural networks with pseudo-labels for repayment prediction in social lending.

In peer-to-peer (P2P) social lending, it is important to predict the repayment of borrowers. P2P lending data are generated in real-time, but most of them are pending to decide the repayment because the deadline is not yet expired. Adding the unexpired data with appropriate labels into the training set could improve the performance of a prediction model, but the pseudo-labels cannot be certainly precise. In this paper, we propose an ensemble classifier composed of diverse convolutional neural networks (CNNs) of GoogLeNet, ResNet and DenseNet for the repayment prediction in social lending with the pseudo-labels approximated by an uncertainty handling scheme. The additional data labeled by Dempster-Shafer fusion of two semi-supervised learning methods boost up training of various models of CNNs, which are combined by weighted voting. A diversity measure is applied to constructing a pool of different models of CNNs that extract the effective features in the social lending data with labeling noise and predict the borrower's loan status. The experiment with the real dataset of 855,502 cases from Lending Club confirms that the diverse ensemble combined with weighted voting achieves the highest performance and outperforms conventional methods.

Clustered embedding using deep learning to analyze urban mobility based on complex transportation data.

Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage. In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. However, as urban mobility becomes increasingly complex, it becomes difficult to embed patterns into a single vector because of its limited capacity. In this paper, we propose a novel method for analyzing urban mobility based on deep learning. The proposed method involves clustering mobility patterns and embedding them to capture their implicit meaning. Clustering groups mobility patterns based on their spatiotemporal characteristics, and embedding provides meaningful information regarding both individual residents (i.e., personalized mobility) and all residents as a whole, enabling a more effective analysis of mobility patterns. Experiments were performed to predict the successive points of interest (POIs) based on transportation data collected from 1.5 million citizens in a large metropolitan city; the results demonstrate that the proposed method achieves top-1, 3, and 5 accuracies of 73.64%, 88.65%, and 91.54%, respectively, which are much higher than those of the conventional method (59.48%, 75.85%, and 80.1%, respectively). We also demonstrate that the proposed method facilitates the analysis of urban mobility through arithmetic operations between POI vectors.

Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions.

In recent years, various deep learning models have been developed for the fault diagnosis of rotating machines. However, in practical applications related to fault diagnosis, it is difficult to immediately implement a trained model because the distribution of source data and target domain data have different distributions. Additionally, collecting failure data for various operating conditions is time consuming and expensive. In this paper, we introduce a new transformation method for the latent space between domains using the source domain and normal data of the target domain that can be easily collected. Inspired by semantic transformations in an embedded space in the field of word embedding, discrepancies between the distribution of the source and target domains are minimized by transforming the latent representation space in which fault attributes are preserved. To match the feature area and distribution, spatial attention is applied to learn the latent feature spaces, and the 1D CNN LSTM architecture is implemented to maximize the intra-class classification. The proposed model was validated for two types of rotating machines such as a dataset of rolling bearings as CWRU and a gearbox dataset of heavy machinery. Experimental results show the proposed method has higher cross-domain diagnostic accuracy than others, therefore showing reliable generalization performance in rotating machines operating under various conditions.

3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance.

As the surveillance devices proliferate, various machine learning approaches for video anomaly detection have been attempted. We propose a hybrid deep learning model composed of a video feature extractor trained by generative adversarial network with deficient anomaly data and an anomaly detector boosted by transferring the extractor. Experiments with UCSD pedestrian dataset show that it achieves 94.4% recall and 86.4% precision, which is the competitive performance in video anomaly detection.

Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment.

The cyber-physical system (CPS) is a next-generation smart system that combines computing with physical space. It has been applied in various fields because the uncertainty of the physical world can be ideally controlled using cyber technology. In terms of environmental control, studies have been conducted to enhance the effectiveness of the service by inducing ideal emotions in the service space. This paper proposes a CPS control system for inducing emotion based on multiple sensors. The CPS can expand the constrained environmental sensors of the physical space variously by combining the virtual space with the physical space. The cyber space is constructed in a Unity 3D space that can be experienced through virtual reality devices. We collect the temperature, humidity, dust concentration, and current emotion in the physical space as an environmental control elements, and the control illumination, color temperature, video, sound and volume in the cyber space. The proposed system consists of an emotion prediction module using modular Bayesian networks and an optimal stimulus decision module for deriving the predicted emotion to the target emotion based on utility theory and reinforcement learning. To verify the system, the performance is evaluated using the data collected from real situations.

Fuzzy-Rough Entropy Measure and Histogram Based Patient Selection for miRNA Ranking in Cancer.

MicroRNAs (miRNAs) are known as an important indicator of cancers. The presence of cancer can be detected by identifying the responsible miRNAs. A fuzzy-rough entropy measure (FREM) is developed which can rank the miRNAs and thereby identify the relevant ones. FREM is used to determine the relevance of a miRNA in terms of separability between normal and cancer classes. While computing the FREM for a miRNA, fuzziness takes care of the overlapping between normal and cancer expressions, whereas rough lower approximation determines their class sizes. MiRNAs are sorted according to the highest relevance (i.e., the capability of class separation) and a percentage among them is selected from the top ranked ones. FREM is also used to determine the redundancy between two miRNAs and the redundant ones are removed from the selected set, as per the necessity. A histogram based patient selection method is also developed which can help to reduce the number of patients to be dealt during the computation of FREM, while compromising very little with the performance of the selected miRNAs for most of the data sets. The superiority of the FREM as compared to some existing methods is demonstrated extensively on six data sets in terms of sensitivity, specificity, and score. While for these data sets the score of the miRNAs selected by our method varies from 0.70 to 0.91 using SVM, those results vary from 0.37 to 0.90 for some other methods. Moreover, all the selected miRNAs corroborate with the findings of biological investigations or pathway analysis tools. The source code of FREM is available at http://www.jayanta.droppages.com/FREM.html.

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities.

Recently, recognizing a user's daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user's obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the "Five W's", and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54-14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing.

Serious Complications after Self-expandable Metallic Stent Insertion in a Patient with Malignant Lymphoma.

An 18-year-old woman was evaluated for a chronic productive cough and dyspnea. She was subsequently diagnosed with mediastinal non-Hodgkin lymphoma (NHL). A covered self-expandable metallic stent (SEMS) was implanted to relieve narrowing in for both main bronchi. The NHL went into complete remission after six chemotherapy cycles, but atelectasis developed in the left lower lobe 18 months after SEMS insertion. The left main bronchus was completely occluded by granulation tissue. However, the right main bronchus and intermedius bronchus were patent. Granulation tissue was observed adjacent to the SEMS. The granulation tissue and the SEMS were excised, and a silicone stent was successfully implanted using a rigid bronchoscope. SEMS is advantageous owing to its easy implantation, but there are considerable potential complications such as severe reactive granulation, stent rupture, and ventilation failure in serious cases. Therefore, SEMS should be avoided whenever possible in patients with benign airway disease. This case highlights that SEMS implantation should be avoided even in malignant airway obstruction cases if the underlying malignancy is curable.

Inference of other's internal neural models from active observation.

Recently, there have been several attempts to replicate theory of mind, which explains how humans infer the mental states of other people using multiple sensory input, with artificial systems. One example of this is a robot that observes the behavior of other artificial systems and infers their internal models, mapping sensory inputs to the actuator's control signals. In this paper, we present the internal model as an artificial neural network, similar to biological systems. During inference, an observer can use an active incremental learning algorithm to guess an actor's internal neural model. This could significantly reduce the effort needed to guess other people's internal models. We apply an algorithm to the actor-observer robot scenarios with/without prior knowledge of the internal models. To validate our approach, we use a physics-based simulator with virtual robots. A series of experiments reveal that the observer robot can construct an "other's self-model", validating the possibility that a neural-based approach can be used as a platform for learning cognitive functions.

Pneumatosis intestinalis complicated by pneumoperitoneum in a patient with asthma.

Pneumatosis intestinalis (PI) is a very rare condition that is defined as the presence of gas within the subserosal or submucosal layer of the bowel. PI has been described in association with a variety of conditions including gastrointestinal tract disorders, pulmonary diseases, connective tissue disorders, organ transplantation, leukemia, and various immunodeficiency states. We report a rare case of a 74-year-old woman who complained of dyspnea during the management of acute asthma exacerbation and developed PI; but, it improved without any treatment.

Mobile human network management and recommendation by probabilistic social mining.

Recently, inferring or sharing of mobile contexts has been actively investigated as cell phones have become more than a communication device. However, most of them focused on utilizing the contexts on social network services, while the means in mining or managing the human network itself were barely considered. In this paper, the SmartPhonebook, which mines users' social connections to manage their relationships by reasoning social and personal contexts, is presented. It works like an artificial assistant which recommends the candidate callees whom the users probably would like to contact in a certain situation. Moreover, it visualizes their social contexts like closeness and relationship with others in order to let the users know their social situations. The proposed method infers the social contexts based on the contact patterns, while it extracts the personal contexts such as the users' emotional states and behaviors from the mobile logs. Here, Bayesian networks are exploited to handle the uncertainties in the mobile environment. The proposed system has been implemented with the MS Windows Mobile 2003 SE Platform on Samsung SPH-M4650 smartphone and has been tested on real-world data. The experimental results showed that the system provides an efficient and informative way for mobile social networking.

A novel evolutionary approach to image enhancement filter design: method and applications.

Image enhancement is an important issue in digital image processing. Various approaches have been developed to solve image enhancement problems, but most of them require deep expert knowledge to design appropriate image filters. To automatically design a filter, we propose a novel approach based on the genetic algorithm that optimizes a set of standard filters by determining their types and order. Moreover, the proposed method is able to manage various types of noise factors. We applied the proposed method to local and global image enhancement problems such as impulsive noise reduction, interpolation, and orientation enhancement. In terms of subjective and objective evaluations, the results show the superiority of the proposed method.

mtDNAmanager: a Web-based tool for the management and quality analysis of mitochondrial DNA control-region sequences.

BACKGROUND: For the past few years, scientific controversy has surrounded the large number of errors in forensic and literature mitochondrial DNA (mtDNA) data. However, recent research has shown that using mtDNA phylogeny and referring to known mtDNA haplotypes can be useful for checking the quality of sequence data. RESULTS: We developed a Web-based bioinformatics resource "mtDNAmanager" that offers a convenient interface supporting the management and quality analysis of mtDNA sequence data. The mtDNAmanager performs computations on mtDNA control-region sequences to estimate the most-probable mtDNA haplogroups and retrieves similar sequences from a selected database. By the phased designation of the most-probable haplogroups (both expected and estimated haplogroups), mtDNAmanager enables users to systematically detect errors whilst allowing for confirmation of the presence of clear key diagnostic mutations and accompanying mutations. The query tools of mtDNAmanager also facilitate database screening with two options of "match" and "include the queried nucleotide polymorphism". In addition, mtDNAmanager provides Web interfaces for users to manage and analyse their own data in batch mode. CONCLUSION: The mtDNAmanager will provide systematic routines for mtDNA sequence data management and analysis via easily accessible Web interfaces, and thus should be very useful for population, medical and forensic studies that employ mtDNA analysis. mtDNAmanager can be accessed at http://mtmanager.yonsei.ac.kr.

Entropy-based analysis of the non-linear relationship between gene expression profiles of amplified and non-amplified RNA.

Two critical issues in microarray-based gene expression profiling with amplified RNA are its reliability and reproducibility compared to the non-amplified RNA. In this study, the non-linear relationship between the two methods was evaluated with the entropy in addition to the linear relationship using correlation coefficients. The correlation coefficients within the amplification method and between the two methods were significantly high, 0.98 and 0.88, respectively. Comparing the entropy as increasing fold-change difference (k), the average entropy value was reduced to 0.02 in the cell line and 0.09 in the tissue samples, indicating that the number of different genes between the two methods was decreased. In addition, the threshold of k according to the percentage of p estimated from entropy values could be used to provide the cut-off line on gene selection. The quantity discordance rate of 0.3-5.47% and the common outlier proportion of 84.2-94.3% between the two methods were detected, according to the expression levels. In summary, we showed a high similarity between the two methods using non-linear as well as linear comparison. Furthermore, we proved that the entropy as the measure of non-linear relationship is useful for analyzing the similarity of replicated microarray data sets.

Evolutionary neural networks for anomaly detection based on the behavior of a program.

The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection.

A comprehensive overview of the applications of artificial life.

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming.

OBJECT: The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. MATERIALS AND METHODS: An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. RESULTS: Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. CONCLUSION: Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.

Estimating the efficiency of recognizing gender and affect from biological motion.

It is often claimed that point-light displays provide sufficient information to easily recognize properties of the actor and action being performed. We examined this claim by obtaining estimates of human efficiency in the categorization of movement. We began by recording a database of three-dimensional human arm movements from 13 males and 13 females that contained multiple repetitions of knocking, waving and lifting movements done both in an angry and a neutral style. Point-light displays of each individual for all of the six different combinations were presented to participants who were asked to judge the gender of the model in Experiment 1 and the affect in Experiment 2. To obtain estimates of efficiency, results of human performance were compared to the output of automatic pattern classifiers based on artificial neural networks designed and trained to perform the same classification task on the same movements. Efficiency was expressed as the squared ratio of human sensitivity (d') to neural network sensitivity (d'). Average results for gender recognition showed a proportion correct of 0.51 and an efficiency of 0.27%. Results for affect recognition showed a proportion correct of 0.71 and an efficiency of 32.5%. These results are discussed in the context of how different cues inform the recognition of movement style.